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中文摘要：
针对标准粒子滤波算法在机动目标波达方向（direction of arrival，DOA）随时间快速变化导致跟踪精度下降、实时性变差及多目标跟踪误差大等不足的问题，本文提出了一种改进粒子滤波（particle filter，PF）算法。该算法依据阵列信号处理模型和匀速（constant velocity，CV）模型，建立了机动目标跟踪的状态方程和观测方程作为状态空间模型，并在此基础上，借鉴多重信号分类（multiple signal classification，MUSIC）算法谱函数修改了粒子滤波的似然函数，实现了对目标方位的实时动态跟踪。仿真结果表明，与传统子空间类跟踪算法和标准粒子滤波算法相比，本文方法跟踪精度更高，收敛速度更快，抗噪能力及鲁棒性更强，对轨迹交叉的多目标跟踪性能也更优。
英文摘要：
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.
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