Volume 11 Issue 3
Jun.  2022
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Article Navigation >  Journal of Radars  > 2022  >  11(3): 459-468
DA Kai, YANG Ye, ZHU Yongfeng, et al. Multitarget tracking using distributed radar with partially overlapping fields of views[J]. Journal of Radars, 2022, 11(3): 459–468. doi: 10.12000/JR21183
Citation: DA Kai, YANG Ye, ZHU Yongfeng, et al. Multitarget tracking using distributed radar with partially overlapping fields of views[J]. Journal of Radars, 2022, 11(3): 459–468. doi: 10.12000/JR21183
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Multitarget Tracking Using Distributed Radar with Partially Overlapping Fields of Views

DOI: 10.12000/JR21183

  • National Key Laboratory of Science and Technology on ATR, College of Electronic Science and technology, National University of Defense Technology, Changsha 410073, China

Funds:  The National Ministries Foundation
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  • Corresponding author: ZHU Yongfeng, zoyofo@163.com
  • Received Date: 2021-11-19
  • Accepted Date: 2022-01-25
  • Rev Recd Date: 2022-01-21
    • Available Online: 2022-01-29
  • Publish Date: 2022-03-14
    Abstract
  • The Fields of Views (FoVs) of radars in a distributed network partially overlap due to detecting capability, waveform design, and antenna orientation constraints, resulting in observed discrepancies between radars and a significant obstacle to future information fusion. In this paper, we propose a distributed multitarget tracking method under the scene of partially overlapping radar FoVs, based on the Gaussian Mixture Cardinalized Probability Hypothesis Density (GM-CPHD) filter. First, we employ the product of the multitarget densities to split the PHD functions and find the part that characterizes the information of the targets commonly observed by multiple radars. Then, a standard distributed fusion (arithmetic average or geometric average fusion) acts on the splitting information to improve tracking performance, and a compensation fusion acts on the remaining information to expand the observation FoV. The proposed method does not require prior knowledge of the radar’s FoV and may adapt to the scene of distributed multitarget tracking while the FoVs are unknown. Simulations are provided to verify the effectiveness of the proposed approach under the scene of unknown and time-varying radar FoVs, and show that the proposed method has better performance than that of the cluster method based on Gaussian matching.

     

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