There are many advantages of using acoustic sensor arrays to perform targets of interest identification and classification in the battlefield. They are low cost and have relatively low power consumption. They require no line of sight and provide many capabilities for target detection, bearing estimation, target tracking, classification and identification. Furthermore, they can provide cueing for other sensors and multiple acoustic sensors responses can be combined and triangulated to localize an energy source target in the field. 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 Decomposition based Lp norm. Using Low Rank Decomposition based L1 norm where p = 1, 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 significant improvement results over our earlier vehicle classification technique based on Singular Value Decomposition (SVD) and reduces uncertainties associated with classification of target vehicles based on acoustic signatures at different operation speeds in the field.