Target Angle Tracking Algorithm of Bistatic MIMO Radar with Low Signal-to-Noise Ratio

ZHANG Zheng-yan; ZHANG Jian-yun; ZHENG Zhi-dong; LI Xiao-bo

doi:10.3969/j.issn.0372-2112.2019.12.005

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Target Angle Tracking Algorithm of Bistatic MIMO Radar with Low Signal-to-Noise Ratio

更新时间：2025-07-16

- Target Angle Tracking Algorithm of Bistatic MIMO Radar with Low Signal-to-Noise Ratio
- Acta Electronica Sinica Vol. 47, Issue 12, Pages: 2480-2487(2019)
- 作者机构：
  
  1. 75775部队,广东,广州,510010
  2. 国防科技大学电子对抗学院,安徽,合肥,230037
  3. 32802部队,北京,100191
  4. 75775部队,广东,广州,510010
  5. 国防科技大学电子对抗学院,安徽,合肥,230037
  6. 32802部队,北京,100191
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61671453, No.61201379);Natural Science Foundation of Anhui Province (No.1608085MF123)
- DOI：10.3969/j.issn.0372-2112.2019.12.005
  CLC： TN958
- Published Online：25 December 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
ZHANG Zheng-yan, ZHANG Jian-yun, ZHENG Zhi-dong, et al. Target Angle Tracking Algorithm of Bistatic MIMO Radar with Low Signal-to-Noise Ratio[J]. Acta Electronica Sinica, 2019, 47(12): 2480-2487.
DOI：

ZHANG Zheng-yan, ZHANG Jian-yun, ZHENG Zhi-dong, et al. Target Angle Tracking Algorithm of Bistatic MIMO Radar with Low Signal-to-Noise Ratio[J]. Acta Electronica Sinica, 2019, 47(12): 2480-2487. DOI： 10.3969/j.issn.0372-2112.2019.12.005.

摘要

研究了低信噪比时双基地MIMO雷达目标跟踪问题，提出了一种基于改进AAJD（Adaptive Asymmetric Joint Diagonalization）的目标跟踪算法.首先，对AAJD算法进行改进，得到与特征值作用相同的变量，从而找出大特征值变量对应的特征矢量，解决了低信噪比时AAJD算法信号子空间扩展问题.其次，在非稳定跟踪状态时消除特征值变量误差积累的影响，得到更加准确的信号子空间，并对ESPRIT算法进行改进，实现收发角度的配对和相邻时刻角度的自动关联.仿真结果表明改进AAJD算法低信噪比时能够实现角度跟踪，且收敛速度和稳定性能明显优于AAJD算法.

Abstract

The target tracking problem of bistatic MIMO radar under low SNR is studied

and a target tracking algorithm based on the improved AAJD (Adaptive Asymmetric Joint Diagonalization) is proposed. Firstly

the AAJD algorithm is improved to obtain the variable as the eigenvalue and the criterion of selecting the feature vector. The eigenvalue variables are used to find the eigenvectors corresponding to the large eigenvalue variables. And the problem of signal subspace expansion in AAJD algorithm is solved at low SNR. Secondly

the influence of the accumulation of the eigenvalue variables error is eliminated in the unsteady tracking state. The obtained signal subspace is more accurate. Since the estimated eigenvectors order is random at each time

the ESPRIT algorithm is improved to achieve the automatic pairing of transceiver angle of the same moment and the automatic association of the angle of the adjacent moment. The simulation results show that the improved AAJD algorithm can realize the angle tracking with low signal to noise ratio

and the convergence speed and stability performance are significantly better than AAJD algorithm.

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