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LIU Qing, ZHANG Hairui, XIE Jian, et al. A moving target tracking method based on adaptive tensor decomposition in bistatic MIMO radar[J]. Journal of Radars, in press. doi: 10.12000/JR25107
Citation: LIU Qing, ZHANG Hairui, XIE Jian, et al. A moving target tracking method based on adaptive tensor decomposition in bistatic MIMO radar[J]. Journal of Radars, in press. doi: 10.12000/JR25107
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A Moving Target Tracking Method Based on Adaptive Tensor Decomposition in Bistatic MIMO Radar

DOI: 10.12000/JR25107 CSTR: 32380.14.JR25107

  • School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China

Funds:  The National Natural Science Foundation of China (62271412), Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University (CX2025077)
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  • Corresponding author: XIE Jian, xiejian@nwpu.edu.cn
  • Received Date: 2025-06-06
  • Rev Recd Date: 2025-08-16
    • Available Online: 2025-08-20
    Abstract
  • Moving target tracking is a fundamental task in bistatic Multiple-Input Multiple-Output (MIMO) radar systems, as it is essential for improving sensing accuracy and real-time adaptability in dynamic environments. This paper proposes a tracking algorithm based on Adaptive Tensor Decomposition (ATD) to address accuracy degradation caused by target dynamics and high-dimensional data coupling. A third-order streaming tensor is first established to model the time-varying, multi-dimensional structure of received signals from moving targets, which jointly incorporates the Direction Of Departure (DOD) and Direction Of Arrival (DOA). A dynamic mapping is then derived from the tensor to characterize the relationship between the target’s spatial state and the factor matrices. Next, a random dimensionality reduction strategy is integrated into the adaptive tensor decomposition, which iteratively updates the factor matrices that contain target state information, thereby enabling real-time and robust tracking of target angles. Finally, numerical simulations are conducted to evaluate the tracking performance of the proposed method. The results demonstrate that it provides continuous and stable tracking of moving targets under low Signal-to-Noise Ratio (SNR) conditions. Compared to classical approaches, the proposed algorithm reduces computational time by one to two orders of magnitude, demonstrating its effectiveness and real-time applicability in complex and dynamic environments.

     

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