Variational Bayesian-interacting Multiple Model Tracking Filter with Angle Glint Noise

XU Hong; YUAN Huadong; XIE Wenchong; LIU Weijian; WANG Yongliang

doi:10.11999/JEIT171025

Volume 40 Issue 7

Jul. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(7): 1583-1590

XU Hong, YUAN Huadong, XIE Wenchong, LIU Weijian, WANG Yongliang. Variational Bayesian-interacting Multiple Model Tracking Filter with Angle Glint Noise[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1583-1590. doi: 10.11999/JEIT171025

Citation:

XU Hong, YUAN Huadong, XIE Wenchong, LIU Weijian, WANG Yongliang. Variational Bayesian-interacting Multiple Model Tracking Filter with Angle Glint Noise[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1583-1590. doi: 10.11999/JEIT171025

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Variational Bayesian-interacting Multiple Model Tracking Filter with Angle Glint Noise

doi: 10.11999/JEIT171025

XU Hong^① YUAN Huadong^② XIE Wenchong^② LIU Weijian^② WANG Yongliang^②

Funds:

The National Natural Science Foundation of China (61501505, 61501506)

Received Date: 2017-11-02
Rev Recd Date: 2018-04-03
Publish Date: 2018-07-19

Abstract

Abstract

Research on target tracking with glint noise is important to improve detection performance of sensor, in which the glint noise’s unknown distribution and non-stationary property puzzle researchers for a long time. In order to solve this problem, the tracking theoretical framework of variational Bayesian parameter learning with glint noise is firstly introduced. Then, a novel algorithm called Variational Bayesian-Interacting Multiple Model (VB-IMM) is proposed to estimate the system states as well as the unknown glint noise’s distribution. The proposed algorithm designs a bank of tracking filters in parallel with different measurement noise. Moreover, the algorithm utilizes variational Bayesian method to learn distribution parameters of the glint noise online and feed these parameters back to the tracking filters to revise the filters. In order to validate the performance of this algorithm, comparative experiments are carried out from two aspects of tracking accuracy and computational complexity. Simulation results verify good performance of tracking error and low computational complexity of the proposed algorithm.
- Target tracking、Angle glint noise、Non-stationary、Variational Bayesian、Interacting Multiple Model (IMM),

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References(27)

References

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