Improved Gaussian Inverse Wishart Probability Hypothesis Density for Extended Target Tracking

LI Wenjuan; Lü Jing; GU Hong; SU Weimin; MA Chao; YANG Jianchao

doi:10.11999/JEIT170883

Volume 40 Issue 6

May 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(6): 1279-1286

LI Wenjuan, Lü Jing, GU Hong, SU Weimin, MA Chao, YANG Jianchao. Improved Gaussian Inverse Wishart Probability Hypothesis Density for Extended Target Tracking[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1279-1286. doi: 10.11999/JEIT170883

Citation:

LI Wenjuan, Lü Jing, GU Hong, SU Weimin, MA Chao, YANG Jianchao. Improved Gaussian Inverse Wishart Probability Hypothesis Density for Extended Target Tracking[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1279-1286. doi: 10.11999/JEIT170883

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Improved Gaussian Inverse Wishart Probability Hypothesis Density for Extended Target Tracking

doi: 10.11999/JEIT170883

Funds:

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

Received Date: 2017-09-19
Rev Recd Date: 2018-03-16
Publish Date: 2018-06-19

Abstract

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.

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References(18)

References

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