Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (3): 8-14.doi: 10.6040/j.issn.1672-3961.0.2019.062

• Machine Learning & Data Mining • Previous Articles     Next Articles

A Chirplet neural network for automatic target recognition

Yifei LI(),Zunhua GUO*()   

  1. School of Mechanical, Electrical & Information Engineering, Shandong University(Weihai), Weihai 264209, Shandong, China
  • Received:2019-02-14 Online:2020-06-20 Published:2020-06-16
  • Contact: Zunhua GUO E-mail:liyifei13@mail.sdu.edu.cn;gritty@sdu.edu.cn
  • Supported by:
    国家自然科学基金资助项目(61401252)

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Abstract:

Aiming at automatic target recognition of aircrafts, a Chirplet neural network for joint feature extraction and target classification was proposed to realize recognition of one-dimensional high resolution range profiles. Based on the multilayer feedforward neural network structure, the Chirplet-atom transform was used to replace the conventional excitation function in the input layer for feature extraction, and the hidden layer and output layer constituted the classifier of the network. The network weights and the parameters of Chirplet-atom node were simultaneously adjusted and optimized to achieve joint feature extraction and target classification. The simulation results of the four types of aircrafts showed that the Chirplet neural network method with the four-feature-parameters had higher recognition rate and anti-noise performance than the time-frequency transformation and Gabor atoms network.

Key words: automatic target recognition, high resolution range profile, Chirplet neural network

CLC Number: 

  • TP183

Fig.1

The structure of Chirplet neural network"

Fig.2

The normalized original range profiles of four targets"

Table 1

The recognition rate with the azimuth angle of 0°~45°"

识别方法 识别率/% 训练耗时/s
YF22 J6 F117 B2
距离像 78.3 86.7 78.7 84.7 6.70
FFT 89.5 94.0 88.1 95.8 9.89
Gabor变换 91.7 95.8 89.6 96.3 7.54
Chirplet变换 92.3 96.9 90.6 96.7 8.59
小波神经网络 94.3 96.8 92.0 96.4 0.96
Gabor原子网络 95.1 99.1 96.7 97.6 1.22
Chirplet神经网络 97.7 99.8 97.0 98.1 2.13

Table 2

The recognition rate with the azimuth angle of 0°~90°"

识别方法 识别率/% 训练耗时/s
YF22 J6 F117 B2
距离像 76.3 82.4 73.8 82.3 8.90
FFT 80.9 89.8 80.5 95.7 12.41
Gabor变换 82.3 88.3 81.6 96.0 9.39
Chirplet变换 82.5 92.6 83.2 95.5 12.86
小波神经网络 83.2 90.7 80.8 94.5 10.57
Gabor原子网络 85.3 93.8 85.2 96.7 23.38
Chirplet神经网络 87.2 95.3 86.5 96.8 29.50

Table 3

The recognition rate with the azimuth angle of 0°~180°"

识别方法 识别率/% 训练耗时/s
YF22 J6 F117 B2
距离像 72.8 80.9 71.0 80.2 13.00
FFT 74.8 83.0 74.2 96.6 21.22
Gabor变换 77.6 86.7 77.5 96.3 14.91
Chirplet变换 78.0 92.0 79.8 96.8 20.07
小波神经网络 79.3 89.8 81.9 94.8 32.34
Gabor原子网络 83.1 91.8 82.5 95.7 137.69
Chirplet神经网络 83.9 92.1 84.7 96.0 156.22

Fig.3

The recognition rates of the Chirplet neural network and Gabor-atoms network with different SNR"

Table 4

The recognition rate with the data of 0°~180° incondition of SNR=5 dB"

识别方法 识别率/%
YF22 J6 F117 B2
距离像 44.9 48.6 49.5 46.0
FFT 55.1 53.4 51.3 69.8
Gabor变换 51.6 52.2 48.0 67.4
Chirplet变换 59.8 51.4 54.1 73.4
小波神经网络 65.7 76.5 71.4 74.7
Gabor原子网络 72.8 74.5 70.1 76.8
Chirplet神经网络 75.5 77.3 74.2 75.1
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