Evaluating Particle Filtering for RSS-Based Target Localization under Varying Noise Levels and Sensor Geometries

TL;DR

This paper systematically evaluates particle filtering for RSS-based target localization, demonstrating its superior accuracy over trilateration especially under challenging sensor arrangements and high noise levels.

Contribution

It introduces a particle filtering approach tailored for RSS-based localization and compares its performance with traditional trilateration across various sensor geometries and noise conditions.

Findings

Particle filtering outperforms trilateration in accuracy.

Performance degrades with increased noise but remains better than trilateration.

Unfavorable sensor geometries significantly impact localization accuracy.

Abstract

Target localization is a critical task in various applications, such as search and rescue, surveillance, and wireless sensor networks. When a target emits a radio frequency (RF) signal, spatially distributed sensors can collect signal measurements to estimate the target's location. Among various measurement modalities, received signal strength (RSS) is particularly attractive due to its low cost, low power consumption, and ease of deployment. While particle filtering has previously been applied to RSS-based target localization, few studies have systematically analyzed its performance under varying sensor geometries and RSS noise levels. This paper addresses this gap by designing and evaluating a particle filtering algorithm for localizing a stationary target. The proposed method is compared with a conventional RSS-based trilateration approach across different sensor configurations and noise conditions. Simulation results indicate that particle filtering provides more accurate target localization than trilateration, particularly in scenarios with unfavorable sensor geometries and high RSS noise.