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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2023, Vol. 44 ›› Issue (6): 326823-326823.doi: 10.7527/S1000-6893.2022.26823

• Electronics and Electrical Engineering and Control • Previous Articles    

A moving target detector with coherent integration of Bayesian-inferred motion trajectories

Wenbin YANG1,2, Yuebin WANG1,2, Dan LI1,2(), Jianqiu ZHANG1,2   

  1. 1.Key Laboratory of EMW Information,Shanghai 200433,China
    2.Department of Electronic Engineering,School of Information Science and Technology,Fudan University,Shanghai 200433,China
  • Received:2021-12-16 Revised:2021-12-30 Accepted:2022-03-21 Online:2023-03-25 Published:2022-03-30
  • Contact: Dan LI E-mail:lidan@fudan.edu.cn
  • Supported by:
    National Natural Science Foundation of China(11974082)

Abstract:

It has been shown theoretically and practically that coherent integration is an effective method for moving target detection. Unfortunately, its performance is seriously damaged by the unknown range and/or Doppler frequency migrations in a coherent processing interval. To handle such a problem, a moving target detector with the coherent integration of Bayesian-inferred motion trajectories is proposed in this paper. Analyses show that by flipping the range-frequency of the pulse compressed echo signal, the time-frequency signal describing the motion trajectories of the detected targets in the slow-time domain is got by the inverse Fourier transform of the product of the original echo signal and the flipped one. When the time-frequency signal is observed and its phases describing the detected moving target trajectories are regarded as state variables, a state space model for inferring the motion trajectories of multiple targets is given. Based on the inferred target trajectories, the range-frequency-slow-time 2D matched filters are constructed to compensate the unknown range and/or Doppler frequency migrations in an echo signal. Numerical simulation results verify that the proposed method can be applied to high speed/maneuvering targets with unknown complex motion form, and has superior performance than the methods reported in the literature.

Key words: pulse-Doppler radar, Bayesian inference, detection focus, matched filter, range migration, Doppler frequency migration

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