中文摘要：

针对势平衡多目标多贝努利（cardinality balanced multi—target multi-Bernoulli，CBMeMBer）滤波中的量测信息弱化问题，提出一种改进的多目标多贝努利（improved multi—target multi—Bernoulli，IMeMBer）滤波。该算法通过对漏检目标的多贝努利随机集进行修正，在解决目标数过估问题的同时，避免了CBMeMBer滤波中的量测信息弱化问题。在此基础上，将高斯粒子滤波引入IMeMBer算法中，通过一组高斯粒子近似多贝努利随机集中元素的概率分布，实现被动测角情况下的多目标跟踪。仿真结果表明，所提算法能够以较小的运算代价达到高斯混合粒子劳势估计的概率假设密度滤波相似的跟踪精度，具有良好的工程应用前景。

英文摘要：

To solve the measurement innovation weakening problem in the cardinality balanced multi-target multi-Bernoulli（CBMeMBer） filter, an improved multi-target multi-Bernoulli （MeMBer） filter is proposed by modifying the legacy rather than the measurement updated tracks parameters. Then, to provide a closed-form solution to the nonlinear problem occurred in the passive bearings-only multi-target tracking system, a set of Gaussian particles is employed to approximate the distributions of the multi-Bernoulli random finite set. The simulation results show that the proposed algorithm can get nearly the same tracking accuracy as that of the Gaussian mixture particle-cardinalized probability hypothesis density filter with the much lower computational cost, implying good application prospects.