基于IFOA-SA-BP神经网络的雷达信号识别

doi:10.3969/j.issn.1001-506X.2020.12.08

Abstract

Abstract:

In order to improve the recognition rate of radar signal, an improved fruit fly optimization algorithm (IFOA) and simulated annealing (SA) algorithm are combined to optimize the radar signal recognition algorithm of the back propagation (BP) neural network. Firsty, the algorithm extracts the harmonic average fractal box dimension, information dimension and differential approximate entropy feature of radar signal as the three-dimensional features of signal recognition. Then, the optimization step length of fruit fly optimization algorithm (FOA) is improved and the jump coefficient is added to modify the fitness function. At the same time, the three-dimensional search concept is introduced to expand the search range of the fruit fly. And then the acceptance mechanism of solution by the FOA is modified by the SA algorithm. Finally, the fusion algorithm of the IFOA-SA is used to optimize the BP neural network for acquirig the best initial weight and threshold value, and the network is used for radar signal classification and recognition. Compared with the BP and the FOA-BP, the results show that the IFOA-SA-BP can improve the recognition rate of radar signal, which proves the effectiveness of the algorithm.

Key words: radar signal recognition, feature extraction, back propagation (BP) neural network, fruit fly optimization algorithm (FOA), simulated annealing (SA) algorithm

CLC Number:

TN957.51

Jiadong YI, Jie YANG. Radar signal recognition based on IFOA-SA-BP neural network[J]. Systems Engineering and Electronics, 2020, 42(12): 2735-2741.

Figures/Tables 7

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References 29

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函数	算法名称	平均值	方差
f₁(x)	FOA	8.175×10^-5	3.168×10^-11
f₁(x)	IFOA-SA	8.561×10^-6	1.610×10^-12
f₂(x)	FOA	1.236×10^-2	1.179×10^-6
f₂(x)	IFOA-SA	1.652×10^-3	7.743×10^-8

识别方法	时间/s
小波特征	78.65
模糊函数	63.31
本文方法	54.27

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Radar signal recognition based on IFOA-SA-BP neural network

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