Maneuvering Target Tracking Algorithm Based on the Adaptive Augmented State Interracting Multiple Model

Hong XU; Wenchong XIE; Huadong YUAN; Keqing DUAN; Yongliang WANG

doi:10.11999/JEIT190516

Volume 42 Issue 11

Nov. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(11): 2749-2755

Hong XU, Wenchong XIE, Huadong YUAN, Keqing DUAN, Yongliang WANG. Maneuvering Target Tracking Algorithm Based on the Adaptive Augmented State Interracting Multiple Model[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2749-2755. doi: 10.11999/JEIT190516

Citation:

Hong XU, Wenchong XIE, Huadong YUAN, Keqing DUAN, Yongliang WANG. Maneuvering Target Tracking Algorithm Based on the Adaptive Augmented State Interracting Multiple Model[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2749-2755. doi: 10.11999/JEIT190516

Citation:

Hong XU, Wenchong XIE, Huadong YUAN, Keqing DUAN, Yongliang WANG. Maneuvering Target Tracking Algorithm Based on the Adaptive Augmented State Interracting Multiple Model[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2749-2755. doi: 10.11999/JEIT190516

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Maneuvering Target Tracking Algorithm Based on the Adaptive Augmented State Interracting Multiple Model

doi: 10.11999/JEIT190516

1.
College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China
2.
Air Force Early Warning Academy, Wuhan 430019, China
3.
School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510006, China

Funds: The National Natural Science Foundation of China (61871397)

Received Date: 2019-07-10
Rev Recd Date: 2020-02-28

Available Online: 2020-09-01

Publish Date: 2020-11-16

Abstract

Abstract

The existing Augmented State-Interracting Multiple Model (AS-IMM) algorithm suffers from the problem that it relies on the prior information of the covariance matrix of the measurement noise. When the prior information is unavailable or inaccurate, the tracking performance of AS-IMM will be degraded. In order to overcome this problem, a novel adaptive Bayesian Variational Augmented State-Interracting Multiple Model (VB-AS-IMM) algorithm is proposed. Firstly, the variational Bayesian inference probabilistic model of the augmented state and the covariance matrix of the measurement noise for the jump Markovarian system is presented. Secondly, the probabilistic model is proven to be non-conjugated. Finally, by introducing a novel post processing method, the suboptimal solution to calculate the joint posterior distribution is proposed. The proposed algorithm can estimate the unknown covariance matrix of the measurement noise online, thus it is more robust and has higher adaptability. Simulation result verifies good performance of the proposed algorithm.
- Maneuvering target tracking,
- Interracting multiple model,
- Augmented state,
- Variational Bayesian,
- Adaptive filtering

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References(19)

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