Adaptive Non-myopic Quantizer Design for Target Tracking in Wireless Sensor Networks

TL;DR

This paper presents an adaptive, non-myopic quantizer design method for target tracking in wireless sensor networks, optimizing multi-step performance using a particle filter-based approach and theoretical decomposition.

Contribution

It introduces a temporally decomposable optimization framework for non-myopic quantizer design using A-CPCRLB and particle filters, enabling adaptive sensor quantization.

Findings

Effective quantizer adaptation improves tracking accuracy.

The approach is computationally feasible with interior-point algorithms.

Simulation confirms enhanced performance over traditional methods.

Abstract

In this paper, we investigate the problem of nonmyopic (multi-step ahead) quantizer design for target tracking using a wireless sensor network. Adopting the alternative conditional posterior Cramer-Rao lower bound (A-CPCRLB) as the optimization metric, we theoretically show that this problem can be temporally decomposed over a certain time window. Based on sequential Monte-Carlo methods for tracking, i.e., particle filters, we design the local quantizer adaptively by solving a particlebased non-linear optimization problem which is well suited for the use of interior-point algorithm and easily embedded in the filtering process. Simulation results are provided to illustrate the effectiveness of our proposed approach.