Dynamic Quantizer Design for Target Tracking for Wireless Sensor Network With Imperfect Channels

Abstract

Wireless sensor networks (WSNs) have been demonstrated to enhance parameter estimation performance for target tracking. In this paper, a prior information based quantizer design framework is proposed for target tracking for WSNs. In the proposed framework, the imperfect wireless channels between local sensors and the fusion center are considered. To make full use of the historical states and measurements embedded into the Bayesian tracking methodology, the quantizer design is suggested to be implemented with considering the prior state information. To this end, a channel-aware posterior Cramér-Rao lower bound (PCRLB) is derived based on the state prediction and further used as the performance indicator for quantizer design. Regarding target tracking, we model the quantizer design problem as a non-convex and highly nonlinear optimization problem that is intractable in general. We split the problem in terms of different scenarios, and for one-bit quantizer design based on a binary symmetric channel (BSC), we find that the optimal solution can be analytically computed. While for the general fading channel-based quantizer design problems, we propose two polynomial-time algorithms to find the solutions. Meanwhile, an approximation-based channel-aware particle filter (A-CAPF) is proposed to improve the implementation efficiency of state filtering. Simulation results demonstrate the enhanced performance and execution efficiency of the proposed algorithms in the context of the BSC and Rayleigh fading channel.