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  • 1.
    Alam, Assad
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Asplund, Fredrik
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Behere, Sagar
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Björk, Mattias
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Garcia Alonso, Liliana
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Khaksari, Farzad
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Khan, Altamash
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Kjellberg, Joakim
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Liang, Kuo-Yun
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Lyberger, Rickard
    Scania CV AB.
    Mårtensson, Jonas
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Nilsson, John-Olof
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Pettersson, Henrik
    Scania CV AB.
    Pettersson, Simon
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Stålklinga, Elin
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Zachariah, Dave
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Cooperative driving according to Scoop2011Report (Other academic)
    Abstract [en]

    KTH Royal Institute of Technology and Scania are entering the GCDC 2011 under the name Scoop –Stockholm Cooperative Driving. This paper is an introduction to their team and to the technical approach theyare using in their prototype system for GCDC 2011.

  • 2.
    Blasco-Serrano, Ricardo
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Ericsson Research.
    Zachariah, Dave
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Division of Systems and Control. Division of Systems and Control. Uppsala University.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Thobaben, Ragnar
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    A Measurement Rate-MSE Tradeoff for Compressive Sensing Through Partial Support Recovery2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 18, 4643-4658 p.Article in journal (Refereed)
    Abstract [en]

    We study the fundamental relationship between two relevant quantities in compressive sensing: the measurement rate, which characterizes the asymptotic behavior of the dimensions of the measurement matrix in terms of the ratio m/ log n (m being the number of measurements and n the dimension of the sparse signal), and the mean square estimation error. First, we use an information-theoretic approach to derive sufficient conditions on the measurement rate to reliably recover a part of the support set that represents a certain fraction of the total signal power when the sparsity level is fixed. Second, we characterize the mean square error of an estimator that uses partial support set information. Using these two parts, we derive a tradeoff between the measurement rate and the mean square error. This tradeoff is achievable using a two-step approach: first support set recovery, then estimation of the active components. Finally, for both deterministic and random signals, we perform a numerical evaluation to verify the advantages of the methods based on partial support set recovery.

  • 3.
    Blasco-Serrano, Ricardo
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Zachariah, Dave
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Thobaben, Ragnar
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    An Achievable Measurement Rate-MSE Tradeoff in Compressive Sensing Through Partial Support Recovery2013In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New York: IEEE , 2013, 6426-6430 p.Conference paper (Refereed)
    Abstract [en]

    For compressive sensing, we derive achievable performance guarantees for recovering partial support sets of sparse vectors. The guarantees are determined in terms of the fraction of signal power to be detected and the measurement rate, defined as a relation between the dimensions of the measurement matrix. Based on this result we derive a tradeoff between the measurement rate and the mean square error, and illustrate it by a numerical example.

  • 4.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Hybrid greedy pursuit2011In: 19th European Signal Processing Conference (EUSIPCO 2011), 2011, 343-347 p.Conference paper (Refereed)
    Abstract [en]

    For constructing the support set of a sparse vector in the standardcompressive sensing framework, we develop a hybridgreedy pursuit algorithm that combines the advantages ofserial and parallel atom selection strategies. In an iterativeframework, the hybrid algorithm uses a joint sparsity informationextracted from the independent use of serial and parallelgreedy pursuit algorithms. Through experimental evaluations,the hybrid algorithm is shown to provide a significantimprovement for the support set recovery performance.

  • 5.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Look ahead orthogonal matching pursuit2011In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2011, 4024-4027 p.Conference paper (Refereed)
  • 6.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skolglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Robust matching pursuit for recovery of Gaussian sparse signal2011In: 2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011 - Proceedings, 2011, 420-424 p.Conference paper (Refereed)
    Abstract [en]

    For compressive sensing (CS) recovery of Gaussian sparse signal, we explore the framework of Bayesian linear models to achieve a robust reconstruction performance in the presence of measurement noise. Using a priori statistical knowledge, we develop a minimum mean square error (MMSE) estimation based iterative greedy search algorithm. Through experimental evaluations, we show that the new algorithm provides a robust CS reconstruction performance compared to an existing least square based algorithm.

  • 7.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skolglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Statistical post-processing improves basis pursuit denoising performance2010In: 2010 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2010, 2010, 23-27 p.Conference paper (Refereed)
  • 8.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Vehkaperä, Mikko
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Skolglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Projection-based and look ahead strategies for atom selection2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 2, 634-647 p.Article in journal (Refereed)
    Abstract [en]

    In this paper, we improve iterative greedy search algorithms in which atoms are selected serially over iterations, i.e., one-by-one over iterations. For serial atom selection, we devise two new schemes to select an atom from a set of potential atoms in each iteration. The two new schemes lead to two new algorithms. For both the algorithms, in each iteration, the set of potential atoms is found using a standard matched filter. In case of the first scheme, we propose an orthogonal projection strategy that selects an atom from the set of potential atoms. Then, for the second scheme, we propose a look-ahead strategy such that the selection of an atom in the current iteration has an effect on the future iterations. The use of look-ahead strategy requires a higher computational resource. To achieve a tradeoff between performance and complexity, we use the two new schemes in cascade and develop a third new algorithm. Through experimental evaluations, we compare the proposed algorithms with existing greedy search and convex relaxation algorithms.

  • 9.
    Mårtensson, Jonas
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Alam, Assad
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Behere, Sagar
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Khan, Altamash
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Kjellberg, Joakim
    Liang, Kuo-Yun
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Pettersson, Henrik
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    The development of a cooperative heavy-duty vehicle for the GCDC 2011: Team Scoop2012In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 13, no 3, 1033-1049 p.Article in journal (Refereed)
    Abstract [en]

    The first edition of the Grand Cooperative Driving Challenge (GCDC) was held in the Netherlands in May 2011. Nine international teams competed in urban and highway platooning scenarios with prototype vehicles using cooperative adaptive cruise control. Team Scoop, a collaboration between KTH Royal Institute of Technology, Stockholm, Sweden, and Scania CV AB, Sodertalje, Sweden, participated at the GCDC with a Scania R-series tractor unit. This paper describes the development and design of Team Scoop's prototype system for the GCDC. In particular, we present considerations with regard to the system architecture, state estimation and sensor fusion, and the design and implementation of control algorithms, as well as implementation issues with regard to the wireless communication. The purpose of the paper is to give a broad overview of the different components that are needed to develop a cooperative driving system: from architectural design, workflow, and functional requirement descriptions to the specific implementation of algorithms for state estimation and control. The approach is more pragmatic than scientific; it collects a number of existing technologies and gives an implementation-oriented view of a cooperative vehicle. The main conclusion is that it is possible, with a modest effort, to design and implement a system that can function well in cooperation with other vehicles in realistic traffic scenarios.

  • 10.
    Sundin, Martin
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Beamformers For Sparse Recovery2013In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New York: IEEE , 2013, 5920-5924 p.Conference paper (Refereed)
    Abstract [en]

    In sparse recovery from measurement data a common approach is to use greedy pursuit reconstruction algorithms. Most of these algorithms have a correlation filter for detecting active components in the sparse data. In this paper, we show how modifications can be made for the greedy pursuit algorithms so that they use beamformers insteadof the standard correlation filter. Using these beamformers, improved performance in the algorithms is obtained. In particular, we discuss beamformers for the average and worst case scenario and give methods for constructing them.

  • 11.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Compressed Sensing: Algorithms and Applications2012Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The theoretical problem of finding the solution to an underdeterminedset of linear equations has for several years attracted considerable attentionin the literature. This problem has many practical applications.One example of such an application is compressed sensing (cs), whichhas the potential to revolutionize how we acquire and process signals. Ina general cs setup, few measurement coefficients are available and thetask is to reconstruct a larger, sparse signal.In this thesis we focus on algorithm design and selected applicationsfor cs. The contributions of the thesis appear in the following order:(1) We study an application where cs can be used to relax the necessityof fast sampling for power spectral density estimation problems. Inthis application we show by experimental evaluation that we can gainan order of magnitude in reduced sampling frequency. (2) In order toimprove cs recovery performance, we extend simple well-known recoveryalgorithms by introducing a look-ahead concept. From simulations it isobserved that the additional complexity results in significant improvementsin recovery performance. (3) For sensor networks, we extend thecurrent framework of cs by introducing a new general network modelwhich is suitable for modeling several cs sensor nodes with correlatedmeasurements. Using this signal model we then develop several centralizedand distributed cs recovery algorithms. We find that both thecentralized and distributed algorithms achieve a significant gain in recoveryperformance compared to the standard, disconnected, algorithms.For the distributed case, we also see that as the network connectivity increases,the performance rapidly converges to the performance of thecentralized solution.

  • 12.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Greedy Algorithms for Distributed Compressed Sensing2014Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Compressed sensing (CS) is a recently invented sub-sampling technique that utilizes sparsity in full signals. Most natural signals possess this sparsity property. From a sub-sampled vector, some CS reconstruction algorithm is used to recover the full signal. One class of reconstruction algorithms is formed by the greedy pursuit, or simply greedy, algorithms, which is popular due to low complexity and good performance. Meanwhile, in sensor networks, sensor nodes monitor natural data for estimation or detection. One application of sensor networking is in cognitive radio networks, where sensor nodes want to estimate a power spectral density. The data measured by different sensors in such networks are typically correlated. Another type are multiple processor networks of computational nodes that cooperate to solve problems too difficult for the nodes to solve individually.

    In this thesis, we mainly consider greedy algorithms for distributed CS. To this end, we begin with a review of current knowledge in the field. Here, we also introduce signal models to model correlation and network models for simulation of network. We proceed by considering two applications; power spectrum density estimation and distributed reconstruction algorithms for multiple processor networks. Then, we delve deeper into the greedy algorithms with the objective to improve reconstruction performance; this naturally comes at the expense of increased computational complexity. The main objective of the thesis is to design greedy algorithms for distributed CS that exploit data correlation in sensor networks to improve performance. We develop several such algorithms, where a key element is to use intuitive democratic voting principles. Finally, we show the merit of such voting principles by probabilistic analysis based on a new input/output system model of greedy algorithms in CS.

    By comparing the new single sensor algorithms to well known greedy pursuit algorithms already present in the literature, we see that the goal of improved performance is achieved. We compare complexity using big-O analysis where the increased complexity is characterized. Using simulations we verify the performance and confirm complexity claims. The complexity of distributed algorithms is typically harder to analyze since it depends on the specific problem and network topology. However, when analysis is not possible, we provide extensive simulation results. No distributed algorithms based on the signal-models used in this thesis were so far available in the literature. Therefore, we compare our algorithms to standard single-sensor algorithms, and our results can then easily be used as benchmarks for future research. Compared to the stand-alone case, the new distributed algorithms provide significant performance gains. Throughout the thesis, we strive to present the work in a smooth flow of algorithm design, simulation results and analysis.

  • 13.
    Sundman, Dennis
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Distributed greedy pursuit algorithms2014In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 105, 298-315 p.Article in journal (Refereed)
    Abstract [en]

    For compressed sensing over arbitrarily connected networks, we consider the problem of estimating underlying sparse signals in a distributed manner. We introduce a new signal model that helps to describe inter-signal correlation among connected nodes. Based on this signal model along with a brief survey of existing greedy algorithms, we develop distributed greedy algorithms with low communication overhead. Incorporating appropriate modifications, we design two new distributed algorithms where the local algorithms are based on appropriately modified existing orthogonal matching pursuit and subspace pursuit. Further, by combining advantages of these two local algorithms, we design a new greedy algorithm that is well suited for a distributed scenario. By extensive simulations we demonstrate that the new algorithms in a sparsely connected network provide good performance, close to the performance of a centralized greedy solution.

  • 14.
    Sundman, Dennis
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    FROGS: A serial reversible greedy search algorithm2012In: 2012 Swedish Communication Technologies Workshop, Swe-CTW 2012, IEEE , 2012, 40-45 p.Conference paper (Refereed)
    Abstract [en]

    For compressed sensing, in the framework of greedy search reconstruction algorithms, we introduce the notion of initial support-set. The initial support-set is an estimate given to a reconstruction algorithm to improve the performance of the reconstruction. Furthermore, we classify existing greedy search algorithms as being serial or parallel. Based on this classification and the goal of robustness to errors in the initial support-sets we develop a new greedy search algorithm called FROGS. We end the paper with careful numerical experiments concluding that FROGS perform well compared to existing algorithms (both in terms of performance and execution time) and that it is robust against errors in the initial support-set.

  • 15.
    Sundman, Dennis
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Greedy pursuits for compressed sensing of jointly sparse signals2011In: European Signal Processing Conference, 2011, 368-372 p.Conference paper (Refereed)
    Abstract [en]

    For compressed sensing with jointly sparse signals, we present a new signal model and two new joint iterativegreedy-pursuit recovery algorithms. The signal model is based on the assumption of a jointly shared support-set and the joint recovery algorithms have knowledge of the size of the shared support-set. Through experimental evaluation, we show that the new joint algorithms provide significant performance improvements compared to regular algorithms which do not exploit a joint sparsity.

  • 16.
    Sundman, Dennis
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Greedy Pursuits for Distributed Compressed SensingIn: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476Article in journal (Other academic)
  • 17.
    Sundman, Dennis
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    On the use of Compressive Sampling for Wide-band Spectrum Sensing2010In: 2010 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), IEEE , 2010, 354-359 p.Conference paper (Refereed)
    Abstract [en]

    In a scenario where a cognitive radio unit wishes to transmit, it needs to know over which frequency bands it can operate. It can obtain thisknowledge by estimating the power spectral density from a Nyquist-rate sampled signal. For wide-band signals sampling at the Nyquistrate is a major challenge and may be unfeasible. In this paper we accurately detect spectrum holes in sub-Nyquist frequencies without assuming wide sense stationarity in the compressed sampled signal. A novel extension to further reduce the sub-Nyquist samples is thenpresented by introducing a memory based compressed sensing thatrelies on the spectrum to be slowly varying.

  • 18.
    Sundman, Dennis
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Parallel pursuit for distributed compressed sensing2013In: 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings, IEEE conference proceedings, 2013, 783-786 p.Conference paper (Refereed)
    Abstract [en]

    We develop a greedy (pursuit) algorithm for a distributed compressed sensing problem where multiple sensors are connected over a de-centralized network. The algorithm is referred to as distributed parallel pursuit and it solves the distributed compressed sensing problem in two stages; first by a distributed estimation stage and then an information fusion stage. Along with worst case theoretical analysis for the distributed algorithm, we also perform simulation experiments in a controlled manner. We show that the distributed algorithm performs significantly better than the stand-alone (disconnected) solution and close to a centralized (fully connected to a central point) solution.

  • 19.
    Sundman, Dennis
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skolglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Greedy pursuits of compressed sensing of jointly sparse signal2011Conference paper (Refereed)
  • 20.
    Sundman, Dennis
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Saikat, Chatterjee
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    A Greedy Pursuit Algorithm for Distributed Compressed Sensing2012In: Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, IEEE , 2012, 2729-2732 p.Conference paper (Refereed)
    Abstract [en]

    We develop a greedy pursuit algorithm for solving the distributed compressed sensing problem in a connected network. This algorithm is based on subspace pursuit and uses the mixed support-setsignal model. Through experimental evaluation, we show that the distributed algorithm performs significantly better than the standalone (disconnected) solution and close to a centralized (fully connected to a central point) solution.

  • 21.
    Sundman, Dennis
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Saikat, Chatterjee
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Look Ahead Parallel Pursuit2011In: 2011 IEEE Swedish Communication Technologies Workshop, Swe-CTW 2011, 2011, 114-117 p.Conference paper (Refereed)
    Abstract [en]

    We endeavor to improve compressed sensing reconstruction performance of parallel pursuit algorithms. In an iteration, standard parallel pursuit algorithms use a support-set expansion by a fixed number of coefficients, leading to restricted performance. To achive a better performance, we develop a look ahead strategy that adaptively chooses the best number of coefficients. We develop a new algorithm which we call look ahead parallel pursuit, where a look ahead strategy is invoked on a minimal residual norm criterion. The new algorithm provides a trade-off between performance and complexity.

  • 22.
    Sundman, Dennis
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Saikat, Chatterjee
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Methods for Distributed Compressed Sensing2014In: Journal of Sensor and Actuator Networks, ISSN 2224-2708, Vol. 3, no 1, 1-25 p.Article in journal (Refereed)
    Abstract [en]

    Compressed sensing is a thriving research field covering a class of problems where a large sparse signal is reconstructed from a few random measurements. In the presence of several sensor nodes measuring correlated sparse signals, improvements in terms of recovery quality or the requirement for a fewer number of local measurements can be expected if the nodes cooperate. In this paper, we provide an overview of the current literature regarding distributed compressed sensing; in particular, we discuss aspects of network topologies, signal models and recovery algorithms.

  • 23.
    Sundman, Dennis
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Zachariah, Dave
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Saikat, Chatterjee
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Distributed Predictive Subspace Pursuit2013In: 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2013, New York: IEEE , 2013, 4633-4637 p.Conference paper (Refereed)
    Abstract [en]

    In a compressed sensing setup with jointly sparse, correlated data,we develop a distributed greedy algorithm called distributed predic-tive subspace pursuit. Based on estimates from neighboring sensornodes, this algorithm operates iteratively in two steps: first forminga prediction of the signal and then solving the compressed sensingproblem with an iterative linear minimum mean squared estimator.Through simulations we show that the algorithm provides better per-formance than current state-of-the-art algorithms.

1 - 23 of 23
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