Design of an Acoustic Target Classification System Based on Small-Aperture Microphone Array

The acoustic recognition module of the unattended ground sensor (UGS) system applied in wild environments is faced with the challenge of complicated noise interference. In this paper, a small-aperture microphone array (MA)-based acoustic target classification system, including the system hardware architecture and classification algorithm scheme, is designed as a node-level sensor for the application of UGS in noisy situation. Starting from the analysis of signature of the acoustic signal in wild environments and the merits of small-aperture array in noise reduction, a closely arranged microelectromechanical systems MA is designed to improve the signal quality. Considering the similarities between speaker discrimination and acoustic target recognition, a classification algorithm scheme, consisting of a simplified Mel-frequency cepstrum coefficients and the Gaussian mixture model, is developed to distinguish acoustic targets´ patterns. The proposed classification algorithm has been implemented on embedded system after being tested on training datasets. By combining the small-aperture array and low-complexity classification algorithm, the presented acoustic classification prototype system is portable and efficient. To demonstrate the efficiency of the design, the prototype system is verified in a practical situation with the employment of wheeled and tracked vehicles. Evaluation of the system performances in comparison with other state-of-the-art methods indicates that the proposed design is practical for the acoustic target classification and may be widely adopted by UGS.