Feature-Aided Localization and Tracking of Ground Vehicles Using Passive Acoustic Sensor Networks

Tracking of a moving ground target using acoustic signals obtained from a passive sensor network is a difficult problem as the signals are contaminated by wind noise and are hampered by road conditions, terrain and multipath, etc., and are not deterministic. Multiple target tracking becomes even more challenging, especially when some of the vehicles are light (wheeled) and some are heavy (e. g., tracked vehicles like tanks). In such cases the stronger acoustic signals from the heavy vehicles can mask those from the light vehicles, leading to poor detection of such targets. Acoustic passive sensor arrays obtain direction of arrival (DoA) angle estimates of such emitters from the received signals. The full position estimates of targets, obtained following the association of the DoA angle estimates from least three sensor arrays, are used for target tracking. However, because of the particular challenges encountered in multiple ground vehicle tracking, this association is not always reliable and thus, target tracking using such full position measurements only is difficult and it can lead to lost tracks. In this paper we propose a new feature-aided tracking (FAT) algorithm to augment the existing target tracking algorithms which use only kinematic measurements, in order to improve the tracking performance. We present a novel DoA detection technique followed by frequency domain feature extraction from real data. The techniques are developed based on real data sets and tested on real data based on a field experiment.