作　　者： ; ; ;

机构地区： 深圳大学信息工程学院ATR国防科技重点实验室

出　　处： 《电子与信息学报》 2009年第4期844-847,共4页

摘　　要： 针对被动传感器阵列中的机动目标跟踪问题,该文提出了一种基于多模Rao-Blackwellized粒子滤波的机动目标跟踪新方法。算法首先基于Rao-Blackwellization理论将机动目标跟踪问题划分为模型选择和目标跟踪两个子问题;采用多模Rao-Blackwellized粒子滤波对目标运动模型进行选择,扩展Kalman滤波对目标进行更新,有效降低了抽样粒子状态维数,节省了计算时间;最后,建立了被动传感器阵列的非线性观测模型。实验结果表明,提出方法可以有效地对目标模型进行选择,算法的跟踪性能及稳定性要好于交互多模型(IMM)方法。 In this paper, a new Multiple Model Rao-Blackwellized Particle Filter （MMRBPF） based algorithm is proposed for maneuvering target tracking in passive sensor array. The advantage of the proposed approach is that the Rao-Blackwellization allows the algorithm to be partitioned into target tracking and model selection sub-problems, where the target tracking can be solved by the extend Kalman filter, and the model selection by multiple model Rao-Blackwellized particle filter. The analytical relationship between target state and model is exploited to improve the efficiency and accuracy of the proposed algorithm. Finally, a nonlinear measurement model of multiple passive sensors is founded. The simulation results show that the proposed algorithm results in more accurate tracking than the IMM （Interacting Multiple Model） method.

关 键 词： 机动目标跟踪 被动传感器阵列 粒子滤波

领　　域： [自动化与计算机技术] [自动化与计算机技术]