新生目标强度未知的单量测PHD滤波器

摘要

In situations where the targets cannot be detected in the surveillance region,the estimated performance of the adaptive target birth intensity probability hypothesis density (PHD)filter will get worse because of false or low estimate.To overcome this problem,with unknown target birth intensity,a single measurement PHD (PHD-SM)filter and its sequential Monte Carlo (SMC)method are proposed.First,the undetected targets are compensated through de-veloping the one step virtual measurement set.Afterward,according to the single measurement decomposition technique of PHD,the predication and update equations are derived.Finally,a novel multi-target state estimation method is pres-ented.The simulation results show that,when the detection probability PD is small,PHD-SM filter has higher estimation performance.Moreover,the smaller the detection probability,the more significant advantage of estimation performance for PHD-SM filter.%自适应新生目标强度PHD滤波器（PHD-M）在目标漏检时易发生错估或漏估，从而导致滤波器估计性能下降。为解决这一问题，提出了一种新生目标强度未知的单量测（single measurement）PHD滤波器（PHD-SM）并给出了其粒子实现方式。该文首先通过构建一步虚拟量测对漏检目标进行补偿，然后基于单量测PHD分解技术推导了PHD预测和更新公式，最后设计了一种无须聚类操作的多目标状态估计方法。仿真实验表明，在当检测概率PD 较小时，PHD-SM滤波器估计性能优于PHD-M滤波器，且检测概率越小，性能优势越明显。