Sparse target counting and localization in sensor networks based on compressive sensing

Zhang, Bowu; Cheng, Xiuzhen; Zhang, Nan; Cui, Yong; Yingshu, Li; Liang, Qilian

doi:10.1109/infcom.2011.5935041

Cited by 145 publications

(132 citation statements)

References 17 publications

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“…In most of the aforementioned applications, target counting is often considered as the first step and the problem of counting multiple targets is non-trivial since it needs to differentiate and count the targets [2]. In addition, determining the physical location of targets such as wireless transmit nodes is a fundamental requirement and a key challenge in WSN applications.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, determining the physical location of targets such as wireless transmit nodes is a fundamental requirement and a key challenge in WSN applications. In this context, target counting and localization has been a topic of active research in the recent years [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…Examples of target localization by interpreting RSS can be found in [12][13][14]. The existing target counting and localization methods can be broadly categorized into the following types [2]: (i) non-CS based, and (ii) CS-based. The non-CS based target counting methods can be further categorized into: (i) Minimalistic approach [15], (ii) Binary sensingbased approach [16,17], and (iii) Clustering-based approach [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…In order to take advantage of the sparse nature of WSNs, recent contributions have focussed on CS-based target counting and localization [2,4,5,11,[20][21][22][23][24]. Out of these contributions, [2,11] have focussed on CS-based target counting, [4,5] has focussed on both target counting and localization, and the rest have focussed on CSbased localization.…”

Section: Introductionmentioning

confidence: 99%

“…Out of these contributions, [2,11] have focussed on CS-based target counting, [4,5] has focussed on both target counting and localization, and the rest have focussed on CSbased localization. The contribution in [11] has proposed CS-based Baysian detection and heuristic approaches which can reduce the sampling rate by 25% without compromising the detection performance.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Compressive sensing based target counting and localization exploiting joint sparsity

Lagunas

Sharma

Chatzinotas

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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One of the fundamental issues in Wireless Sensor Networks (WSN) is to count and localize multiple targets accurately. In this context, there has been an increasing interest in the literature in using Compressive Sensing (CS) based techniques by exploiting the sparse nature of spatially distributed targets within the monitored area. However, most existing works aim to count and localize the sparse targets utilizing a Single Measurement Vector (SMV) model. In this paper, we consider the problem of counting and localizing multiple targets exploiting the joint sparsity feature of a Multiple Measurement Vector (MMV) model. Furthermore, the conventional MMV formulation in which the same measurement matrix is used for all sensors is not valid any more in practical time-varying wireless environments. To overcome this issue, we reformulate the MMV problem into a conventional SMV in which MMVs are vectorized. Subsequently, we propose a novel reconstruction algorithm which does not need the prior knowledge of the sparsity level unlike the most existing CS-based approaches. Finally, we evaluate the performance of the proposed algorithm and demonstrate the superiority of the proposed MMV approach over its SMV counterpart in terms of target counting and localization accuracies.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Compressive sensing based target counting and localization exploiting joint sparsity

Lagunas

Sharma

Chatzinotas

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Distributed compressed sensing in wireless local area networks

Yang

Huang

et al. 2013

Int J Communication

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SUMMARYIn order to improve energy efficiency in the process of gathering data and transmitting information, compressed sensing (CS) has been applied increasingly in wireless local area networks (WLANs). In traditional usages of CS techniques in previous literatures, the sparsities of the signals have to be known beforehand, which is significant for the recovered results. However, it is difficult to realize precisely the structures of the actual signals in WLANs. Therefore, it is important to further exploit the reasonable practicality of signals in actual applications. In this paper, we present a spatial-temporal correlation model to optimize measure matrix of CS on the basis of a local region. Furthermore, two algorithms based on two kinds of situations are designed, both of which are pervasive data distributions in real environments. Our algorithms have been proven to be valuable and could be considered for actual applications. Experiments show that the signals could be reconstructed accurately and stably even if their sparsities could not be known in advance.

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On Target Counting by Sequential Snapshots of Binary Proximity Sensors

Wang

Song

et al. 2015

Lecture Notes in Computer Science

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Sparse target counting and localization in sensor networks based on compressive sensing

Cited by 145 publications

References 17 publications

Compressive sensing based target counting and localization exploiting joint sparsity

Compressive sensing based target counting and localization exploiting joint sparsity

Distributed compressed sensing in wireless local area networks

On Target Counting by Sequential Snapshots of Binary Proximity Sensors

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