Multiple target tracking and classification improvement using data fusion at node level using acoustic signals

摘要

Target tracking and classification using passive acoustic signals is difficult at best as the signals are contaminated by wind noise, multi-path effects, road conditions, and are generally not deterministic. In addition, microphone characteristics, such as sensitivity, vary with the weather conditions. The problem is further compounded if there are multiple targets, especially if some are measured with higher signal-to-noise ratios (SNRs) than the others and they share spectral information. At the U. S. Army Research Laboratory we have conducted several field experiments with a convoy of two, three, four and five vehicles traveling on different road surfaces, namely gravel, asphalt, and dirt roads. The largest convoy is comprised of two tracked vehicles and three wheeled vehicles. Two of the wheeled vehicles are heavy trucks and one is a light vehicle. We used a super-resolution direction-of-arrival estimator, specifically the minimum variance distortionless response, to compute the bearings of the targets. In order to classify the targets, we modeled the acoustic signals emanated from the targets as a set of coupled harmonics, which are related to the engine-firing rate, and subsequently used a multivariate Gaussian classifier. Independent of the classifier, we find tracking of wheeled vehicles to be intermittent as the signals from vehicles with high SNR dominate the much quieter wheeled vehicles. We used several fusion techniques to combine tracking and classification results to improve final tracking and classification estimates. We will present the improvements (or losses) made in tracking and classification of all targets. Although improvements in the estimates for tracked vehicles are not noteworthy, significant improvements are seen in the case of wheeled vehicles. We will present the fusion algorithm used.