An Efficient Counting and Localization Framework for Off-Grid Targets in WSNs

Sun, Baoming; Guo, Yan; Li, Ning; Fang, Da‐Gang

doi:10.1109/lcomm.2016.2644659

Cited by 17 publications

(11 citation statements)

References 10 publications

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“…To consider target localization in the presence of sensor position uncertainty, a CS-based framework was developed for jointly localizing targets and adjusting inaccurate sensor positions [36]. Moreover, the works presented in [37], [38] focused on solving the off-grid target problem in CS-based localization and proposed effective solutions by using the sparse Bayesian learning method.…”

Section: B Compressive Sensing Based Localizationmentioning

confidence: 99%

“…Otherwise, the miss-distance between the actual and approximated location of the target will deteriorate the estimation accuracy. We referred it as the off-grid problem, which can be handled by further approximating the sensing matrix with its Taylor expansion to estimate the mismatch between target location and grid centroid, as proposed in [37], [38]. In this paper, we preclude the off-grid problem, and focus on investigating and handling the effect of quantization.…”

Section: B Cs-based Localization With Quantized Measurementsmentioning

confidence: 99%

“…For each quantization error e i (i = 1, • • • , M ), its prior p (e i ) is the linear function p(e i ) = (k i e i + h i ) /C i within the interval (l ei , u ei ) as defined in (15). From (37), it follows that the posterior q (e i ) is the product of p (e i ) and the Gaussian distribution N e i |µ ei , σ 2 ei , where µ ei = z i − φ T i µ and σ ei = β − 1 2 . It is equivalent to the product of the linear function and a truncated Gaussian distribution with the interval (l ei , u ei ).…”

Section: ) Update Of the Quantization Error Ementioning

confidence: 99%

“…In this section, we validate and illustrate our theoretical results by Monte Carlo simulations. These are conducted in different scenarios to compare the proposed Q-VBI algorithm with five of the best representative algorithms currently available in the literature: (i) Basis Pursuit (BP) [25], [31], (ii) Orthogonal Matching Pursuit (OMP) [43], (iii) Bayesian Compressive Sensing (BCS) [44], (iv) Variational Bayesian Expectation-Maximization (VBEM) [37], and (v) Quantized Variational Message Passing (Q-VMP) [22].…”

Section: Numerical Simulationsmentioning

confidence: 99%

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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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Section: B Compressive Sensing Based Localizationmentioning

confidence: 99%

Section: B Cs-based Localization With Quantized Measurementsmentioning

confidence: 99%

Section: ) Update Of the Quantization Error Ementioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

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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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“…In [26], the indoor visible light positioning was formulated as a CS sparse reconstruction problem and a 3-step workflow is proposed accordingly to solve it. The works presented in [27]- [29] focused on solving the off-grid target problem and proposed effective solutions under the Bayesian framework.…”

Section: Related Workmentioning

confidence: 99%

Block Variational Bayesian Algorithm for Multiple Target Localization With Unknown and Time-Varying Transmit Powers in WSNs

Qian

Guo

2019

IEEE Access

Self Cite

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Target localization is an important research field in wireless sensor networks and the received signal strength (RSS)-based method is of particular interest. Since traditional RSS-based method is invalid for simultaneously localizing multiple targets, the compressive sensing theory has been applied to develop an effective localization framework in recent years. However, most existing works implicitly assume the transmit powers of targets are known and constant, which contradicts the practical application scenario as the power might change with time and usually difficult to be known. In this paper, we consider the challenging problem of multiple target localization with unknown and time-varying transmit powers by exploiting multiple measurement vectors (MMVs). Since the conventional MMV formulation is incapable of the time-varying environment, we develop a novel MMV framework, in which the temporal correlation of varying powers can be further exploited. In this way, the problem is transformed to reconstruct a block-sparse signal and learn its inner correlation structure. To address this, a block-sparse reconstruction algorithm is designed, based on the novel application of the variational Bayesian approximation and the expectation-maximization methodology. The proposed algorithm can jointly estimate locations and varying powers of multiple targets with high accuracy. The extensive simulation results demonstrate the superiority of the proposed method in comparison with the state-of-the-art algorithms.

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A Multi-emitter Localization Method Based on Improved Sparse Bayesian Learning Under Multi-station Association

Wang¹,

Zhou²,

Bai³

et al. 2022

Lecture Notes in Electrical Engineering

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An Efficient Counting and Localization Framework for Off-Grid Targets in WSNs

Cited by 17 publications

References 10 publications

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

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

Block Variational Bayesian Algorithm for Multiple Target Localization With Unknown and Time-Varying Transmit Powers in WSNs

A Multi-emitter Localization Method Based on Improved Sparse Bayesian Learning Under Multi-station Association

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