Distributed target localization using quantized received signal strength

In this paper, we propose a distributed gradient algorithm for received signal strength based target localization using only quantized data. The Maximum Likelihood of the Quantized RSS is derived and Particle Swarm Optimization is used to provide an initial estimate for the gradient algorithm. A pra...

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Vydané v:Signal processing Ročník 134; s. 214 - 223
Hlavní autori: Li, Zeyuan, Chung, Pei-Jung, Mulgrew, Bernard
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.05.2017
Predmet:
Quantization
Received signal strength
Target localization
Quantization
Received signal strength
Target localization
ISSN:0165-1684, 1872-7557
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Popis
Shrnutí:In this paper, we propose a distributed gradient algorithm for received signal strength based target localization using only quantized data. The Maximum Likelihood of the Quantized RSS is derived and Particle Swarm Optimization is used to provide an initial estimate for the gradient algorithm. A practical quantization threshold designer is presented for RSS data. To derive a distributed algorithm using only the quantized signal, the local estimate at each node is also quantized. The RSS measurements and the local estimate at each sensor node are quantized in different ways. By using a quantization elimination scheme, a quantized distributed gradient method is proposed. In the distributed algorithm, the quantization noise in the local estimate is gradually eliminated with each iteration. Section 5 shows that the performance of the centralized algorithm can reach the Cramer Rao Lower Bound. The proposed distributed algorithm using a small number of bits can achieve the performance of the distributed gradient algorithm using unquantized data. •The quantized distributed gradient method is applied for target localization.•Particle swarm optimization is used to provide an initial estimate.•A quantization error elimination scheme reduces communication cost.•A quantization method for RSS measurements is presented.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2016.12.003