Vehicle recognition in acoustic sensor networks using multiple kernel sparse representation over learned dictionaries

Sparse representation–based classification and kernel methods have emerged as important methods for pattern recognition. In this work, we study the problem of vehicle recognition using acoustic sensor networks in real-world applications. To improve the recognition accuracy with noise sensor data collected from challenging sensing environments, we develop a new method, called multiple kernel sparse representation–based classification, for vehicle recognition. In the proposed multiple kernel sparse representation–based classification method, acoustic features of vehicles are extracted and mapped into a high-dimensional feature space using a kernel function, which combines multiple kernels to obtain linearly separable samples. To improve the recognition accuracy, we incorporate dictionary learning method K-singular value decomposition into the multiple kernel sparse representation–based classification framework. The vehicle recognition from acoustic sensor network is then formulated into an optimization problem. Our extensive experimental results demonstrate that the proposed multiple kernel sparse representation–based classification method with learned dictionaries outperforms other existing methods in the literature on vehicle recognition from complex acoustic sensor network datasets.