一种改进的高斯逆威沙特概率假设密度扩展目标跟踪算法

李文娟; 吕靖; 顾红; 苏卫民; 马超; 杨建超

doi:10.11999/JEIT170883

一种改进的高斯逆威沙特概率假设密度扩展目标跟踪算法

doi: 10.11999/JEIT170883

基金项目:

国家自然科学基金(61471198, 61671246)，江苏省自然科学基金(BK20160847, BK20170855)

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- 被引次数: 0
出版历程
- 收稿日期: 2017-09-19
- 修回日期: 2018-03-16
- 刊出日期: 2018-06-19

Improved Gaussian Inverse Wishart Probability Hypothesis Density for Extended Target Tracking

Funds:

The National Natural Science Foundation of China (61471198, 61671246), The Natural Science Foundation of Jiangsu Province (BK20160847, BK20170855)

摘要

摘要: 假设扩展目标(ET)的扩展和量测数目分别为椭圆和泊松模型，高斯逆威沙特概率假设密度(GIW-PHD)能够估计扩展目标的运动和扩展状态。然而，该滤波器对空间邻近目标的数目、非椭圆目标和受到遮挡目标的扩展估计不够准确。针对这些问题，该文提出一种改进的GIW-PHD。首先，假设目标扩展为一个相同尺寸的参考椭圆，通过设计新的散射矩阵得到改进的随机矩阵(RM)方法。然后，将改进的RM方法与假设量测数目服从多伯努利分布的ET-PHD结合，得到改进的GIW-PHD滤波器。仿真和实验结果表明，与传统GIW-PHD相比，改进的GIW- PHD估计的目标数目和量测数目较多，扩展较大的椭圆和非椭圆目标的扩展更准确。
- 扩展目标跟踪 /
- 高斯逆威沙特概率假设密度 /
- 随机矩阵 /
- 多伯努利分布
Abstract: Assumed that extension and measurement number of Extended Targets (ET) are respectively modeled as ellipse and Poisson, a Gaussian Inverse Wishart Probability Hypothesis Density (GIW-PHD) filter can estimate kinematic and extension states. However, for the number of spatially close targets and the extensions of non-ellipsoidal and occluded targets, the results estimated by this filter are not accurate enough. In view of these problems, an improved GIW-PHD filter is proposed in this paper. Firstly, assumed that target extension is modeled as a reference ellipse of the same size, a modified Random Matrix (RM) method is obtained by devising a new scatter matrix. Then, combining the improved RM method with the ET-PHD based on a measurement number multi-Bernoulli model, the improved GIW-PHD filter is obtained. Simulated and experimental results show that, compared with the traditional GIW-PHD, the improved GIW-PHD filter can obtain more accurate estimates in target number and the extensions of ellipsoidal and non-ellipsoidal targets with large measurement number and extensions.
- Extended target tracking /
- Gaussian Inverse Wishart Probability Hypothesis Density (GIW-PHD) /
- Random Matrix (RM) /
- Multi-Bernoulli distribution

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参考文献(18)

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