New Perspectives on Multiple Source Localization in Wireless Sensor Networks

TL;DR

This paper introduces a novel Sequential Monte Carlo algorithm for localizing multiple sources in wireless sensor networks using quantized data, providing improved accuracy and analysis tools.

Contribution

It develops a new SMC sampler methodology for multiple source localization in WSNs with unknown sources and quantized data, advancing prior importance sampling techniques.

Findings

The proposed SMC algorithm outperforms classical importance sampling in accuracy.

The method effectively estimates the number and characteristics of sources.

PCRB analysis validates the accuracy and impact of quantization.

Abstract

In this paper we address the challenging problem of multiple source localization in Wireless Sensor Networks (WSN). We develop an efficient statistical algorithm, based on the novel application of Sequential Monte Carlo (SMC) sampler methodology, that is able to deal with an unknown number of sources given quantized data obtained at the fusion center from different sensors with imperfect wireless channels. We also derive the Posterior Cram\'er-Rao Bound (PCRB) of the source location estimate. The PCRB is used to analyze the accuracy of the proposed SMC sampler algorithm and the impact that quantization has on the accuracy of location estimates of the sources. Extensive experiments show that the benefits of the proposed scheme in terms of the accuracy of the estimation method that are required for model selection (i.e., the number of sources) and the estimation of the source characteristics compared to the classical importance sampling method.