基于IRT的大动态反射系数微动目标检测算法

doi:10.3969/j.issn.1001-506X.2020.09.08

摘要/Abstract

摘要：

当雷达回波信号中同时存在强弱差异较大的微动目标时,弱反射目标分量常常会被噪声和强反射目标所掩盖,导致弱反射目标难以被检测。为解决该问题,提出一种大动态反射系数微动目标检测算法。首先该算法利用逆Radon变换(inverse Radon transform, IRT)来获取强微动分量的参数估计值,然后通过逐次消去强分量来增强弱分量,从而使得弱分量更容易被检测。本文算法能够实现反射系数差异较大的多个微动目标的检测,同时能够准确估计出其微动参数。仿真实验证明了本文算法的正确性，同时对本文算法的性能进行了分析。

关键词: 微多普勒效应, 逆Radon变换, 正弦频率调制, 时频分析, 反射系数, 掩盖现象

Abstract:

When there are micro-motion targets with large differences in the radar echo signals, the components of the weak reflection targets are often masked by the strong reflection targets and noise, which makes the weak reflection targets difficult to be detected. To solve the problem, a detection algorithm of large dynamic reflection coefficient micro-motion target is proposed. Firstly, the inverse Radon transform (IRT) algorithm is used to obtain the parameter estimation value of the strongest reflection components, and then the weak components are enhanced by removing the strong componens one by one, which makes the weak components easier to be detected. This algorithm applies to detect micro-motion targets with different reflection coefficients and has high precision of parameter estimation. The simulation experiments show that the validity of the algorithm. At the same time, the performance of the algorithm is analyzed.

Key words: micro-Doppler effect, inverse Radon transform (IRT), sinusoidal frequency modulation (SFM), time-frequency analyze, reflection coefficient, masking phenomenon

中图分类号:

TN957

周阳, 毕大平, 沈爱国. 基于IRT的大动态反射系数微动目标检测算法[J]. 系统工程与电子技术, 2020, 42(9): 1935-1944.

Yang ZHOU, Daping BI, Aiguo SHEN. Micro-motion target detection algorithm of IRT based large dynamic reflection coefficient[J]. Systems Engineering and Electronics, 2020, 42(9): 1935-1944.

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目标	参数	理论值	估计值
T1	f_a⁽¹⁾/Hz	1	1.01
	A_ω⁽¹⁾/Hz	100	99.26
	φ₀⁽¹⁾/(°)	120	120.1
	r₀⁽¹⁾/m	0.244	0.239
T2	f_a⁽²⁾/Hz	1.4	1.44
	A_ω⁽²⁾/Hz	60.4	60.1
	φ₀⁽²⁾/(°)	210	210.6
	r₀⁽²⁾/m	0.105	0.102
T3	f_a⁽³⁾/Hz	1.2	1.18
	A_ω⁽³⁾/Hz	77.2	78.45
	φ₀⁽³⁾/(°)	30	29.9
	r₀⁽³⁾/m	0.157	0.162
T4	f_a⁽⁴⁾/Hz	1	1.02
	A_ω⁽⁴⁾/Hz	90.7	89.29
	φ₀⁽⁴⁾/(°)	60	60.7
	r₀⁽⁴⁾/m	0.221	0.213

信号分量	SPWVD	获取每个α对应的M(α)	消去强分量	总时间
原始信号	1.68	2.03	0.41	4.12
第1次消去残留信号	1.53	2.27	0.46	4.26
第2次消去残留信号	1.46	1.97	0.39	3.82
第3次消去残留信号	1.42	1.98	0.43	3.83

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