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Dec. 2022
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Rui Zhang, Xinyu Zhang, Shenghua Zhou, Xiaojun Peng. Distributed Radar Target Tracking with Low Communication Cost[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2022, 31(6): 595-604. doi: 10.15918/j.jbit1004-0579.2022.129
Citation: Rui Zhang, Xinyu Zhang, Shenghua Zhou, Xiaojun Peng. Distributed Radar Target Tracking with Low Communication Cost[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2022, 31(6): 595-604.doi:10.15918/j.jbit1004-0579.2022.129
shu

Distributed Radar Target Tracking with Low Communication Cost

doi:10.15918/j.jbit1004-0579.2022.129
  • 1.

    National Laboratory of Radar Signal Processing Xidian University, Xi’an 710071, China

  • 2.

    the Xi’an Electronic Engineering Research Institute, Xi’an 710100, China

Funds:This work was supported in part by the National Laboratory of Radar Signal Processing Xidian Univrsity, Xi’an 710071, China.
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  • Author Bio:

    Rui Zhangreceived the B.E. degree from Xi’an University of Technology in 2012. He received the M.E. degree from Xi’an Electronic Engineering Research Institute in 2016. He is currently pursuing the Ph.D. degree in Xidian University, Xi’an, China. His research interests include multi-target tracking, and data compression in radar system

    Xinyu Zhangreceived the B.E. degree in remote sensing science and technology from the Xidian Univrsity, Xi’an, China in 2020. She is currently pursuing the M.E. degree in information and communication engineering at Xidian Univrsity. Her research interests include multi-target tracking, and data compression in radar system

    Shenghua Zhouis currently a professor and doctoral supervisor at Xidian Univrsity, China. He is mainly engaged in radar networking, MIMO radar related research, and he has published more than 130 academic papers, who is authorized more than 60 patents

    Xiaojun Pengreceived the Ph.D. degree in precision instrument control from Tsinghua University, Beijing, China, in 2005. He is currently a member of the National Laboratory of Radar Signal Processing, Xidian University, Xi’an, China. His current research interests include, but are not limited to, radar signal processing, target detection, and target recognition

  • Corresponding author:shzhou@mail.xidian.edu.cn
  • Received Date:2022-11-12
  • Rev Recd Date:2022-12-01
  • Accepted Date:2022-12-01
  • Publish Date:2022-12-25
    Abstract
  • In distributed radar, most of existing radar networks operate in the tracking fusion mode which combines radar target tracks for a higher positioning accuracy. However, as the filtering covariance matrix indicating positioning accuracy often occupies many bits, the communication cost from local sensors to the fusion is not always sufficiently low for some wireless communication channels. This paper studies how to compress data for distributed tracking fusion algorithms. Based on the K-singular value decomposition (K-SVD) algorithm, a sparse coding algorithm is presented to sparsely represent the filtering covariance matrix. Then the least square quantization (LSQ) algorithm is used to quantize the data according to the statistical characteristics of the sparse coefficients. Quantized results are then coded with an arithmetic coding method which can further compress data. Numerical results indicate that this tracking data compression algorithm drops the communication bandwidth to 4% at the cost of a 16% root mean squared error (RMSE) loss.
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