基于FRFT的雷达信号chirp基稀疏特征提取 及分选

doi:10.7527/S1000-6893.2013.0045

Abstract

Abstract:

Feature analysis is the basis of radar signal sorting and identifying, and feature extraction of a new system radar signals through sparse decomposition is a new research topic. This paper uses the fractional Fourier transform kernel function as the basic chirp function of sparse decomposition to make up chirp functions with similar parameters into a function family for extracting sparse components, and derive the sparse decomposition formula with a matching pursuit in the fractional Fourier domain. Then a characteristic parameter sequence is formed consisting of chirp-based sparse components’ chirp-ratio and initial frequency, and the radar signal pulses are divided into five classes for sorting and identifying. Simulation analysis proves the validity of the derived conclusion, and results show that the linear or curved time-frequency characteristics of radar signals still have 95% correct sorting probability when the SNR is -3 dB, sampling frequency is 500 MHz, observed time is 2 μs, and the chirp-ratio is no more than 100 MHz/μs.

Key words: fractional Fourier transform, sparse decomposition, signal sorting, chirp function, feature extraction

CLC Number:

HUANG Yu, LIU Feng, WANG Zezhong, XIANG Chongwen. Chirp Function Sparse Feature Extraction and Sorting of Radar Signals Based on FRFT[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013, 34(2): 393-400.

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Chirp Function Sparse Feature Extraction and Sorting of Radar Signals Based on FRFT

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