Systems Engineering and Electronics ›› 2023, Vol. 45 ›› Issue (7): 2043-2050.doi: 10.12305/j.issn.1001-506X.2023.07.14

• Sensors and Signal Processing • Previous Articles     Next Articles

Multi-frequency modulation spectrum fusion enhanced recognition method for pneumatic targets

Qingyuan ZHAO, Zhiqiang ZHAO, Chunmao YE, Yaobing LU   

  1. Beijing Institute of Radio Measurement, Beijing 100854, China
  • Received:2022-08-11 Online:2023-06-30 Published:2023-07-11
  • Contact: Chunmao YE

Abstract:

To solve the problem of unstable recognition performance caused by weak energy of aircraft fretting echo in early warning radar detection process, a multi-frequency modulation spectrum fusion and enhanced recognition method combining sparse constrained non-negative matrix factorization (SCNMF) and integrated extreme learning machine (IELM) is proposed.By analyzing the echo frequency domain characteristics of the micro-motion parts, SCNMF is performed on the modulation spectrum of multi-frequency to achieve pixel-level fusion and obtain the enhanced sparse modulation spectrum, which is input into IELM as a sample to achieve pneumatic target classification. Simulation and measured data verify that the proposed method can effectively integrate multi-frequency micro-motion features, and has the advantages of strong anti-noise ability, fewer training samples and robust recognition performance.

Key words: modulation spectrum, pneumatic target, sparse constrained non-negative matrix factorization, integrated extreme learning machine (IELM)

CLC Number: 

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