Target detection and target type & motion classification: Comparison of feature extraction algorithms

Abstract

This paper addresses sensor network-based surveillance of target detection and target type & motion classification. The performance of target detection and classification could be compromised (e.g., due to high rates of false alarm and misclassification), because of inadequacies of feature extraction from (possibly noisy) sensor data and subsequent pattern classification over the network. A feature extraction algorithm, called symbolic dynamic filtering (SDF), is investigated for solving the target detection & classification problem. In this paper, the performance of SDF is compared with two commonly used feature extractors, namely, Cepstrum and principal component analysis (PCA)). Each of these three feature extractors is executed in conjunction with three well-known pattern classifiers, namely, k-nearest neighbor (k-NN), support vector machine (SVM), and sparse representation classification (SRC). Results of numerical simulation are presented based on a dynamic model of target maneuvering and passive sonar sensing in the ocean environment. These results show that SDF has a consistently superior performance for all tasks - target detection and target type & motion classification.