Compressive sensing based target counting and localization exploiting joint sparsity

Lagunas, Eva; Sharma, Shree Krishna; Chatzinotas, Symeon; Ottersten, Björn

doi:10.1109/icassp.2016.7472274

Cited by 25 publications

(19 citation statements)

References 28 publications

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“…Also, the state-of-art recovery algorithm is applied to compare with the Ges-DFL algorithm, including Temporally Sparse Bayesian Learning (TSBL) [32], Multiple-Focal Underdetermined System Solver (M-FOCUSS) [33], Multiple Greedy Marching Pursuit (MGMP) [34], Simultaneous Orthogonal Marching Pursuit SOMP [35] and Variational Bayesian Expect-Mean (VBEM) [31].…”

Section: A the Simulation Performancementioning

confidence: 99%

Device-Free Localization Scheme With Time-Varying Gestures Using Block Compressive Sensing

Guo

Yang

et al. 2020

IEEE Access

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In Device-Free Localization (DFL) schemes, most researches assume that the targets are stationary with fixed gestures and motions. However, objects always change their gestures in practice, undoubtedly influencing the localization accuracy. To solve the issue, we propose a new algorithm named Ges-DFL under the Compressive Sensing (CS) framework, which considers the time-varying target gestures in the DFL scheme. Firstly, leaning that the matrixes referring to different target gestures cannot be achieved, we transfer them to a fixed sensing matrix by the transferring method. Secondly, we build the localization scheme as an MMV recovery issue and exploit the block sparsity property of transferred location vectors to improve the localization accuracy. Thirdly, the new algorithm Ges-DFL scheme is designed under the framework of the variational Bayesian inference to reconstruct transferred location vectors. Simulations show that the proposed algorithm performs well both in localization accuracy and robustness.

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Section: A the Simulation Performancementioning

confidence: 99%

Device-Free Localization Scheme With Time-Varying Gestures Using Block Compressive Sensing

Guo

Yang

et al. 2020

IEEE Access

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“…The research in [31] considered the same problem in a novel two-dimensional framework, where RSS samples are compressed in the space domain and the location information is compressed in the time domain. By exploiting the joint sparsity feature of the target location, the multiple measurement vectors model was adopted to tackle the time-varying environment [32]. In [33], a multi-channel indoor localization approach was proposed to overcome the channel mismatch problem and the matrix completion theory was adopted to reduce the training workload.…”

Section: B Compressive Sensing Based Localizationmentioning

confidence: 99%

“…Additionally, when multiple targets are simultaneously active, signals transmitted by targets are overlapped at each sensor. To this end, as in [8], [28], [32], [36]- [38], we further assume that the strengths of targets will be linearly superimposed without any interaction between them. For instance, as shown in Fig.…”

Section: A Signal Modelmentioning

confidence: 99%

Variational Bayesian Inference-Based Multiple Target Localization in WSNs With Quantized Received Signal Strength

Qian

Guo

et al. 2019

IEEE Access

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The received signal strength (RSS)-based target localization is an important field of research with numerous applications in wireless sensor networks. By exploiting the sparsity of localization, the compressive sensing (CS) can be applied to develop an effective localization framework for multiple targets. However, the existing CS-based method considers localization directly from real-valued sensor measurements, which implicitly assume that the measurements have infinite bit precision. When the assumption is violated, the localization performance will deteriorate dramatically, especially at low quantization bit rates. In this paper, we first design a quantizer for efficiently processing the raw RSS measurements and then develop a novel Bayesian CS framework for estimating target locations from the quantized measurements. By modeling the quantization errors as independent random variables, the non-linear localization problem is formulated in a linear CS observation model. Following this idea, we solve the problem from a Bayesian perspective and design some sophisticated prior distributions to guarantee the performance. To address this, we resort to the variational Bayesian inference methodology and propose a novel iterative algorithm for jointly estimating target locations and dealing with quantization errors. The extensive simulation results demonstrate the superiority of the proposed algorithm in comparison with the state-of-the-art methods.

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“…The contribution in [157] proposed a CS-based approach for sparse target counting and positioning in wireless sensor networks by employing a novel Greedy Matching pursuit (GMP) algorithm. Recently, authors in [158] studied the problem of target counting and localization by exploiting the joint sparsity feature of an MMV model and demonstrated that the performance of the proposed MMV approach is superior than that of the conventional single measurement vector method in terms of target counting and localization accuracies.…”

Section: B Related Literaturementioning

confidence: 99%

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Sharma

Lagunas

Chatzinotas

et al. 2016

IEEE Commun. Surv. Tutorials

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205

112

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Abstract-Compressive Sensing (CS) has received much attention in several fields such as digital image processing, wireless channel estimation, radar imaging, and Cognitive Radio (CR) communications. Out of these areas, this survey paper focuses on the application of CS in CR communications. Due to the underutilization of the allocated radio spectrum, spectrum occupancy is usually sparse in different domains such as time, frequency and space. Such a sparse nature of the spectrum occupancy has inspired the application of CS in CR communications. In this regard, several researchers have already applied the CS theory in various settings considering the sparsity in different domains. In this direction, this survey paper provides a detailed review of the state of the art related to the application of CS in CR communications. Starting with the basic principles and the main features of CS, it provides a classification of the main usage areas based on the radio parameter to be acquired by a wideband CR. Subsequently, we review the existing CSrelated works applied to different categories such as wideband sensing, signal parameter estimation and Radio Environment Map (REM) construction, highlighting the main benefits and the related issues. Furthermore, we present a generalized framework for constructing the REM in compressive settings. Finally, we conclude this survey paper with some suggested open research challenges and future directions.

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Compressive sensing based target counting and localization exploiting joint sparsity

Cited by 25 publications

References 28 publications

Device-Free Localization Scheme With Time-Varying Gestures Using Block Compressive Sensing

Device-Free Localization Scheme With Time-Varying Gestures Using Block Compressive Sensing

Variational Bayesian Inference-Based Multiple Target Localization in WSNs With Quantized Received Signal Strength

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

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