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

摘要/Abstract

摘要：

调制谱间隔是气动目标识别的重要特征, 在雷达波形资源受限的情况下存在估计精度差且噪声鲁棒性弱的问题。针对此问题, 利用稀疏迭代协方差谱估计方法进行调制谱超分辨, 根据调制谱谱线等间隔的原理提出用基频组功率累积占比表征基频谱, 进而实现基频的超分辨估计。基于甚高频波段雷达的仿真和实测数据分析证明了该方法抗噪能力强, 且在相参积累时间大于1.5倍微动周期、调制谱折叠、重频参差等场景下均可有效提高调制谱间隔参数估计准确率。

关键词: 调制谱间隔, 稀疏迭代协方差谱估计, 短驻留, 重频参差

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

中图分类号:

TN95

赵庆媛, 赵志强, 叶春茂, 鲁耀兵. 基于调制谱超分辨重构的微多普勒参数估计[J]. 系统工程与电子技术, 2023, 45(2): 407-415.

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.

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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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