Distributed target localization using quantized received signal strength - Li, Zeyuan; Chung, Pei-Jung; Mulgrew, Bernard (Signal processing, 2017)
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.