基于神经网络和散射中心模型的目标参数提取

doi:10.12305/j.issn.1001-506X.2023.01.02

Abstract

Abstract:

Target geometry extraction from radar echoes are often subject to high computational cost, non-linearity, and other difficulties. In this paper, based on convolutional neural network and back propagation neural network, a method is proposed to automatically identify the target pattern and extract the target geometry parameters from the time-frequency image characteristics of scattering center. Since the construction of a neural network requires a large number of training data samples, and the computation of the scattering field of the extended target is very time-consuming, the scattering center model established based on the known target is used in this paper to quickly generate large sample training data, which effectively solves the problem of obtaining training samples. Taking warhead targets as an example, the neural networks are established, and the effectiveness of the proposed method is verified by numerical experiment results.

Key words: neural network, scattering center, time-frequency characteristics, parameter extraction

CLC Number:

TN95

Yuhang LUO, Yanxi CHEN, Kunyi GUO, Xinqing SHENG, Jing MA. Target parameter extraction based on neural network and scattering center model[J]. Systems Engineering and Electronics, 2023, 45(1): 9-14.

Figures/Tables 10

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References 30

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散射中心	类型	成因
LSC1	局部型	尖顶曲面反射
LSC4	局部型	边缘绕射
LSC5	局部型	边缘绕射
DSC1	分布型	单曲面反射
DSC2	分布型
DSC3	分布型
DSC4	分布型	平面反射

各层名称	配置	输出大小
Conv1	7×7, 64, stride2	112×112
Conv2_x	$ 3 \times 3 \text{ maxpool, stride 2}\\\left[\begin{array}{ll}3 \times 3 & 64 \\3 \times 3 & 64\end{array}\right] \times 2$	56×56
Conv3_x	$\left[\begin{array}{ll}3 \times 3 & 128 \\3 \times 3 & 128\end{array}\right] \times 2 $	28×28
Conv4_x	$\left[\begin{array}{ll}3 \times 3 & 256 \\3 \times 3 & 256\end{array}\right] \times 2 $	14×14
Conv5_x	$ \left[\begin{array}{ll}3 \times 3 & 512 \\3 \times 3 & 512\end{array}\right] \times 2$	7×7
	average pool, 1000-d fc, softmax	1×1

雷达频率/GHz	信噪比/dB	识别率/%
1	5	94.8
	10	96.2
	15	97.2
2	5	94.5
	10	96.5
	15	97.0
3	5	94.4
	10	96.6
	15	97.2

几何结构	几何参数	相对误差/%
锥体	h₁	7.59
柱体	b₂、h₂	(11.59, 6.63)
锥台	b₁、h₃	(9.76, 12.91)

样本标号	实际参数/m					预测参数/m
样本标号	h₁	h₂	h₃	b₁	b₂	h₁	h₂	h₃	b₁	b₂
1	1.920	2.140	0.171	0.596	0.440	2.126	2.148	0.230	0.537	0.527
2	2.158	1.826	0.189	0.319	0.501	2.118	2.107	0.177	0.315	0.580
3	1.995	1.585	0.100	0.442	0.317	1.757	1.590	0.101	0.509	0.243
4	1.724	1.716	0.189	0.542	0.480	1.926	1.765	0.149	0.539	0.506
5	1.959	2.139	0.219	0.645	0.683	2.191	2.072	0.214	0.557	0.577
6	2.417	2.387	0.196	0.686	0.584	2.201	2.026	0.232	0.636	0.644
7	1.962	1.887	0.184	0.425	0.559	2.035	1.947	0.207	0.499	0.494
8	2.285	1.798	0.143	0.562	0.627	2.209	1.961	0.161	0.567	0.564
9	1.557	2.038	0.257	0.628	0.552	1.653	1.876	0.256	0.557	0.549
10	2.322	2.202	0.156	0.354	0.674	2.296	2.397	0.187	0.425	0.699

Target parameter extraction based on neural network and scattering center model

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