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ZHOU Enji, WEN Gongjian, SONG Haibo, et al. Target parameter and time-frequency bias estimation method based on multitemporal measurement data for distributed MIMO radar[J]. Journal of Radars, in press. doi: 10.12000/JR25201
Citation: ZHOU Enji, WEN Gongjian, SONG Haibo, et al. Target parameter and time-frequency bias estimation method based on multitemporal measurement data for distributed MIMO radar[J]. Journal of Radars, in press. doi: 10.12000/JR25201
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Target Parameter and Time-frequency Bias Estimation Method Based on Multitemporal Measurement Data for Distributed MIMO Radar

DOI: 10.12000/JR25201 CSTR: 32380.14.JR25201
  • 1.

    College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

  • 2.

    College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China

Funds:  The National Natural Science Foundation of China (62501622), Natural Science Foundation of Hunan Province (2023JJ40680)
More Information
  • Corresponding author: WEN Gongjian, wengongjian@sina.com
  • Received Date: 2025-10-11
  • Rev Recd Date: 2025-12-17
    • Available Online: 2025-12-24
    Abstract
  • This study addresses time-frequency synchronization errors in distributed Multiple-Input Multiple-Output (MIMO) radar systems and proposes a joint estimation method for target parameters and system time-frequency biases based on multitemporal measurement data. The method overcomes the limitations of traditional approaches that rely on singletemporal measurement data and direct-path signals, enabling high-accuracy joint parameter estimation through multiepoch data fusion without requiring direct-path information. The proposed method adopts a two-step strategy that combines a closed-form solution with iterative optimization. First, a closed-form solution is derived within a two-stage weighted least-squares framework using only the first- and last-epoch observations to obtain initial estimates of the target position, velocity, and auxiliary variables. This stage explicitly models second-order error terms and optimizes the construction of the weighting matrix, significantly improving accuracy and robustness under high-error conditions. Second, using the closed-form estimates as initialization, a maximum likelihood-maximum a posteriori objective function is formulated based on the full multi-epoch measurement data, and a trust-region iterative optimization method is applied to refine the estimates and recover the time-frequency bias parameters. Simulation results show that the proposed method outperforms existing approaches across various error levels and geometric configurations, significantly enhancing the accuracy and robustness of target localization, velocity estimation, and time-frequency bias estimation. These results demonstrate strong theoretical significance and promising practical application potential.

     

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