Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (1): 24-30.doi: 10.23919/JSEE.2024.000025


Robust adaptive radar beamforming based on iterative training sample selection using kurtosis of generalized inner product statistics

Jing TIAN1(), Wei ZHANG2,*()   

  1. 1 School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
    2 National Key Laboratory of Electromagnetic Space Security, Chengdu 610036, China
  • Received:2023-11-13 Accepted:2024-02-17 Online:2024-02-18 Published:2024-03-05
  • Contact: Wei ZHANG;
  • About author:
    TIAN Jing was born in 1984. She received her B.S. and Ph.D. degrees in electrical engineering from Xidian University, Xi’an, China, and Beijing Institute of Technology, Beijing, China, in 2006 and 2014, respectively. She is a professor with Beijing Institute of Technology. Her research interests include moving-target detection, parameter estimation, and imaging. E-mail:

    ZHANG Wei was born in 1986. He received his Ph.D. degree in signal and information processing in 2014 from Beijing Institute of Technology, Beijing, China. From September 2011 to March 2013, he was a visiting researcher with the Communications Research Group, Department of Electronic and Electrical Engineering, University of Sheffifield, UK. He is currently a senior engineer with the National Key Laboratory of Electromagnetic Space Security. His research interests include radar and electronic countermeasure signal processing. E-mail:
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (62371049).


In engineering application, there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval (PRI). Therefore, if the training samples used to calculate the weight vector does not contain the jamming, then the jamming cannot be removed by adaptive spatial filtering. If the weight vector is constantly updated in the range dimension, the training data may contain target echo signals, resulting in signal cancellation effect. To cope with the situation that the training samples are contaminated by target signal, an iterative training sample selection method based on non-homogeneous detector (NHD) is proposed in this paper for updating the weight vector in entire range dimension. The principle is presented, and the validity is proven by simulation results.

Key words: adaptive radar beamforming, training sample selection, non-homogeneous detector, electronic jamming, jamming suppression