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Probability hypothesis density filter based on strong tracking MIE for multiple maneuvering target tracking

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Abstract

Taking into account the difficulties of multiple maneuvering target tracking due to the unknown target number and the uncertain acceleration, a novel multiple maneuvering target tracking algorithm based on the Probability Hypothesis Density (PHD) filter and Modified Input Estimation (MIE) technique is proposed in this paper. First, the unknown acceleration vector is added to the target state to form a new augmented state vector. Then, strong tracking filter multiple fading factors are introduced to the MIE method which can adjust the prediction covariance and the corresponding filter gain at different rates in real time, so that the MIE method can adaptively track high maneuvering targets well. Finally, we combine this adaptive MIE method with the PHD filter, which can effectively track multiple maneuvering targets without much prior information. Simulation results show that the proposed algorithm has a higher tracking precision and a better real-time performance than the conventional maneuvering target tracking algorithms.

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Author information

Authors and Affiliations

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, P. R. China

    Jin-Long Yang

  2. School of Electronic Engineering, Xidian University, Xi’an, 710071, P. R. China

    Hong-Bing Ji & Zhen-Hua Fan

Authors
  1. Jin-Long Yang
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  2. Hong-Bing Ji
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  3. Zhen-Hua Fan
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Corresponding author

Correspondence to Jin-Long Yang.

Additional information

Recommended by Editorial Board member Seul Jung under the direction of Editor Zengqi Sun.

This work was supported by the National Natural Science Foundation of China (No. 60871074).

Jin-Long Yang received his M.S. degree in Circuit and System from Northwest Normal University, China in 2009, and his Ph.D. degree in Pattern Recognition and Intelligent System from Xidian University, China in 2012. He is now an assistant professor of the School of Internet of Things Engineering in Jiangnan University. His research interests include target tracking, information fusion and signal processing.

Hong-Bing Ji received his B.S. degree in Radar Engineering, an M.S. degree in Circuit, Signals and Systems, and a Ph.D. degree in Signal and Information processing, in 1983, 1989 and 1999, respectively, from Xidian University. He is now a professor of the school of Electronic Engineering in Xidian University. His research interests include intelligent information processing, radar targets recognition and classification, weak signal detection and recognition.

Zhen-Hua Fan is currently a Ph.D. candidate at the school of Electronic Engineering, Xidian University. His research interests are target tracking, information fusion and signal processing.

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Yang, JL., Ji, HB. & Fan, ZH. Probability hypothesis density filter based on strong tracking MIE for multiple maneuvering target tracking. Int. J. Control Autom. Syst. 11, 306–316 (2013). https://doi.org/10.1007/s12555-012-0032-2

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  • DOI: https://doi.org/10.1007/s12555-012-0032-2

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