Multiple Target Vehicles Detection and Classification with Low-Rank Matrix Decomposition

Considerable interest has arisen in the recent years utilizing inexpensive acoustic sensors in the battlefield to perform targets of interest identification and classification. They require no line of sight and provide many capabilities for target detection, bearing estimation, target tracking, classification and identification. In practice, however, many environment noise, time-varying, and uncertainties factors affect their performance in detecting targets of interest reliably and accurate. In this paper, we have proposed a novel feature extraction approach for robust classification and identification of moving target vehicles to reduce those factors. The approach is based on Low Rank Matrix Decomposition. Using Low Rank Matrix Decomposition, dominant features of vehicle acoustic signatures can be extracted appropriately with respect to vehicle operational responses and used for robust identification and classification of target vehicles. The performance of the proposed approach has been evaluated based on a set of experimental acoustic data from multiple vehicle test-runs. It is demonstrated that the approach yields verv promising results to reduce uncertainties associated with classification of target vehicles based on acoustic signatures at different operation speeds in the field.