An array processing GNSS (Global Navigation Satellite System) receiver may provide increased accuracy, reliability and integrity by forming beams towards satellites and nulls towards interference or reflective surfaces. Also, software defined receivers have proven themselves versatile and provide a convenient environment to implement novel algorithms.This paper first describes the gain/phase calibration of a seven element custom array antenna and proceeds to compare the single antenna performance to that of the performance attained by forming beams towards the satellites. IF (Intermediate Frequency) data, high rate samples representing the received signal in a narrow band around the GPS L1 frequency, from an array antenna have been recorded both in an environment with open sky conditions and also in more challenging areas (central Boulder, Colorado). Simultaneously, data from a high quality GPS based INS was recorded in order to obtain accurate estimates of position/ orientation. Calibration of the system (including antennas and front-ends) was performed using data from the benign environment, and based on this information, deterministic beams were formed towards the satellites using data from the semi-urban dataset. The single antenna accuracy was then compared to the position obtained by processing after forming beams.
Software defined receivers (SDR) are an increasingly important tool within the GNSS research community as the high level of flexibility offer a significant advantage over traditional hardware implementations. Over the last decade, software receivers have been used to investigate techniques as diverse as bi-static radar (additional correlators), multipath mitigation techniques, GPS/INS integration and array processing.Mentioned above are only a few examples of features that could be required of an SDR, other include support for new signals (Galileo, GPS L5), multiple data file formats, high sensitivity and support for very long data sets. The large number of available features should ideally be coupled with program simplicity (such that other people can understand the program) and efficiency. This paper discusses these issues and proposes several solutions such asgeneralized data buffers (that is trivial to extend for new data formats) and a unified tracking structure (regardless of signal modulation). Examples are given using a Matlab implementation based on the Borre/Akos book Ä Software-Defined GPS and Galileo Receiver", however with significant modifications. Where critical, Java is used to increase performance while maintaining cross platform compatibility. Near real-time operation is available under optimal circumstances and the receiver currently supports GPS C/A- and GPS P-code signals.
This article proposes a Bayesian procedure to calculate posterior probabilities of active effects for unreplicated two-level factorials. The results from a literature survey are used to specify individual prior probabilities for the activity of effects and the posterior probabilities are then calculated in a three-step procedure where the principles of effects sparsity, hierarchy, and heredity are successively considered. We illustrate our approach by reanalyzing experiments found in the literature.
In this paper we present an ultrasonic pulseecho technique for estimating the methane (CH4) content in binary mixtures of CH4 and carbon dioxide (CO2). The method is based on parametric estimation of phase velocity and frequency dependent attenuation in combination with Partial Least-Squares Regression (PLSR). The technique is verified using experiments on mixtures with a volume fraction of CO2 in the range of 0 % -10 %. The experiments show that the CH4 content can be accurately estimated with high repeatability.
Bluetooth-equipped wireless sensor nodes can be quickly integrated in small home networks. These networks can be utilized e.g. for surveillance, home monitoring and automation. Accurate time is an important factor for time-stamping of sensor data, encryption/authentication and it can also to used to implement time synchronous schemes for low power radio communication. We argue that IP-based time synchronization, such as various flavors of the NTP protocol, can be used with Bluetooth networks. This in combination with an activation schedule allows an efficient trade-off between energy consumption and communication delay, and provides easy integration with available infrastructure. The proposed approach in this paper is well suited for smaller wireless home networks, typically singlehop networks with access points that are always available. Our approach is verified by experiments performed on a COTS-based platform using Bluetooth.
In this work, we present interesting case studies that lead to new and deeper results on fast convergence of reduced-rank conjugate gradient (RRCG) Wiener filters (WF), for applications in communications and sensor array signal processing. We discover that for signal modes with a specially structured Gram matrix, which induces L groups of distinct eigenvalues in the data covariance matrix, a fast and predictable convergence, in at most L steps, can be achieved when the RRCG WF is used to detect, and/or to focus on, the desired signal mode. For such applications, given knowledge of the repeated eigenstructure of the Gram matrix of signal modes or of the measurement covariance matrix, a RRCG Wiener filter, of at most rank L, delivers the same performance as the full-rank Wiener filter. Typically L is much less than the rank of the Gram matrix.
Using the notion of expanding subspace and the framework of reduced-rank signal processing, we present our latest discovery on applying the vector and matrix conjugate gradient (CG) methods to design reduced-rank linear MMSE multiuser detectors (MUD) for code division multiple access (CDMA) systems. We show that for a synchronous CDMA system with K users, each using a distinct length N spreading code, the vector CG method converges to the full-rank linear MUD in at most K steps (K≤N).The matrix CG method converges to the full-rank linear MUD in one step. Furthermore, when the Gold codes are used as spreading codes in combination with a groupwise power control scheme, early convergence in the vector CG Wiener filter can be reached in just L steps (L≪K≤N), typically L=2∼4 independent of the user number K and the spreading length N.
This technical note introduces the closed form maximum likelihood estimator for estimating the coefficients of the non-parametric frequency response function from system identification experiments. It is assumed that the experiments consist of repeated pulse excitations and that both the excitation and system response are measured which leads to an error-in-variables setting. Monte Carlo simulations indicate that the estimator achieves efficiency at low signal-to-noise ratios with only few measurements. Comparison with the least-squares estimator shows that better, unbiased results are obtained.
This paper addresses a novel method for vehicle tracking using an extended Kalman filter and measurements of road surface vibrations from a single accelerometer. First, a measurement model for vibrations caused by vehicular road traffic is developed. Then the identifiability of the involved parameters is analyzed. Finally, the measurement model is combined with a constant speed motion model and the Kalman filter is derived. Simulation and measurement results indicate that the approach is feasible and show where further development is needed.
This article discusses a novel approach to vehicle property sensing based on traffic-induced road surface vibrations and investigates the feasibility of this approach. Road surface vibrations from real-life experiments are acquired using three-axis accelerometers and the data are analysed. Based on the assessment of the data, a first coarse scheme for axle detection of passing vehicles is developed. The scheme is then evaluated using measurement data from a highway with moderate traffic intensity but diverse traffic. It is found that the proposed approach is feasible and the estimation scheme yields promising results. Furthermore, delimitations, encountered problems and identified research challenges are discussed and future research directions are given.
This paper presents first results for vehicle detection and vehicle property estimation based on the assessment of traffic induced vibrations in the road surface. A surface mounted 3D accelerometer device is used to register the vibrations in the surface. Acquired data from experiments on roads are used to design methods that are able to detect vehicle passages, estimate the number of axles of a vehicle and also deduce the wheel-base for passenger cars. Evaluation of the methods indicate that the accelerometer based approach is feasible and should be further developed in order to deduce vehicle properties like vehicle speed and distance to sensing device from one device. Moreover, results for the vehicle detection on real-life traffic data from the E4 in northern Sweden are summarized.
This paper presents modeling of wave propagation in pavements from a system identification point of view. First, a model based on the physical structure is derived. Second, experiment design and evaluation are discussed and maximum-likelihood estimators for estimating the model parameters are introduced, assuming an error-in-variables setting. Finally, the proposed methods are applied to measurement data from two experiments under varying environmental conditions. It is found that the proposed methods can be used to estimate the dispersion curves of the considered waveguide and the results can be used for further analysis
In this paper, modeling of the pavement as a wave propagation medium and estimation of the corresponding model parameters is approached from a system identification perspective. A model based on the physical background is proposed and the corresponding parameters are then estimated from measurement data. In order to achieve the latter, two estimators are proposed, their performance evaluated, and then applied to the measurement data. It is found that the proposed methods are applicable and the results show that different eigenmodes of the structure are excited.
This paper presents simulations and methods developed to investigate the feasibility of using a Fractional-Sample-Delay (FSD) system in the planned EISCAT_3D incoherent scatter radar. Key requirements include a frequency-independent beam direction over a 30 MHz band centered around 220 MHz, with correct reconstruction of pulse lengths down to 200 ns. The clock jitter from sample to sample must be extremely low for the integer sample delays. The FSD must also be able to delay the 30 MHz wide signal band by 1/1024th of a sample without introducing phase shifts, and it must operate entirely in baseband. An extensive simulation system based on mathematical models has been developed, with inclusion of performance-degrading aspects such as noise, timing error, and bandwidth. Finite Impulse Response (FIR) filters in the baseband of a band-pass-sampled signal have been used to apply true time delay beamforming. It has been confirmed that such use is both possible and well behaved. The target beam-pointing accuracy of 0.06° is achievable using optimized FIR filters with lengths of 36 taps and an 18 bit coefficient resolution. Even though the minimum fractional delay step necessary for beamforming is ∼13.1 ps, the maximum sampling timing error allowed in the array is found to be σ ≤ 120 ps if the errors are close to statistically independent.
Inventory management differs from other manufacturing inventory managements, mainly due to its specialists in function with maintenance. So far, enormous attention has been paid by standing on spares’ manufacturingfactories, sales companies, end users’ purchasing departments, or maintenance engineers, separately. However, not only “bullwhip effect” in forecasting spares demands, but also deteriorated relationships among the spares supply chains have shown that, spares optimization strategies with isolated consideration couldonly bring short-term or partial improvements. In this paper, the spares demand system with consideration of integration management is promoted, the new Solid-Net relationships among four main components are elaborated. Then, the root causes of ineffective in spares demand system are analyzed. Also, distinctoptimization policies are illustrated. What’s more, successful stories in practice are cited.
In traditional methods for reliability analysis, one complex system is often considered as being composed by some subsystems in series. Usually, the failure of any subsystem would be supposed to lead to the failure of the entire system. However, some subsystems' lifetimes are long enough and even never fail during the life cycle of the entire system. Moreover, such subsystems' lifetimes will not be influenced equally under different circumstances. In practice, such interferences will affect the model's accuracy, but it is seldom considered in traditional analysis. To address these shortcomings, this paper presents a new approach to do reliability analysis for complex systems. Here a certain fraction of the subsystems is defined as a "cure fraction" under the consideration that such subsystems' lifetimes are long enough and even never fail during the life cycle of the entire system. By introducing environmental covariates and the joint power prior, the proposed model is developed within the Bayesian survival analysis framework, and thus the problem for censored (or truncated) data in reliability tests can be resolved. In addition, a Markov chain Monte Carlo computational scheme is implemented and a numeric example is discussed to demonstrate the proposed model
In traditional methods for reliability analysis, one complex system is often considered as being composed by some subsystems in series. Usually, the failure of any subsystem would be supposed to lead to the failure of the entire system. However, some subsystems' lifetimes are long enough and even never fail during the life cycle of the entire system. Moreover, such subsystems' lifetimes will not be influenced equally under different circumstances. In practice, such interferences will affect the model's accuracy, but it is seldom considered in traditional analysis.
To address these shortcomings, this paper presents a new approach to do reliability analysis for complex systems. Here a certain fraction of the subsystems is defined as a "cure fraction" under the consideration that such subsystems' lifetimes are long enough and even never fail during the life cycle of the entire system.
By introducing environmental covariates and the joint power prior, the proposed model is developed within the Bayesian survival analysis framework, and thus the problem for censored (or truncated) data in reliability tests can be resolved. In addition, a Markov chain Monte Carlo computational scheme is implemented and a numeric example is discussed to demonstrate the proposed model
This paper presents preliminary analysis of the data from measurements on a minefield in Croatia done in the international cooperation project Airborne Minefield Area Reduction (ARC). Temperature differences above and around suspected mines and minefield indicators, were recorded with a long wave IR camera in 8-9 micrometers , over a time of several days, capturing data under different weather conditions. The data are compared to simulations of land mines, minefield indicators and other objects using a themodynamic FEM model, developed at FOI. Different detection methods are presented and applied to the data.
Linear minimum mean-squared error (LMMSE)-based channel equalization is widely used in multi-input multioutput (MIMO) underwater acoustic communications (UAC). The practical challenge of LMMSE based schemes is the necessity of matrix inversion which generally imposes heavy computational burden on the receiver. To obtain the LMMSE filters efficiently, we exploit the conjugate gradient method and the diagonalization properties of circulant matrices. The proposed scheme is based on fast Fourier transform operations and can be implemented in parallel, which makes it a promising candidate for real-time MIMO underwater acoustic communications. Both numerical and SPACE'08 experimental examples are presented to demonstrate the effectiveness of the proposed approach.
Effective training sequences and reliable channel estimation algorithms are essential for enhancing the performance of multi-input multi-output (MIMO) underwater acoustic communications (UAC). Also, effective interference cancellation schemes are crucial for reliable symbol detection. In this paper, the problem of designing MIMO training sequences is considered. Moreover, we present a sparse learning via iterative minimization (SLIM) algorithm for enhanced channel estimation and reduced computational complexity. Furthermore, RELAX-BLAST, a linear minimum mean-squared error based symbol detection scheme, is implemented efficiently by exploiting the conjugate gradient method and diagonalization properties of circulant matrices. The proposed MIMO UAC techniques are evaluated using both simulated and experimental examples.
This paper addresses multi-input multi-output (MIMO) communications over sparse acoustic channels suffering from frequency modulations. An extension of the recently introduced SLIM algorithm, which stands for sparse learning via iterative minimization, is presented to estimate the sparse and frequency modulated acoustic channels. The extended algorithm is referred to as generalization of SLIM (GoSLIM). The sparseness is exploited through a hierarchical Bayesian model, and because GoSLIM is user parameter free, it is easy to use in practical applications. Moreover this paper considers channel equalization and symbol detection for various MIMO transmission schemes, including both space-time block coding and spatial multiplexing, under the challenging channel conditions. The effectiveness of the proposed approaches is demonstrated using in-water experimental measurements recently acquired during WHOI09 and ACOMM10 experiments.
An embedded system is a small autonomous computer system that is encapsulated in the device it is controlling. Examples of device with embedded systems are TVs, microwaves, and dishwashers. A modern car can hold as much as 60 microprocessors. Embedded systems sales are rapidly increasing. In fact processor sales for embedded system are much larger than those for personal computers. PC processors only represents 2 percent of all processor sales. The division of EISLAB at Luleå University of Technology focuses on research on Embedded internet systems (EIS). Embedded Internet Systems are embedded systems used to collect, process and distribute data over the Internet. EIS devices is a rapidly growing field. The reduced physical size and price of EIS sensors make them attractive to use in a wide range of applications such as; environmental monitoring, industrial, health care, and military surveillance. The vision of EISLAB is sensors with lifetimes in the range of years that will fit within a few cubic centimeters. The sensors developed at EISLAB are based on commercial-of-the-shelf-components (COTS). Using COTS-components reduces development time and cost for prototypes and small series products. All communication is handled using standardized protocols to give the device access to available communication infrastructure, such as cellular networks and the Internet. As most of the nodes are battery operated the dominant constraint for EIS devices is power consumption. This thesis presents our work to address this topic at a high level of abstraction. The first paper includes the development of a power aware design methodology for EIS devices. In the second paper the proposed design methodology is applied to a bluetooth-equipped device. The third paper discusses the problem of reducing power consumption of an always connected EIS device, showing the tradeoffs between power consumption and response time.
Images recorded in ground areas potentially containing surface laid land mines are considered. The first hypothesis is that the image is of clutter (grass) only, while the alternative is that the image contains a partially occluded (covered) land mine in addition to the clutter. In such a scenario, the occlusion pattern is unknown and has to be treated as a nuisance parameter. In a previous paper it was shown that deterministic treatment of the unknown occlusion pattern, in companion with the applied model, renders a substantial increase in detector performance as compared to employment of the traditional additive model. However, a deterministic assumption ignores possible correlation and additional gains could be possible by taking the spatial properties into account. In order to incorporate knowledge regarding the occlusion, the spatial distribution is characterized in terms of an underlying Markov Random Field (MRF) model. A major concern with MRF models is their complexity. Therefore, in addition to this, a less computationally demanding technique to accommodate the occlusion behavior is also proposed. The main purpose of this paper is to investigate if significant gains are possible by acknowledging the spatial dependence. Evaluation on data using real occluded targets however indicates that the gain seem to be marginal.
Wireless Sensor Networks (WSNs) consist of small, autonomous devices with wireless networking capabilities. In order to further increase the applicability of WSNs in real world ap- plications, minimizing energy consumption and size are im- portant research topics. A WSN node itself is a complex system consisting of numerous components, and the energy consumption of the node depends heavily on the interac- tion between its components and their respective operation modes. To develop a power consumption model, we have investigated the power characteristics of a Bluetooth(BT)- equipped node based on COTS (commercial o®-the-shelf) components running standardized protocols for communica- tion. The characterization captures the transient behavior of the individual components as well as the dynamic behav- ior of the system as a whole. Although the parameters of the model are derived for a speci¯c node, the model and our conclusions can be applied to WSN nodes in general. Based on our model the estimated lifetime of a battery powered BT-equipped node can range from a couple of days to sev- eral months depending on battery and usage. This result indicates that COTS based sensor nodes can be used in a wide range of applications.
A key issue in research around embedded Internet systems (EIS) is to reduce power consumption. We envision EIS devices with lifetimes in the range of months or even years. This calls for developing aggressive power management techniques with a high degree of context awareness. As a first step towards this goal we introduce a design methodology for making context aware power optimizations of EIS. The presented design methodology which is verified by experimental results is a promising first step in prolonging operating time of battery powered wireless EIS.
Shallowly buried object sand the background soil have different specific heat and heat conductivity. Thus, the thermal alternation over day and night produces a thermal contrast on the soil surface that can be detected by an IR sensor. We present a simple model for the spatio-temporal temperature signature of a buried land mine by means of a 3D Gaussian function. Such a mode is appropriate since both measured data and simulations based on the finite element method show a spatio-temporal behavior that strongly resembles a 3D Gaussian shape. An advantage of modeling the signature as a Gaussian shape is that objects with approximately the same size but with different physical properties can be obtained simply from a scaled version of the original model.
We address high-level synthesis of low-power digital signal processing (DSP) systems by using efficient switching activity models. We present a technology-independent hierarchical scheme that can be easily integrated into current communications/DSP CAD tools for comparing the relative power/performance of two competing DSP designs without specific knowledge of transistor-level details. The basic building blocks considered for such systems are a full adder, a half adder, and a one-bit delay. Estimates of the switching activity at the output of these primitives are used to model the activity in more complex building blocks of DSP systems. The presented hierarchical method is very fast and simple. The accuracy of estimates obtained using the proposed approach is shown to be within 4% of the results obtained using extensive bit-level simulations. Our approach shows that the choice of multiplier/multiplicand is important when using array multipliers in a datapath. If the input signal with smaller mean square value is chosen as the multiplicand, almost 20% savings in switching activity can be achieved. This observation is verified by an analog simulation using a 16 × 16 bit array multiplier implemented in a 0.6-μ process with 3.3 V supply voltage.
We address the issue of high-level synthesis of low-power digital signal processing (DSP) systems by proposing switching activity models. In particular, we present a technology independent hierarchical scheme to compare relative power performance of two competing DSP systems. The basic building blocks considered for such system are a full-adder and a one-bit delay. Estimates of switching activity at the output of these building blocks is used to model the activity in different architectural primitives used for building DSP systems. This method is very fast and simple and simulations show accuracy within 4% of extensive bit-level simulations. Therefore, it can easily be integrated into current communications/DSP CAD tools for low-power applications. The models show that the choice of multiplier/multiplicand is important when using array multipliers in a data-path. If the input signal with smaller variance is chosen as the as the multiplicand, up to 20% savings in switching activity can be achieved. This observation is verified by analog simulation
This paper presents a multi-rank extension of the Capon beamformer. By expanding the rank of the beamformer it is possible to fully exploit situations in which signals lie in multi-dimensional subspaces, as opposed to the standard point source case. Such situations are commonly caused by array mismatches and scattered or distributed sources. The extension involves the design of a constraint matrix which can be interpreted in terms of signal power. Three possible choices for the constraint are proposed. These correspond to one non-adaptive choice, one choice that is dependent on the signal covariance structure only, and one choice that is both signal and data adaptive. Simulation examples are presented that show the promise of the idea of multi-rank Capon beamforming. Especially the signal- and data adaptive constraint appears very promising.
This paper derives the Haar measure over the set of unitary matrices. The Haar measure is essential when studying the statistical behavior of complex sample covariance matrices in terms of their eigenvalues and eigenvectors. The characterization is based on Murnaghans parameterization of unitary matrices which can be seen as a generalization of the representation of orthogonal matrices using Givens rotations. In addition to deriving the Haar measure, an efficient method to obtain samples from it is also presented
A parametric model for the infrared signature caused by a buried land mine is presented. Further, two ways of modeling the colored background noise, is proposed. In the first, it is assumed the noise can be approximated by an autoregressive process, while in the second, the statistics of the noise is described using recent development in texture modeling, the so called FRAME method. Given an a priori distribution of the mine parameters in combination with a trained noise distribution, a Bayesian detector is derived. Experiments indicate that significant gains in performance can be achieved as compared to the standard detector used, which correlates the infrared image with the known mine shape and thresholds the square of the output.
The aim of this report is to present work on threee processing methods with the potential to improve the performance of active sonar systems. Advantages of the processing methods are discussed and illustrated with simulation results.In addition, simulation packages are presented. Further work needed for a more detailed evaluation of the methods is indicated. We describe the development of software for an active sonar processor for a monostatic transceiver and a bistatic receiver including Doppler processing. Combined mono- and bistatic processing yields advantages in terms of covert operation of the bistatic receiver. Improved detection probability can be achieved and increased resilience to countermeasures. The objective has been to obtain a reference for comparison with new advanced sonar processors and a tool for accurate performance predictions.In addition, initial results from two promising methods are included.Synthetic Aperture Sonar (SAS) processing, commonly used in advanced mine hunting sonars, is here utilized at lower frequencies for surveillance applications.Space-Time Adaptive Processing (STAP), a signal processing technique used in radars to enhance the ability to detect targets that might otherwise be obscured by clutter or by jamming, can be applied in active sonar in order to reject reverberation. SAS has potential for improving the detection and classification performance of conventional sonars.Simulations made under idealised conditions indicate that good results can be obtained for relevant distances and frequencies. Fully adaptive STAP processing requires large amounts of training data, which is not always readily available in sonar applications. We present some alternative approaches that require less training data, with promising results.
This paper presents a noise-adaptive estimator for the linear model. The strategy is based on a hierarchical approach where in each step, a decreasing number of unbiased estimates for the parameter of interest is produced. In this way, the complexity is greatly reduced compared to standard estimators, like the adaptive maximum likelihood (AML) estimator. Also, since the method combines solutions to sub-problems of smaller dimensionality, the required size of the noise training data set is also reduced. As a result, the derived scheme performs better than AML for small sample support. The results are verified by simulations and show that the derived scheme is a very appropriate choice for a large class of problems with high dimensionality.
This paper presents a soft input/soft output linear equalizer for Alamouti encoded MIMO signals. The derived structure allows for simultaneous equalization of MIMO channels and decoding of Alamouti coded signals. The equalizer/decoder is here used within the turbo equalization framework to exploit the complex and rich characteristics of the acoustic underwater channel. Such schemes can operate ate very low signal-to-noise ratio (SNR) levels enabling high transmission rates over long distances. We investigate the viability of the technique by using a simulation example and by studying its behavior for a real scenario, using data collected in the Baltic Sea
Constrained minimization problems considered here arise in the design of multi-dimensional subspace beamformers for radar, sonar, seismology, and wireless communications, and in the design of precoders and equalizers for digital communications. The problem is to minimize a quadratic form, under a set of linear or quadratic constraints. We derive the solutions to these problems and establish connections between them. We show that the quadratically- constrained problem can be solved by solving a set of linearly-constrained problems and then using a majorization argument and Poincare's separation theorem to determine which linearly-constrained problem solves the quadratically-constrained one. In addition, we present illuminating circuit diagrams for our solutions, called generalized sidelobe canceller (GSC) diagrams, which allow us to tie our constrained minimizations to linear minimum mean-squared error (LMMSE) estimations
This paper addresses canonical correlation analysis of two-channel data, when channel covariances are estimated from a limited number of samples, and are not necessarily full-rank. We show that empirical canonical correlations measure the cosines of the principal angles between the row spaces of the data matrices for the two channels. When the number of samples is smaller than the sum of the ranks of the two data matrices, some of the empirical canonical correlations become one, regardless of the two-channel model that generates the samples. In such cases, the empirical canonical correlations may not be used as estimates of correlation between random variables.
We derive eigenvalue beamformers to resolve an unknown signal of interest whose spatial signature lies in a known subspace, but whose orientation in that subspace is otherwise unknown. The unknown orientation may be fixed, in which case the signal covariance is rank-1, or it may be random, in which case the signal covariance is multirank. We present a systematic treatment of such signal models and explain their relevance for modeling signal uncertainties. We then present a multirank generalization of the MVDR beamformer. The idea is to minimize the power at the output of a matrix beamformer, while enforcing a data dependent distortionless constraint in the signal subspace, which we design based on the type of signal we wish to resolve. We show that the eigenvalues of an error covariance matrix are fundamental for resolving signals of interest. Signals with rank-1 covariances are resolved by the largest eigenvalues of the error covariance, while signals with multirank covariances are resolved by the smallest eigenvalues. Thus, the beamformers we design are eigenvalue beamformers, which extract signal information from eigenmodes of an error covariance. We address the tradeoff between angular resolution of eigenvalue beamformers and the fraction of the signal power they capture.
We introduce a multi-channel soft input/soft output receiver for underwater communication that performs joint iterative channel estimation, linear equalization, and decoding. The transmitted symbols were encoded using a turbo coded bit-sequence. Our method exploits the gain present in the turbo code through a feed-back of soft information from the decoder to the channel estimator and to the equalizer. The use of several receiving hydrophones makes the method more robust to frequency selective or low-SNR channels, or can improve reception in channels with very large delay spreads. We give examples of such channels measured in the Baltic Sea at two different sea trials during 2005 and 2006. Properties like time variations of the channel impulse response or channel Doppler spread are given, as well as decoding examples using our method. It is shown that the multi-channel version of the receiver achieve error free reception of 4000 coded symbols per second up to 60 km through channels featuring very large delay spreads. The maximum transmitter source level used was 180 dB re 1μPa at 1m at the carrier frequency 12 kHz.
This paper presents a multi-rank generalization of the Capon beamformer to accommodate model mismatch in situations where the unknown signal of interest lies in a multidimensional subspace. By expanding the beamforming subspace robustness (or diversity) is achieved at the expense of resolution. The generalization involves solving a quadratically-constrained quadratic minimization problem, and designing a constraint matrix. Three strategies for designing this constraint matrix are discussed. Simulation examples are presented to demonstrate the performance of the multi-rank Capon beamformer.
This paper presents activities concerning optical detection of landmines at FOI, former FOA. The work is focused on the understanding of the origin of detectable optical signatures for choosing the most favorable conditions for detection. Measurements in test beds and calculations using a thermodynamic FEM model with conditions similar to those of the measurements are compared and interpreted in order to explain the behavior of the contrast. Examples will be given on modeling of buried landmines in soil. The heat flow as well as moisture flow has been taken into consideration. The diurnal heat exchange between the soil surface and the atmosphere generates the contrasts in the infrared images. Calculated temperature differences between the background and the surface above the buried object are compared to measured data from experiments. Results are presented and show how the temperature differences can vary over a 24-hour period. The variation depends on the weather at the time as well as the weather before the measurements started. Results from processing and analysis of temporal variations of optical signals from buried landmines and backgrounds are presented as well as their relation to weather parameters. A detection approach including the Likelihood Ratio Test (LRT) is presented. Some of the work has been carried out in an international cooperation project, Airborne Minefield Area Reduction (ARC). The objective is to develop, demonstrate and promote a new system for performing the UN Level 2 surveys allowing a quick reduction of suspected mine polluted areas and post cleaning quality control.
The paper describes a Bayesian approach to estimate the amplitude of a given signal embedded in complex zero-mean Gaussian noise with unknown covariance. By employing Jeffreys priors to unknown parameters, the posterior distribution is derived analytically. While the resulting estimates are merely reproductions of classical estimates, the Bayesian approach offers an enhanced ability to predict the quality of estimates conditioned on the measured data. This ability is further highlighted by simulations using finite training sets.