Acoustic target classification using distributed sensor arrays

Target classification using distributed sensor arrays remains a challenging problem due to the non-stationarity of target signatures, large geographical area coverage of sensor arrays, and the requirements of time-critical and reliable information delivery. In this paper, we develop an algorithm to derive effective and stable features from both the frequency and the time-frequency domains of the acoustic signals. A modified data fusion algorithm for distributed sensor arrays is also developed in order to integrate the classification results from different sensors and provide fault-tolerance. By using data fusion, the accuracy of the classification can be increased by as many as 50%.