基于调制谱超分辨重构的微多普勒参数估计

doi:10.12305/j.issn.1001-506X.2023.02.11

Abstract

Abstract:

Modulation spectrum interval is an important feature of aerodynamic target recognition. However, its estimation encounters the problem of poor estimation accuracy and weak noise robustness in cases of limited radar waveform resources. In order to solve this problem, sparse iterative covariance spectrum estimation algorithm is introduced to carry out super-resolution of the modulation spectrum, and the power accumulation ratio of the fundamental frequency group is proposed to characterize the fundamental frequency according to the generation theory of modulation spectrum interval, and then the super-resolution estimation of the fundamental frequency is realized. Based on the analysis of the simulation and measured data of very high frequency band radar, it is proved that the proposed method has strong noise robustness, and can effectively improve the accuracy of modulation spectrum interval parameter estimation when the phase-coherent accumulation time is greater than 1.5 times of micro-motion period, and with modulation spectrum folding and pulse repetition frequency stagger.

Key words: modulation spectrum interval, sparse iterative covariance-based estimation, short dwell, staggered pulse repetition frequency

CLC Number:

TN95

Qingyuan ZHAO, Zhiqiang ZHAO, Chunmao YE, Yaobing LU. Micro-Doppler parameter estimation based on super-resolution modulation spectrum reconstruction[J]. Systems Engineering and Electronics, 2023, 45(2): 407-415.

Figures/Tables 12

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References 33

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目标	桨叶数	L₁/m	L₂/m	w_r/rps	基频/Hz	v_tip/(m·s^-1)	f_max/Hz
1	4	0.69	5.965	6	24	224.8	374.6
2	5	1.20	10.965	3	15	206.6	344.3
3	4	0.00	1.800	20	80	226.1	376.8
4	38	0.38	0.600	58	2 204	218.5	364.2

对比项目	PRT/ms
对比项目	1	2	3	4
折叠程度	0.748	1.496	2.992	5.984
SPICE+倒谱/%	91	70	15	5
SPICE+基频谱/%	99	98	94	92

场景	方法	随机森林	SVM
场景1	传统方法^[14]	94	92.5
场景1	本文方法	98.5	97
场景2	传统方法^[14]	89.5	88
场景2	本文方法	99	99
场景3	传统方法^[14]	94.5	95
场景3	本文方法	99.5	99.5
场景1、场景2交叉	传统方法^[14]	71.5	70
场景1、场景2交叉	本文方法	99	84.5
场景1、场景3交叉	传统方法^[14]	70	67.5
场景1、场景3交叉	本文方法	99.5	92.3

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Micro-Doppler parameter estimation based on super-resolution modulation spectrum reconstruction

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