A Track-Before-Detect Algorithm for UWB Radar Sensor Networks

TL;DR

This paper presents a track-before-detect algorithm for UWB radar sensor networks that improves localization and tracking of targets in cluttered environments by jointly processing waveform data to generate and select optimal trajectories.

Contribution

It introduces a novel track-before-detect technique that leverages spatial and temporal data for enhanced target tracking in UWB radar networks, validated through simulations and real experiments.

Findings

Average localization error of 41.9 cm in outdoor environment

Localization errors of 25.4 cm and 19.7 cm for two targets

Detection rate of 88.75% with no false alarms

Abstract

Precise localization and tracking of moving non-collaborative persons and objects using a network of ultra-wideband (UWB) radar nodes has been shown to represent a practical and effective approach. In UWB radar sensor networks (RSNs), existence of strong clutter, weak target echoes, and closely spaced targets are obstacles to achieving a satisfactory tracking performance. Using a track-before-detect (TBD) approach, the waveform obtained by each node during a time period are jointly processed. Both spatial information and temporal relationship between measurements are exploited in generating all possible candidate trajectories and only the best trajectories are selected as the outcome. The effectiveness of the developed TBD technique for UWB RSNs is confirmed by numerical simulations and by two experimental results, both carried out with actual UWB signals. In the first experiment, a human target is tracked by a monostatic radar network with an average localization error of 41.9 cm with no false alarm trajectory in a cluttered outdoor environment. In the second experiment, two targets are detected by multistatic radar network with localization errors of 25.4 cm and 19.7 cm, and detection rate of the two targets is 88.75%, and no false alarm trajectory.