Decision Fusion and Micro-Doppler Effects in Moving Sonar Target Recognition

Abstract

This paper proposes a method for underwater target recognition based on micro-Doppler effects (called STR_MD) using a majority voting ensemble classifier weighted with particle swarm optimization (PSO) (called MV-PSO). The micro-Doppler effect refers to amplitude/phase modulation of the received signal by rotating parts of a target such as propellers. Since different targets’ geometric and physical properties differ, their micro-Doppler signature is different. This inconsistency can be considered an effective issue (especially in the frequency domain) for sonar target recognition. To demonstrate the effectiveness of the proposed method, both simulated and practical micro-Doppler data are produced and applied to the designed STR_MD. Also, MV-PSO with six well-known basic classifiers, k-nearest neighbors (k-NN), Naive Bayes (NB), decision tree (DT), MLP_NN, support vector machine (SVM), and random forest (RF), has been used to evaluate the performance of the proposed method. This ensemble classifier assigns an instance to a class that most base classifiers agree on. However, basic classifiers in a set seldom work just as well. Therefore, in this case, one strategy is to weigh each classification depending on its performance using PSO. The performance parameters measured are the recognition score, reliability, and processing time. The simulation results showed that the correct recognition rate, reliability, and processing time for the simulated data at SNR = 5 dB and 10° viewing angle were 98.50, 98.89, and 9.81 s, respectively, and for the practical dataset with RPM = 1200, 100, 100, and 4.43, respectively. Thus, MV-PSO has a more encouraging performance in STR_MD for simulated and practical micro-Doppler sonar datasets.