基于时间错序量测的多目标多传感器跟踪算法研究

在多传感器多目标跟踪系统中,经常有来自同一目标的量测到达融合中心时存在时间先后顺序上的混乱,被称为时间错序量测(Oosm);通常,现有的跟踪算法都是假设理想目标的观测值不混乱;现实中,可能错过的目标探测随意混乱,因而,滤波器不得不处理起因未知的量测,那么针对顺序量测的传统滤波器,例如KF,在此就不能直接使用;通过基于一些特殊矩阵非单一假设的经济存储和能效估计介绍了全局最优Oosm刷新算法,并结合概率数据关联PDA到Oosm刷新算法中;仿真结果显示Oosm刷新的PDA滤波器在性能上优于忽略Oosm的PDA滤波器,还就关于杂波中多目标跟踪如何通过JPDA结合oosm刷新算法展开讨论.

Research of Algorithm with Out-of-Sequence Measurements in Multi-target Multi-sensor Tracking

In multi-target multi-sensor tracking systems,sensor measurements from the same target can arrive out of sequence at the central processor,called the out-of-sequence measurement(Oosm).In general,the existing algorithms assume perfect target detection and no clutter in the received measurements.The real world has,however,possible missed target detection and random clutter and thus the filter has to handle the measurement origin uncertainty.As a result,classical filters for orderly measurements,such as Kalman filter,cannot be used directly.we present the OOSM updated algorithm globally optimal with economic storage and efficient computation based on the nonsingularity assumption of some special matrices,incorporating the probabilistic data association(PDA) into the OOSM updated algorithm.Simulation results show that the PDA with OOSM update has better performance than just ignoring those Oosm.A discussion was given concerning how to incorporate the OOSM update algorithms with the JPDA for multi-target tracking in clutter.