Gaussian-Mixture Probability Hypothesis Density Filter for Multiple Extended Targets

doi:10.7652/xjtuxb201404017

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Gaussian-Mixture Probability Hypothesis Density Filter for Multiple Extended Targets

更新时间：2025-04-29

- Gaussian-Mixture Probability Hypothesis Density Filter for Multiple Extended Targets
- Vol. 48, Issue 4, Pages: 95-101(2014)
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201404017
  CLC： TN274
- Online First：10 April 2014，
  
  Published：2014
- 稿件说明：
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Gaussian-Mixture Probability Hypothesis Density Filter for Multiple Extended Targets[J]. 2014, 48(4): 95-101.
DOI：

Gaussian-Mixture Probability Hypothesis Density Filter for Multiple Extended Targets[J]. 2014, 48(4): 95-101. DOI： 10.7652/xjtuxb201404017.

摘要

针对多扩展目标跟踪中状态信息难以估计的问题

提出了一种可以估计扩展目标运动状态和形状信息的多扩展目标高斯混合概率假设密度(RHM-GMPHD)滤波器。首先利用描述凸星形扩展目标量测源分布的随机超曲面模型和传感器量测方程

建立扩展目标运动状态及形状信息与量测之间关系的伪量测函数; 然后结合扩展目标状态预报信息

推导了扩展目标状态更新方程

递推地对扩展目标运动状态及形状信息进行估计跟踪。此外

还建立了Jaccard距离来度量RHM-GMPHD滤波器对目标形状的估计性能。与联合概率数据关联(JPDA)滤波器和GMPHD滤波器相比

RHM-GMPHD滤波器不仅可以估计凸星形扩展目标的形状信息

并能有效提高对目标数和运动状态的估计精度。仿真实验表明

RHM-GMPHD滤波器对质心估计的均方根误差分别约为JPDA和GMPHD滤波器的1/3和1/2

对目标数的估计接近真实值

对形状估计的Jaccard距离一般小于0.2。

Abstract

A multiple extended-target Gaussian-mixture probability hypothesis density(RHM-GMPHD)filter

which provides the kinematic state and the extension state of extended targets

is proposed to address the difficultly estimated extension state. The pseudo-measurement likelihood function describing the relationship between kinematic state and extension state of extended target and measurements is constructed via the random hypersurface model(RHM)for convex-star extended target and sensor measurement function. Then the predicted state is considered

the update of extend target filter is derived to recursively estimate the kinematic state and extension state for extended targets. Moreover

the Jaccard distance is presented to evaluate the performance of the estimate extension state. Compared with the joint probabilistic data association(JPDA)and GMPHD filter

RHM-GMPHD provides the extension state and enhances the precision of the estimate number and the estimate kinematic state. Simulations indicate that the root-mean-square error of centroid from RHM-GMPHD gets 1/3 of that from JPDA or 1/2 of that from GMPHD. The estimation number of extended targets approaches the true value

and Jaccard distance gets usually less than 0.2.

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references

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