闪烁现象下旋翼目标微动参数估计方法

doi:10.12305/j.issn.1001-506X.2022.01.08

Abstract

Abstract:

Inverse Radon transform is often used to estimate the parameters of micro-motion signals because of its high precision and good denoising performance. However, when the micro-motion signal of the rotor target has a flashing phenomenon, the method fails. In order to solve this problem, a method to estimate the micro-motion parameters under the flashing phenomenon is proposed. Firstly, the scattering point model of single-rotor helicopter radar echoes based on linear frequency modulation signals is established, and the micro-motion characteristics of the echoes under the flashing phenomenon are analyzed. Secondly, the denoising network and the deflashing network are trained respectively through the denosing convolutional neural network(DnCNN) structure to eliminate the noise, flashing band and zero band in the time-frequency diagram of rotor target echoes, and the time-frequency diagram of micro-motion signals enhanced by cosine envelope feature is obtained. Finally, the traditional inverse Radon transform uses the ergodic method to search for micro-motion parameters, which has a large amount of calculation. Therefore, the golden section method is adopted to improve the search process and the speed of parameter estimation, and finally complete the estimation of micro-motion parameters of the rotor target. Simulation results verify the feasibility and effectiveness of the proposed method.

Key words: rotor target, micro-Doppler, time-frequency analysis, deep learning, inverse Radon transform, golden section method, parameter estimation

CLC Number:

TN957.51

Yiheng ZHOU, Jun YANG, Saiqiang XIA, Mingjiu LYU. Estimation method of micro-motion parameters for rotor targets under flashing[J]. Systems Engineering and Electronics, 2022, 44(1): 54-63.

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参数	参数值
翼长l/m	5~12之间任意数
旋翼个数I	2~6个之间任意数
转动角频率ω/(rad/s)	6π~14π之间任意数
俯仰角β/(°)	20~60之间任意数
叶片的散射点个数N	70(生成集合S₄时为2)
方位角α/(°)	0

参数	参数值
窗长	100
频率点数	2 048
采样频率/Hz	4 000

类型	叶片数	叶片长度m	旋转角频率rad/s	叶片1的初相角/(°)	叶片2的初相角/(°)	叶片3的初相角/(°)	叶片4的初相角/(°)	俯仰角βrad
图 9(a)	3	8.42	19.24	120	240	360	-	0.41
图 10(a)	2	10.44	23.33	180	360	-	-	0.59
图 11(a)	4	7.63	29.37	90	180	270	360	0.45

类型	实际旋翼长度	叶片1长度估计值	叶片2长度估计值	叶片3长度估计值	叶片4长度估计值	叶片长度估计均值
图 9(c)	8.42	9.27	8.21	7.71	-	8.40
图 10(c)	10.44	11.16	10.27	-	-	10.72
图 11(c)	7.63	7.46	7.08	8.07	7.72	7.58

类型	实际转动角频率(rad/s)	角频率估计值(rad/s)	误差%
图 9(c)	19.24	19.38	0.7
图 10(c)	23.33	23.28	0.2
图 11(c)	29.37	29.58	0.7

Estimation method of micro-motion parameters for rotor targets under flashing

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类型	叶片1的初相角估计值	叶片2的初相角估计值	叶片3的初相角估计值	叶片4的初相角估计值
图 9(c)	130	242	360	-
图 10(c)	180	-2.4	-	-
图 11(c)	97	185	270	360

类型	真实叶片数	估计叶片数
图 9(c)	3	3
图 10(c)	2	2
图 11(c)	4	4

类型	角频率搜索范围	改进逆Radon变换/s	传统逆Radon变换/s
图 9(c)	[2π, 14π]	0.9	111.1
图 10(c)	[2π, 14π]	1.0	111.2
图 11(c)	[2π, 14π]	1.1	112.1

算法类型	参数搜索区间	估计精度	迭代次数
遍历法	[2π, 14π]	0.01	3 770
黄金分割法	[2π, 14π]	0.01	16

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