贪婪的量测划分机制下的多传感器多机动目标跟踪算法

针对低检测概率下多机动目标的跟踪问题，该文提出一种新的交互式多传感器多目标多伯努利滤波器(IMM-MS-MeMBer)。在IMM-MS-MeMBer滤波器的预测阶段，该文利用当前的量测信息自适应地更新目标的模型概率，并利用更新后的模型概率对目标状态进行混合预测；在IMM-MS-MeMBer滤波器的更新阶段，使用贪婪的多传感器量测划分策略对多传感器量测进行划分，并利用得到的量测划分集合和IMM-MS-MeMBer滤波器对目标的后验概率密度进行更新；除此之外，IMM-MS-MeMBer滤波器能够利用目标的角度和多普勒量测信息同时实现多个机动目标的位置、速度估计。数值实验验证了该文所提IMM-MS-MeMBer滤波器的优越性能。

Multi-sensor Multiple Maneuvering Targets Tracking Algorithm under Greedy Measurement Partitioning Mechanism

A novel method Interacting Multiple Mode Multi-Sensor Multi-target Multi-Bernoulli (IMM-MS-MeMBer) filter to track multiple maneuvering targets in low detection probability scenario is proposed. At the prediction stage of the IMM-MS-MeMBer filter, model probability of the target is adaptively updated by utilizing the current measurement information, and then the mixed prediction of the target state is executed; At the update stage of the IMM-MS-MeMBer filter, the greedy multi-sensor measurement partitioning strategy is employed in measurement partition step, the posterior probability density of the target is updated by using the divided set of measurements and the IMM-MS-MeMBer filter; In addition, the IMM-MS-MeMBer filter utilizes the target angle and Doppler information to realize the simultaneous estimation of the position and speed of multiple maneuvering targets. Numerical experiments verify the superior performance of the IMM-MS-MeMBer filter.