Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (11): 7-10.doi: 10.16180/j.cnki.issn1007-7820.2020.11.002

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Radar Pulse Compression Method Based on LASSO

SHAO Yu'e1,WANG Jianlai2,ZHOU Shenghua3,LIU Hongwei3,ZHANG Yuehong4   

  1. 1. School of Electronic Engineering,Xidian University,Xi’an 710071,China
    2. Department of Tactical Weapons,First Aerospace Institute, Beijing 100076,China
    3. State Key Laboratory of Radar Signal Processing, Xi’an 710071,China
    4. PLA Air Force Xi’an Flight Academy,Xi’an 710071,China
  • Received:2019-08-21 Online:2020-11-15 Published:2020-11-27
  • Supported by:
    National Science Fund for Distinguished Young Scholars(61525105);Foreign Scholars in University Research and Teaching Programs(111 Project B18039);Fundamental Research Funds for the Central Universities(JB180215);Cheung Kong Scholars and Innovative Research Team in University the National Natural Science Foundation of China(61601340)


The anti-jamming performance of radar is an important indicator to measure the pros and cons of a radar, which directly determines the performance of radar combat. Common anti-interference measures include sidelobe cancellation, pulse compression, moving target detection, constant false alarm processing. In this paper, a LASSO-based impulse compression anti-jamming measure is proposed. The method use the characteristics of sparsity of LASSO regression, and combines radar echo signals to set matching dictionary. Then, the data set is constructed by using redundant predictors. The LASSO model is constructed by cross validation, and predictors are identified to achieve the target detection. Compared with the pulse compression method, these simulation results show that the LASSO algorithm can obtain better target resolution without considering the influence of side lobes, and the target detection effect is better under the condition of smaller SNR.

Key words: target detection, LFM signal, pulse compression, LASSO, matching dictionary, linear regression, feature extraction

CLC Number: 

  • TN956