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自适应目标新生强度的SMC-PHD/CPHD 滤波
引用本文:秦岭,黄心汉.自适应目标新生强度的SMC-PHD/CPHD 滤波[J].控制与决策,2016,31(8):1461-1467.
作者姓名:秦岭  黄心汉
作者单位:华中科技大学自动化学院,武汉430074.
基金项目:

国家自然科学基金项目(61370180).

摘    要:

提出一种基于量测驱动的自适应目标新生强度PHD/CPHD 滤波算法. 该算法认为新生目标是不可检测的, 有效地克服了归一化失衡问题; 同时, 基于量测驱动自适应设计目标新生强度, 利用PHD/CPHD 滤波分别递归估计存活目标和新生目标的状态及其数目. 最后, 在序列蒙特卡罗框架下实现了该PHD/CPHD 滤波算法. 算例结果表明, 改进算法可以有效地实时跟踪多个机动目标的状态和数目, 应用前景较好.



关 键 词:

多目标跟踪|概率假设密度滤波|量测驱动|粒子滤波|归一化失衡

收稿时间:2015/7/15 0:00:00
修稿时间:2015/10/27 0:00:00

Adaptive target birth intensity for SMC-PHD/CPHD filtering
QIN Ling HUANG Xin-han.Adaptive target birth intensity for SMC-PHD/CPHD filtering[J].Control and Decision,2016,31(8):1461-1467.
Authors:QIN Ling HUANG Xin-han
Abstract:

The PHD/CPHD filter with the adaptive target birth intensity driven by measurements is proposed. The result that the newborn targets are not always detected can solve the problem of normalized unbalance. The adaptive target birth intensity can be designed based on measurement-driven and the estimated state and number of persistent targets, and the newborn targets are propagated separately by using the PHD/CPHD filter. The SMC implementation of the improved PHD/CPHD filter is described. The numerical simulation results show that the improved algorithms can efficiently and instantaneously estimate the number of targets and their states, and have great application prospection.

Keywords:

multi-target tracking|PHD filter|measurement-driven|particles filter|normalized unbalance

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