Particle Filter Algorithm for DOA Tracking of Maneuvering Targets

An improved particle filter(PF) algorithm is proposed to address the problems of tracking precision descend, bad real-time performance and large error against multiple targets tracking due to the direction-of-arrival(DOA) of maneuvering targets changing rapidly. According to the model of array signal processing and constant velocity(CV) model, the state equation and measure equation are built as a state space model to track time-varying DOA of maneuvering target and extended it to multiple targets tracking. Then an improved likelihood function is proposed to improve the performance of traditional DOA estimate real-time dynamic tracking. The modified likelihood function is derived from MUSIC(multiple signal classification) algorithm spectral function. Simulation results show that the proposed algorithm is superior to the traditional subspace tracking algorithms and standard particle filter algorithm through the root mean square error(RMSE) and probability of convergence(PROC) comparisons, improves the performance of multiple DOAs tracking for crossing trajectories and has less tracking error, fast rate of convergence, as well as higher resistance to SNR(signal-to-noise ratio) and robustness.