Direction Tracking of Multiple Moving Targets Using Quantum Particle Swarm Optimization

College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

Abstract

Based on weighted signal covariance (WSC) matrix and maximum likelihood (ML) estimation, a directionof-arrival (DOA) estimation method of multiple moving targets is designed and named as WSC-ML in the presence of impulse noise. In order to overcome the shortcoming of the multidimensional search cost of maximum likelihood estimation, a novel continuous quantum particle swarm optimization (QPSO) is proposed for this continuous optimization problem. And a tracking method of multiple moving targets in impulsive noise environment is proposed and named as QPSO-WSC-ML. Later, we make use of rank-one updating to update the weighted signal covariance matrix of WSC-ML. Simulation results illustrate the proposed QPSO-WSC-ML method is efficient and robust for the direction tracking of multiple moving targets in the presence of impulse noise.