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中文摘要：
针对粒子概率假设密度（PHD）滤波算法在虚警、漏检情况下,目标状态估计不稳定和目标可观测性较弱的问题,提出了一种基于序贯融合的粒子PHD滤波方法,利用雷达和红外传感器多目标进行融合跟踪.其基本思想是先对红外传感器进行粒子PHD滤波,再将红外传感器滤波结果作为雷达的预测值,然后利用雷达观测的数据进行更新,这样通过雷达和红外传感器交替工作保证目标状态的可观测性,从滤波器输出结果即可得到目标的状态信息.仿真结果表明,在虚警、漏检和密集目标环境下,该方法是有效的和稳健的.
英文摘要：
The problem of target state estimation instability and observability weaker in the presence of false alarms and missed detection was deal with.On the basis of sequential fusion,a particle probability hypothesis density （PHD） filter for multi-sensor multi-target tracking was proposed.Observed data collected from the infrared sensor was estimated with the particle PHD filter.Then the results from the filter were set as the radar predicted value by the radar observations.The multi-target state can be updated to guarantee observing the target state.In this way,the global state is updated at the fusion center.Simulation results show that the proposed algorithm is effective and robust under the false warning,omission and concentrated target environment.
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