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As a typical implementation of the probability hypothesis density (PHD) filter,sequential Monte Carlo PHD (SMC-PHD) is widely employed in highly nonlinear systems.However,the particle impoverishment problem introduced by the resampling step,together with the high computational burden problem,may lead to performance degradation and restrain the use of SMC-PHD filter in practical applications.In this work,a novel SMC-PHD filter based on particle compensation is proposed to solve above problems.Firstly,according to a comprehensive analysis on the particle impoverishment problem,a new particle generating mechanism is developed to compensate the particles.Then,all the particles are integrated into the SMC-PHD filter framework.Simulation results demonstrate that,in comparison with the SMC-PHD filter,proposed PC-SMC-PHD filter is capable of overcoming the particle impoverishment problem,as well as improving the processing rate for a certain tracking accuracy in different scenarios.