Automatic recognition of sonar targets using feature selection in micro-Doppler signature

Abstract

Currently, the use of intelligent systems for the automatic recognition of targets in the fields of defence and military has increased significantly. The primary advantage of these systems is that they do not need human participation in target recognition processes. This paper uses the particle swarm optimization (PSO) algorithm to select the optimal features in the micro-Doppler signature of sonar targets. The micro-Doppler effect is referred to amplitude/phase modulation on the received signal by rotating parts of a target such as propellers. Since different targets' geometric and physical properties are not the same, their micro-Doppler signature is different. This Inconsistency can be considered a practical issue (especially in the frequency domain) for sonar target recognition. Despite using 128-point fast Fourier transform (FFT) for the feature extraction step, not all extracted features contain helpful information. As a result, PSO selects the most optimum and valuable features. To evaluate the micro-Doppler signature of sonar targets and the effect of feature selection on sonar target recognition, the simplest and most popular machine learning algorithm, k-nearest neighbor (k-NN), is used, which is called k-PSO in this paper because of the use of PSO for feature selection. The parameters measured are the correct recognition rate, reliability rate, and processing time. The simulation results show that k-PSO achieved a 100% correct recognition rate and reliability rate at 19.35 s when using simulated data at a 15 dB signal-to-noise ratio (SNR) angle of 40°. Also, for the experimental dataset obtained from the cavitation tunnel, the correct recognition rate is 98.26%, and the reliability rate is 99.69% at 18.46s. Therefore, the k-PSO has an encouraging performance in automatically recognizing sonar targets when using experimental datasets and for real-world use.