Change search
Refine search result
3456789 251 - 300 of 583
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 251.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On Time-Frequency Resolution of Signal Properties using Parametric Techniques1993Report (Other academic)
    Abstract [en]

    The problem to track time-varying properties of a signal is studied. The somewhat contradictory notion of “time-varying spectrum” and how to estimate the “current” spectrum in an on-line fashion is discussed. The traditional concepts and relations between time- and frequency resolution are crucial for this problem. An adaptive estimation algorithm is used to estimate the parameters of a time-varying autoregressive model of the signal. It is shown how this algorithm can be equipped with a feature such that the time-frequency resolution trade-off favors quick detection of changes at higher frequencies and has slower adaptation at lower frequencies. This should be an attractive feature and similar to, for example, what wavelet transform techniques achieve for the same problem.

  • 252.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Shaping Frequency-Dependent Time Resolution when Estimating Spectral Properties with Parametric Methods1997In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 45, no 4, p. 1025-1035Article in journal (Refereed)
    Abstract [en]

    The problem of tracking time-varying properties of a signal is studied. The somewhat contradictory notion of “time-varying spectrum” and how to estimate the “current” spectrum in an on-line fashion is discussed. The traditional concepts and relations between time and frequency resolution are crucial for this problem. We introduce two definitions for the time resolution of filters, essentially measuring the effective number of past data that are used to form the estimate. In, for example, wavelet transform techniques, frequency-dependent time resolutions are used so that fewer data are used at higher frequencies, thus enabling faster tracking of high-frequency components (at the price of worse frequency resolution). The main contribution of the paper is to show how this same feature can be introduced when estimating spectra via a time-varying, autoregressive model of the signal. This is achieved by a special choice of nominal covariance matrix for the underlying parameter changes.

  • 253.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Some Relations Between Extended and Unscented Kalman Filters2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 2, p. 545-555Article in journal (Refereed)
    Abstract [en]

    The unscented Kalman filter (UKF) has become a popular alternative to the extended Kalman filter (EKF) during the last decade. UKF propagates the so called sigma points by function evaluations using the unscented transformation (UT), and this is at first glance very different from the standard EKF algorithm which is based on a linearized model. The claimed advantages with UKF are that it propagates the first two moments of the posterior distribution and that it does not require gradients of the system model. We point out several less known links between EKF and UKF in terms of two conceptually different implementations of the Kalman filter: the standard one based on the discrete Riccati equation, and one based on a formula on conditional expectations that does not involve an explicit Riccati equation. First, it is shown that the sigma point function evaluations can be used in the classical EKF rather than an explicitly linearized model. Second, a less cited version of the EKF based on a second-order Taylor expansion is shown to be quite closely related to UKF. The different algorithms and results are illustrated with examples inspired by core observation models in target tracking and sensor network applications.

  • 254.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Lindgren, David
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Mathai, George
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Habberstad, Hans
    Swedish Defence Research Agency (FOI).
    Direction of Arrival Estimation in Sensor Arrays Using Local Series Expansion of the Received Signal2015In: 18th International Conference of Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2015Conference paper (Refereed)
    Abstract [en]

    A local series expansion of a received signal is pro-posed for computing direction of arrival (DOA) in sensor arrays. The advantages compared to classical DOA estimation methods include general sensor configurations, ultra-slow sampling, smalldimension of the arrays, and that it applies for both narrowbandand wideband signals without prior knowledge of the signals. This makes the method well suited for DOA estimation in sensor networks where size and energy consumption have to be small. We generalize the common far-field assumption of the target toalso include the near-field, which enables target tracking usinga network of sensor arrays in one framework.

  • 255.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hjalmarsson, Håkan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nineteen ML Estimators for Model Structure Selection1993In: Proceedings of the 12th IFAC World Congress, 1993, p. 119-124Conference paper (Refereed)
  • 256.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hjalmarsson, Håkan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nineteen ML Estimators for Model Structure Selection1993Report (Other academic)
  • 257.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hjalmarsson, Håkan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Seventeen ML Estimators for Model Structure Selection1992Report (Other academic)
  • 258.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hjalmarsson, Håkan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Twenty-One ML Estimators for Model Selection1993Report (Other academic)
    Abstract [en]

    Classical approaches to determine a suitable model structure from observed input-output data are based on hypothesis tests and information-based criteria. Recently, the model structure has been considered as a stochastic variable, and standard estimation techniques have been proposed. The resulting estimators are closely related to the aforementioned methods. However, it turns out that there are a number of prior choices in the problem formulation, which are crucial for the estimators' behavior. The contribution of this paper is to clarify the role of the prior choices, to examine a number of possibilities and to show which estimators are consistent. This is done in a linear regression framework. For autoregressive models, we also investigate a novel prior assumption on stability, and give the estimator for the model order and the parameters themselves.

  • 259.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hjalmarsson, Håkan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Twenty-One ML Estimators for Model Selection1995In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 31, no 10, p. 1377-1392Article in journal (Refereed)
    Abstract [en]

    Classical approaches to determine a suitable model structure from observed input-output data are based on hypothesis tests and information-based criteria. Recently, the model structure has been considered as a stochastic variable, and standard estimation techniques have been proposed. The resulting estimators are closely related to the aforementioned methods. However, it turns out that there are a number of prior choices in the problem formulation, which are crucial for the estimators' behavior. The contribution of this paper is to clarify the role of the prior choices, to examine a number of possibilities and to show which estimators are consistent. This is done in a linear regression framework. For autoregressive models, we also investigate a novel prior assumption on stability, and give the estimator for the model order and the parameters themselves.

  • 260.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hriljac, Paul
    Embry-Riddle Aeronautical University, USA.
    Particle Filters for Prediction of Chaos2003In: Proceedings of the 13th IFAC Symposium on System Identification, 2003Conference paper (Refereed)
    Abstract [en]

    The use of particle filters for the prediction of time series arising from chaotic dynamical systems is explored. The specific dynamical systems considered are variations of the logistical map with an unknown parameter. This parameter is in the chaotic regime for these dynamical systems. The systems considered have both observation and process noise. The prediction algorithms studied are variations of particle filters which include a roughening technique. Cramer-Rao bounds for the prediction algorithm are developed and compared with Monte-Carlo simulations. 

  • 261.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf J.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Best Choice of Coordinate System for Tracking Coordinated Turns1995Report (Other academic)
    Abstract [en]

    A standard approach to tracking is to use the extended Kalman filter (EKF) applied to a nonlinear state-space model. We compare two conceivable choices of state variables for modeling civil aircrafts. One where Cartesian velocities are used and one where absolute velocity and heading angle are used. In both choices, Cartesian coordinates are used for position and angular velocity for turning. It is shown that the latter state vector always performs better. This is proven by considering the linearization error made in the extended Kalman filter applied either to a time-continuous model or a discretized model. The result is supported by a Monte Carlo simulation study.

  • 262.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Karlsson, Rickard
    Nira Dynamics AB, Linköping, Sweden.
    Generating Dithering Noise for Maximum Likelihood Estimation from Quantized Data2013In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 49, no 2, p. 554-560Article in journal (Refereed)
    Abstract [en]

    The Quantization Theorem I (QT I) implies that the likelihood function can be reconstructed from quantized sensor observations, given that appropriate dithering noise is added before quantization. We present constructive algorithms to generate such dithering noise. The application to maximum likelihood estimation (mle) is studied in particular. In short, dithering has the same role for amplitude quantization as an anti-alias filter has for sampling, in that it enables perfect reconstruction of the dithered but unquantized signal’s likelihood function. Without dithering, the likelihood function suffers from a kind of aliasing expressed as a counterpart to Poisson’s summation formula which makes the exact mle intractable to compute. With dithering, it is demonstrated that standard mle algorithms can be re-used on a smoothed likelihood function of the original signal, and statistically efficiency is obtained. The implication of dithering to the Cramér–Rao Lower Bound (CRLB) is studied, and illustrative examples are provided.

  • 263.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Karlsson, Rickard
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Range Estimation using Angle-Only Target Tracking with Particle Filters2001In: Proceedings of the Third Conference on Computer Science and Systems Engineering, 2001, p. 55-62Conference paper (Other academic)
    Abstract [en]

    We consider the recursive state estimation of a maneuverable aircraft using an airborne passive IR-sensor. The main issue addressed in the paper is the range- and velocity estimation using angle-only measurements. In contrast to standard target tracking literature we do not rely on linearized motion models and measurement relations, or on any Gaussian assumptions. Instead, we apply optimal recursive Bayesian filters directly to the nonlinear target model. We present novel sequential simulation based algorithms developed explicitly for the angle-only target tracking problem. These Monte Carlo filters approximate optimal inference by simulating a large number of tracks, or particles. In a simulation study our particle filter approach is compared to a range parameterized extended Kalman filter (RPEKF). Tracking is performed in both Cartesian and modified spherical coordinates (MSC).

  • 264.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Karlsson, Rickard
    NIRA Dynamics AB, Sweden.
    Statistical Results for System Identification Based on Quantized Observations2009In: Proceedings of the 15th IFAC Symposium on System Identification, 2009Conference paper (Refereed)
    Abstract [en]

    System identification based on quantized observations requires either approximations of the quantization noise, leading to suboptimal algorithms, or dedicated algorithms tailored to the quantization noise properties. This contribution studies fundamental issues in estimation that relate directly to the core methods in system identification. As a first contribution, results from statistical quantization theory are surveyed and applied to both moment calculations (mean, variance etc) and the likelihood function of the measured signal. In particular, the role of adding dithering noise at the sensor is studied. The overall message is that tailored dithering noise can considerably simplify the derivation of optimal estimators. The price for this is a decreased signal to noise ratio, and a second contribution is a detailed study of these effects in terms of the Cramér–Rao lower bound. The common additive uniform noise approximation of quantization is discussed, compared, and interpreted in light of the suggested approaches.

  • 265.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Karlsson, Rickard
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Statistical Results for System Identification based on Quantized Observations2009In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 45, no 12, p. 2794-2801Article in journal (Refereed)
    Abstract [en]

    System identification based on quantized observations requires either approximations of the quantization noise, leading to suboptimal algorithms, or dedicated algorithms tailored to the quantization noise properties. This contribution studies fundamental issues in estimation that relate directly to the core methods in system identification. As a first contribution, results from statistical quantization theory are surveyed and applied to both moment calculations (mean, variance etc) and the likelihood function of the measured signal. In particular, the role of adding dithering noise at the sensor is studied. The overall message is that tailored dithering noise can considerably simplify the derivation of optimal estimators. The price for this is a decreased signal to noise ratio, and a second contribution is a detailed study of these effects in terms of the Cramer-Rao lower bound. The common additive uniform noise approximation of quantization is discussed, compared, and interpreted in light of the suggested approaches.

  • 266.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On the Estimation of Tire-Road Friction1992Report (Other academic)
  • 267.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Bergman, Niclas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Point-Mass Filter and Cramer-Rao Bound for Terrain-Aided Navigation1997In: Proceedings of the 36th IEEE Conference on Decision and Control, 1997, p. 565-570 vol.1Conference paper (Refereed)
    Abstract [en]

    The nonlinear estimation problem in navigation using terrain height variations is studied. The optimal Bayesian solution to the problem is derived. The implementation is grid based, calculating the probability of a set of points on an adaptively dense mesh. The Cramer-Rao bound is derived. Monte Carlo simulations over a commercial map shows that the algorithm, after convergence, reaches the Cramer-Rao lower bound.

  • 268.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Millnert, Mille
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Signal processing2010 (ed. 1)Book (Other academic)
    Abstract [en]

    This book provides signal processing exercises and can with advantage be used together with the text book Signal Processing by Fredrik Gustafsson, Lennart Ljung and Mille Millnert. The chapters of the books are aligned, which means that there are matching exercises to each theory chapter. The first part of the book treats classical digital signal processing based on transforms and filters, while model based digital processing is in focus in the second part. Some exercises are more theoretical and solved by hand, while others are intended for Matlab on a computer. The book material is inspired by real problems, and so are the exercises. This is emphasized by the use of data sets, both simulated and real. Most exercises have complete solutions, and a section with hints provides guidance to some exercises. Selected exercises also result in a Matlab function corresponding to specific signal processing algorithms. These functions are used to solve other exercises. Thereby, the reader gradually build up a signal processing toolbox during the studies of the material. The book homepage contains more information and links to access the matlab functions, data sets and examples used in the book. Main book Signal Processing

  • 269.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nazin, Alexander V
    Institute of Control Sciences, Russia.
    Stenman, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Asymptotic Properties of Just-in-Time Models1997In: Proceedings of the 11th IFAC Symposium on System Identification, 1997, p. 1249-1254Conference paper (Refereed)
    Abstract [en]

    The concept of Just-in-Time models has been introduced for models that are not estimated until they are really needed. The prediction is taken as a weighted average of neighboring points in the regressor space, such that an optimal bias/variance trade-off is achieved. The asymptotic properties of the method are investigated, and are compared to the corresponding properties of related statistical non-parametric kernel methods. It is shown that the rate of convergence for Just-in-Time models at least is in the same order as traditional kernel estimators, and that better rates probably can be achieved. 

  • 270.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ninness, Brett
    University of Newcastle, Australia.
    Asymptotic Power and the Gain of Under-Modelling in Change Detection1994Report (Other academic)
    Abstract [en]

    It is well-known from experience that low order models perform well in change detection problems even if the true system is quite complicated. By computing the asymptotic power function, it is here shown that common hypothesis tests proposed in literature attain their maximum power for a low order model under certain conditions on how much each parameter change.

  • 271.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ninness, Brett M
    University of Newcastle, Australia.
    Asymptotic Power and the Benefit of Under-Modeling in Change Detection1995In: Proceedings of the 3rd European Control Conference, 1995, p. 1237-1242Conference paper (Refereed)
    Abstract [en]

    It is well-known from experience that low order models perform well in change detection problems even if the true system is quite complicated. By computing the asymptotic power function, it is here shown that common hypothesis tests proposed in literature attain their maximum power for a low order model under certain conditions on how much each parameter change.

  • 272.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ninness, Brett M
    University of Newcastle, Australia.
    Asymptotic Power and the Benefit of Under-Modeling in Change Detection1995Report (Other academic)
    Abstract [en]

    It is well-known from experience that low order models perform well in change detection problems even if the true system is quite complicated. By computing the asymptotic power function, it is here shown that common hypothesis tests proposed in literature attain their maximum power for a low order model under certain conditions on how much each parameter change.

  • 273.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nordlund, Per-Johan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Recursive State Estimation of Nonlinear Systems2001In: Proceedings of the Third Conference on Computer Science and Systems Engineering, 2001, p. 175-181Conference paper (Refereed)
    Abstract [en]

    Particle filters (sequential Monte Carlo methods) handle recursive state estimation of arbitrary systems. However, a direct application shows that often a vast number of particle is needed for the filter to work well. This paper developes a combined particle and Kalman filter for recursive state estimation of nonlinear systems, where the state vector can be partitioned into one linear and Gaussian part and one nonlinear and/or non-Gaussian. The linear and Gaussian part is estimated using the Kalman filter and there maining part is estimated using the particle filter. Based on Bayes rule the solutions from the two filters are blended together. This two-filter approach is compared to a standard particle filter by applying both filters to a simple navigation problem. The result shows that using the combined filter the number of particles needed to achieve the same performance as the particle filter is much less.

  • 274.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nordlund, Per-Johan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Sequential Monte Carlo Filtering Techniques Applied to Integrated Navigation Systems2001In: Proceedings of the 2001 American Control Conference, 2001, p. 4375-4380 vol.6Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of integrated aircraft navigation, more specifically how to integrate inertial navigation with terrain aided positioning. This is a highly nonlinear and non-Gaussian recursive state estimation problem which requires state of the art methods. We propose an algorithm based on the particle filter with particular attention to the complexity of the problem. The proposed algorithm takes advantage of linear and Gaussian structure within the system and solves these parts using the Kalman filter. The remaining parts suffering from severe nonlinear and/or non-Gaussian structure are solved using the particle filter. The proposed filter is applied to a simplified integrated navigation system. The result shows that very good performance is achieved for a tractable computational load.

  • 275.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas B.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Skoglar, Per
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Karlsson, G Rickard
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Navigation and Tracking of Road-Bound Vehicles2012In: Handbook of Intelligent Vehicles / [ed] Eskandarian, Azim, London: Springer, 2012, p. 397-434Chapter in book (Refereed)
    Abstract [en]

    The Handbook of Intelligent Vehicles provides a complete coverage of the fundamentals, new technologies, and sub-areas essential to the development of intelligent vehicles; it also includes advances made to date, challenges, and future trends. Significant strides in the field have been made to date; however, so far there has been no single book or volume which captures these advances in a comprehensive format, addressing all essential components and subspecialties of intelligent vehicles, as this book does. Since the intended users are engineering practitioners, as well as researchers and graduate students, the book chapters do not only cover fundamentals, methods, and algorithms but also include how software/hardware are implemented, and demonstrate the advances along with their present challenges. Research at both component and systems levels are required to advance the functionality of intelligent vehicles. This volume covers both of these aspects in addition to the fundamentals listed above. 

  • 276.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Palmqvist, Jan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Change Detection Design for Low False Alarm Rates1997In: Proceedings of the SAFEPROCESS'97 Conference on Fault Detection, Supervision and Safety for Technical Processes, 1997Conference paper (Refereed)
    Abstract [en]

    This contribution addresses the problem of automated tuning of change detectors with given false alarm rate. By estimating a parametric distribution to the test statistics computed from real or simulated data, the threshold of the test can be computed directly. The advantage is that we can predict the threshold although there are no or very few false alarms in the used data. Using real data, the method is robust to assumed noise distributions and modeling errors. We illustrate the method on the CUSUM and GLR tests applied to friction estimation in cars and an airborne navigation system, respectively.

  • 277.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Persson, Niclas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Event Based Sampling with Application to Vibration Analysis in Pneumatic Tires2001In: Proceedings of the 2001 IEEE International Conference on Acoustics, Speech and Signal Processing, 2001, p. 3885-3888 vol.6Conference paper (Refereed)
    Abstract [en]

    Event based sampling occurs when the time instants are measured everytime the amplitude passes certain pre-defined levels. This is in contrast with classical signal processing where the amplitude is measured at regular time intervals. The signal processing problem is to separate the signal component from noise in both amplitude and time domains. Event based sampling occurs in a variety of applications. The purpose here is to explain the new types of signal processing problems that occur, and identify the need for processing in both the time and event domains. We focus on rotating axles, where amplitude disturbances are caused by vibrations and time disturbances from measurement equipment. As one application, we examine tire pressure monitoring in cars where suppression of time disturbance is of utmost importance.

  • 278.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Persson, Niclas
    NIRA Dynamics AB, Sweden.
    Ahlqvist, Stefan
    NIRA Dynamics AB, Sweden.
    Forssell, Urban
    NIRA Dynamics AB, Sweden.
    Sensor Fusion for Accurate Computation of Yaw Rate and Absolute Velocity2001In: Proceedings of the SAE 2001 World Congress, 2001Conference paper (Refereed)
    Abstract [en]

    In the presented sensor fusion approach, centralized filtering of related sensor signals is used to improve and correct low price sensor measurements. From this, we compute high-quality state information as drift-free yaw rate and exact velocity (accounting for unknown tire radius and slipping wheels on 4WD vehicles). The basic tool here is a Kalman filter supported by change detection for sensor diagnosis. Results and experience of real-time implementations are presented.

  • 279.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Persson, Niclas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Drevö, Markus
    Nira Dynamics AB, Sweden.
    Indirect Tire Pressure Monitoring using Sensor Fusion2002In: Proceedings of the SAE 2002 World Congress, 2002Conference paper (Refereed)
    Abstract [en]

    Vehicle handling depends critically on the tire-road contact patch. When the tire inflation pressure changes the contact patch is no longer optimal and the handling properties deteriorate. Furthermore fuel consumption increases and the lifetime of the tires decreases. Therefore it is very important that the tires are correctly inflated.

    We here focus on an indirect tire pressure monitoring system, where no pressure sensors are needed. The system is based on vibration and wheel radius analysis. These two approaches are combined for optimal performance concerning sensitivity to detect pressure losses and robustness to different driving conditions. When these two approaches are combined, it is possible to detect pressure losses larger than 15% in one, two, three, or four (diffusion) tires within 1 minute. It is also possible to detect which of the tires that are underinflated.

  • 280.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Persson, Niclas
    NIRA Dynamics AB, Sweden.
    Drevö, Markus
    NIRA Dynamics AB, Sweden.
    Forssell, Urban
    NIRA Dynamics AB, Sweden.
    Löfgren, Mats
    NIRA Dynamics AB, Sweden.
    Quicklund, Henrik
    NIRA Dynamics AB, Sweden.
    Virtual Sensors of Tire Pressure and Road Friction2001In: Proceedings of the SAE 2001 World Congress, 2001Conference paper (Refereed)
    Abstract [en]

    The idea of a virtual sensor is to extract information of parameters that cannot be measured directly, or at least would require very costly sensors, by only using available information. Virtual sensors are described for the friction between road and tire, the tire inflation pressure and wheel imbalance. There are certain interconnections between these virtual sensors so they are preferably implemented in one unit. Results from a real-time implementation, using mainly sensor information from the CAN bus, are given.

  • 281.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Persson, Niclas
    NIRA Dynamics AB, Sweden.
    Drevö, Markus
    NIRA Dynamics AB, Sweden.
    Forssell, Urban
    NIRA Dynamics AB, Sweden.
    Quicklund, Henrik
    NIRA Dynamics AB, Sweden.
    Löfgren, Mats
    NIRA Dynamics AB, Sweden.
    Virtual Sensors of Tire Pressure and Road Friction2001Report (Other academic)
    Abstract [en]

    The idea of a virtual sensor is to extract information of parameters that cannot be measured directly, or at least would require very costly sensors, by only using available information. Virtual sensors are described for the friction between road and tire, the tire inflation pressure and wheel imbalance. There are certain interconnections between these virtual sensors so they are preferably implemented in one unit. Results from a real-time implementation, using mainly sensor information from the CAN bus, are given.

  • 282.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Persson, Niclas
    NIRA Dynamics AB, Sweden.
    Forssell, Urban
    NIRA Dynamics AB, Sweden.
    Ahlqvist, Stefan
    NIRA Dynamics AB, Sweden.
    Sensor Fusion for Accurate Computation of Yaw Rate and Absolute Velocity2001Report (Other academic)
    Abstract [en]

    In the presented sensor fusion approach, centralized filtering of related sensor signals is used to improve and correct low price sensor measurements. From this, we compute high-quality state information as drift-free yaw rate and exact velocity (accounting for unknown tire radius and slipping wheels on 4WD vehicles). The basic tool here is a Kalman filter supported by change detection for sensor diagnosis. Results and experience of real-time implementations are presented.

  • 283.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Pintelon, Rik
    Vrije Universiteit Brussel, Belgium.
    Rolain, Yves
    Vrije Universiteit Brussel, Belgium.
    Schoukens, Johan
    Vrije Universiteit Brussel, Belgium.
    Fast Calculations of Linear and Nonlinear Least-Squares Estimates for System Identification1998In: Proceedings of the 37th IEEE Conference on Decision and Control, 1998, p. 3408-3410 vol.3Conference paper (Refereed)
    Abstract [en]

    In this paper an FFT based method is presented to speed-up the calculations of least-squares estimates of d unknown parameters from O(Nd2) to O(Nlog2N) (with N the number of data points) resulting in a significant reduction of the required computation time. The method can be applied to models/methods that are linear or nonlinear-in-the-parameters. Also the memory requirements are significantly reduced, without needing dedicated memory management techniques. 

  • 284.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Saha, Saikat
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Particle Filtering with Dependent Noise2010In: Proceedings of the 13th Conference on Information Fusion, 2010, , p. 4Conference paper (Refereed)
    Abstract [en]

    The theory and applications of the particle filter (PF) have developed tremendously during the past two decades. However, there appear to be no version of the PF readily applicable to the case of dependent process and measurement noise. This is in contrast to the Kalman filter, where the case of correlated noise is a standard modification. Further, the fact that sampling continuous time models give dependent noise processes is an often neglected fact in literature. We derive the optimal proposal distribution in the PF for general and Gaussian noise processes, respectively. The main result is a modified prediction step. It is demonstrated that the original Bootstrap particle filter gets a particular simple and explicit form for dependent Gaussian noise. Finally, the practical importance of dependent noise is motivated in terms of sampling of continuous time models.

  • 285.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Saha, Saikat
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Non-Linear Filtering based on Observations from Gaussian Processes2011In: Proceedings of the 2011 IEEE Aerospace Conference, 2011, , p. 6Conference paper (Refereed)
    Abstract [en]

    We consider a class of non-linear filtering problems, where the observation model is given by a Gaussian process rather than the common non-linear function of the state and measurement noise. The new observation model can be considered as a generalization of the standard one with correlated measurement noise in both time and space. We propose a particle filter based approach with a measurement update step that requires a memory of past observations which can be truncated using a moving window to obtain a finite-dimensional filter with arbitrarily good accuracy. The validity of the conceptual solution is proved via simulations on a one dimensional tracking problem and implementation issues are discussed.

  • 286.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Saha, Saikat
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    The Benefits of Down-Sampling in the Particle Filter2011In: Proceedings of the 14th International Conference on Information Fusion, 2011, , p. 6Conference paper (Refereed)
    Abstract [en]

    The choice of proposal distribution in the particle filter is one of the most important design choices, and also one of the trickiest one to implement. There are basically three main options: the prior, the likelihood and the optimal proposal that combines the prior and the likelihood. The optimal proposal however, can not be obtained in most cases. The prior proposal is although easy to implement, it does not incorporate the information available otherwise from the recent observation. The prior may thus work fine for low signal to noise ratio (SNR), where the recent observation does not carry much information. However, defining the critical value of the SNR is not that obvious. On the other hand, the likelihood as a proposal always includes the information from the recent observation, but it requires that the measurement dimension is at least equal to the state dimension. We here formalize the problem, and point out an approach based on down-sampling the model. One main advantage of down-sampling is that it can decrease the problem of particle degeneracy.

  • 287.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schoukens, Johan
    Vrije Universiteit Brussel, Belgium.
    Utilizing Periodic Excitation in Prediction Error Based System Identification1998In: Proceedings of the 37th IEEE Conference on Decision and Control, 1998, p. 3926-3931 vol.4Conference paper (Refereed)
    Abstract [en]

    The standard prediction error method (PEM) in system identification for estimating output error models is studied. The PEM has recently been proposed to be formulated in the frequency domain, and in this context it has been pointed out that a periodic excitation signal give many advantages. The most immediate is data reduction when using data averaged over the periods, We will here project the main results onto the time domain, and show how to utilize a nonparametric noise model as a pre-filter to increase accuracy and numerical convergence speed in output error modeling. A possible drawback with using only the averaged data is decreased estimation accuracy when the system and noise model have common parameters. A new result is presented that shows how the nonparametric noise model can be used to recover the original accuracy for ARX models.

  • 288.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hol, Jeroen
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Sensor Fusion for Augmented Reality2009Report (Other academic)
    Abstract [en]

    The problem of estimating the position and orientation (pose) of a camera is approached by fusing measurements from inertial sensors (accelerometers and rate gyroscopes) and a camera. The sensor fusion approach described in this contribution is based on nonlinear filtering using the measurements from these complementary sensors. This way, accurate and robust pose estimates are available for the primary purpose of augmented reality applications, but with the secondary effect of reducing computation time and improving the performance in vision processing. A real-time implementation of a nonlinear filter is described, using a dynamic model for the 22 states, where 100 Hz inertial measurements and 12.5 Hz vision measurements are processed. An example where an industrial robot is used to move the sensor unit, possessing almost perfect precision and repeatability, is presented. The results show that position and orientation accuracy is sufficient for a number of augmented reality applications.

  • 289.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hol, Jeroen
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Sensor Fusion for Augmented Reality2008In: Proceedings of the 17th IFAC World Congress, 2008, p. 14100-14100Conference paper (Refereed)
    Abstract [en]

    The problem of estimating the position and orientation (pose) of a camera is approached by fusing measurements from inertial sensors (accelerometers and rate gyroscopes) and a camera. The sensor fusion approach described in this contribution is based on nonlinear filtering using the measurements from these complementary sensors. This way, accurate and robust pose estimates are available for the primary purpose of augmented reality applications, but with the secondary effect of reducing computation time and improving the performance in vision processing. A real-time implementation of a nonlinear filter is described, using a dynamic model for the 22 states, where 100 Hz inertial measurements and 12.5 Hz vision measurements are processed. An example where an industrial robot is used to move the sensor unit, possessing almost perfect precision and repeatability, is presented. The results show that position and orientation accuracy is sufficient for a number of augmented reality applications.

  • 290.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Karlsson, Rickard
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nordlund, Per-Johan
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
    State-of-the-Art for the Marginalized Particle Filter2006In: Proceedings of the 2006 IEEE Nonlinear Statistical Signal Processing Workshop, 2006, p. 172-174Conference paper (Refereed)
    Abstract [en]

    The marginalized particle filter is a powerful combination of the particle filter and the Kalman filter, which can be used when the underlying model contains a linear substructure subject to Gaussian noise. This paper surveys state of the art for theory and practice.

  • 291.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Strömberg, Jan-Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Estimating Model Trees for Describing Piecewise Linear Systems1989Report (Other academic)
  • 292.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wahlberg, Bo
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Blind Equalization by Direct Examination of the Input Sequences1992In: Proceedings of the 1992 IEEE International Conference on Acoustics, Speech and Signal Processing, 1992, p. 701-704 vol.4Conference paper (Refereed)
    Abstract [en]

    The authors' approach to blind equalization examines the possible input sequences directly by using a bank of filters and, in contrast to common approaches, does not try to find an approximative inverse of the channel dynamics. The identifiability question of a noise-free finite impulse response (FIR) model is investigated. A sufficient condition for the input sequence (persistently exciting of a certain order) is given which guarantees that both the channel model and the input sequence can be determined exactly in finite time. A recursive algorithm is given for a time-varying infinite impulse response (IIR) channel model with additive noise, which does not require a training sequence. The estimated sequence is an arbitrarily good approximation of the maximum a posteriori estimate. The proposed method is evaluated on a Rayleigh fading communication channel. It shows fast convergence properties and good tracking ability.

  • 293.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wahlberg, Bo
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Blind Equalization by Direct Examination of the Input Sequences1991Report (Other academic)
    Abstract [en]

    The authors' approach to blind equalization examines the possible input sequences directly by using a bank of filters and, in contrast to common approaches, does not try to find an approximative inverse of the channel dynamics. The identifiability question of a noise-free finite impulse response (FIR) model is investigated. A sufficient condition for the input sequence (persistently exciting of a certain order) is given which guarantees that both the channel model and the input sequence can be determined exactly in finite time. A recursive algorithm is given for a time-varying infinite impulse response (IIR) channel model with additive noise, which does not require a training sequence. The estimated sequence is an arbitrarily good approximation of the maximum a posteriori estimate. The proposed method is evaluated on a Rayleigh fading communication channel. It shows fast convergence properties and good tracking ability.

  • 294.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wahlberg, Bo
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Blind Equalization by Direct Examination of the Input Sequences1995Report (Other academic)
    Abstract [en]

    This paper presents a novel approach to blind equalization (deconvolution), which is based on direct examination of possible input sequences. In contrast to many other approaches, it does not rely on a model of the approximative inverse of the channel dynamics. To start with, the blind equalization identifiability problem for a noise-free finite impulse response channel model is investigated. A necessary condition for the input, which is algorithm independent, for blind deconvolution is derived. This condition is expressed in an information measure of the input sequence. A sufficient condition for identifiability is also inferred, which imposes a constraint on the true channel dynamics. The analysis motivates a recursive algorithm where all permissible input sequences are examined. The exact solution is guaranteed to be found as soon as it is possible. An upper bound on the computational complexity of the algorithm is given. This algorithm is then generalized to cope with time-varying infinite impulse response channel models with additive noise. The estimated sequence is an arbitrary good approximation of the maximum a posteriori estimate. The proposed method is evaluated on a Rayleigh fading communication channel. The simulation results indicate fast convergence properties and good tracking abilities.

  • 295.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wahlberg, Bo
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Blind Equalization by Direct Examination of the Input Sequences1991Report (Other academic)
    Abstract [en]

    The authors' approach to blind equalization examines the possible input sequences directly by using a bank of filters and, in contrast to common approaches, does not try to find an approximative inverse of the channel dynamics. The identifiability question of a noise-free finite impulse response (FIR) model is investigated. A sufficient condition for the input sequence (persistently exciting of a certain order) is given which guarantees that both the channel model and the input sequence can be determined exactly in finite time. A recursive algorithm is given for a time-varying infinite impulse response (IIR) channel model with additive noise, which does not require a training sequence. The estimated sequence is an arbitrarily good approximation of the maximum a posteriori estimate. The proposed method is evaluated on a Rayleigh fading communication channel. It shows fast convergence properties and good tracking ability.

  • 296.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wahlberg, Bo
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Blind Equalization by Direct Examination of the Input Sequences1995In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 43, no 7, p. 2213-2222Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel approach to blind equalization (deconvolution), which is based on direct examination of possible input sequences. In contrast to many other approaches, it does not rely on a model of the approximative inverse of the channel dynamics. To start with, the blind equalization identifiability problem for a noise-free finite impulse response channel model is investigated. A necessary condition for the input, which is algorithm independent, for blind deconvolution is derived. This condition is expressed in an information measure of the input sequence. A sufficient condition for identifiability is also inferred, which imposes a constraint on the true channel dynamics. The analysis motivates a recursive algorithm where all permissible input sequences are examined. The exact solution is guaranteed to be found as soon as it is possible. An upper bound on the computational complexity of the algorithm is given. This algorithm is then generalized to cope with time-varying infinite impulse response channel models with additive noise. The estimated sequence is an arbitrary good approximation of the maximum a posteriori estimate. The proposed method is evaluated on a Rayleigh fading communication channel. The simulation results indicate fast convergence properties and good tracking abilities.

  • 297.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wahlberg, Bo
    Royal Institute of Technology, Sweden.
    Identifiability in Blind Equalization1993In: Proceedings of the 12th IFAC World Congress, 1993Conference paper (Refereed)
  • 298.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wahlberg, Bo
    Royal Institute of Technology, Sweden.
    Identifiability in Blind Equalization1992Report (Other academic)
  • 299.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wahlberg, Bo
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On Simultaneous System and Input Sequence Estimation1992In: Proceedings of the 4th IFAC Symposium on Adaptive Systems in Control and Signal Processing, 1992, p. 93-98Conference paper (Refereed)
    Abstract [en]

    Equalization is concerned with estimation of the input sequence of a linear system given noisy measurements of the output signal. In case the system description is unknown we have the problem of blind equalization. A scheme for blind equalization which is based on the assumption that the input signal belongs to a finite alphabet is proposed. A finite impulse response model can be directly estimated by the least-squares method if the input sequence is known. Since we know that the number of possible input sequences is limited, we can associate one system estimate to each possible input sequence. This allows us to determine the a posteriori probability of an input sequence given output observations. The maximum a posteriori (MAP) input sequence estimate is then taken as the most probable input sequence. Sufficient conditions for identifiability of the input signal and the system are given. The complexity of this scheme increases exponentially with time. A recursive approximate MAP estimator of fixed complexity is obtained by, at each time update, only keeping the K most probable input sequences. This method is evaluated on a Rayleigh fading communication channel.

  • 300.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Åslund, Jan
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Frisk, Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Krysander, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Nielsen, Lars
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    On Threshold Optimization in Fault Tolerant Systems2008In: Proceedings of the 17th IFAC World Congress, 2008, p. 7883-7888Conference paper (Refereed)
    Abstract [en]

    Fault tolerant systems are considered, where a nominal system is monitored by a fault detection algorithm, and the nominal system is switched to a backup system in case of a detected fault. Conventional fault detection is in the classical setting a trade-off between detection probability and false alarm probability. For the considered fault tolerant system, a system failure occurs either when the nominal system gets a fault that is not detected, or when the fault detector signals an alarm and the backup system breaks down. This means that the trade-off for threshold setting is different and depends on the overall conditions, and the characterization and understanding of this trade-off is important. It is shown that the probability of system failure can be expressed in a general form based on the probability of false alarm and detection power, and based on this form the influence ratio is introduced. This ratio includes all information about the supervised system and the backup system that is needed for the threshold optimization problem. It is shown that the influence ratio has a geometrical interpretation as the gradient of the receiver operating characteristics (ROC) curve at the optimal point, and furthermore, it is the threshold for the optimal test quantity in important cases.

3456789 251 - 300 of 583
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf