基于HRRP特征统计特性的雷达目标识别方法

doi:10.12305/j.issn.1001-506X.2026.07.09

Abstract

Abstract:

In modern battlefield environments, radar target recognition faces dual challenges: the difficulty in acquiring datasets for advanced targets and the high demand for interpretability for decision-making. To address these issues, a fuzzy probabilistic pattern recognition method, based on the statistical properties of high-resolution range profiles features, is proposed by integrating the high-precision advantages of statistical pattern recognition with the noise-resistant characteristics of fuzzy pattern recognition. In terms of statistical properties, the χ² distribution is extended to a generalized p-norm, allowing for a comprehensive investigation and analysis of the statistical properties of the χ^p distribution to achieve higher recognition accuracy. In terms of fuzzy attributes, a membership function is utilized, enabling the proposed method to maintain good recognition performance even under low signal-to-noise ratio conditions. Data generation for advanced foreign fighter aircraft is conducted using the electromagnetic simulation software. The experimental results indicate that the proposed method outperforms existing pattern recognition technologies in both identification accuracy and noise robustness.

Key words: radar target recognition, high resolution range profile （HRRP）, pattern recognition, feature extraction, fuzzy probability

CLC Number:

TN 957.52

Jian DONG, Junjie ZOU, Feifei HOU, Chengwang XIAO. Radar target recognition method based on statistical properties of HRRP features[J]. Systems Engineering and Electronics, 2026, 48(7): 2206-2217.

Figures/Tables 13

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类别	机型	飞机长度	翼展宽度	机身高度
国外先进战斗机	美国F-22“猛禽”战斗机	18.90	13.56	5.08
国外先进战斗机	美国F-35“闪电II”战斗机	15.67	10.70	4.33
国外先进轰炸机	美国B-2“幽灵”轰炸机	21.00	52.40	5.18
国外先进轰炸机	美国B-21“突袭者”轰炸机	18.30	46.00	5.00
国外先进无人机	美国RQ-4“全球鹰”无人机	13.50	35.40	4.60
国外先进无人机	美国MQ-9“死神”无人机	10.97	20.10	3.81
库外目标（战斗机）	俄罗斯Su-57战斗机	19.80	13.98	4.70
库外目标（轰炸机）	俄罗斯Tu-160轰炸机	54.10	55.70	13.10

方法类型	方法来源	具体策略	准确率	召回率	精确率	F1分数
统计模式识别	[4]	KNN	91.73	90.16	98.57	94.18
统计模式识别	[8]	SVM	93.25	92.88	97.64	95.20
模糊模式识别	[10]	模糊集方法	75.94	99.53	76.83	86.72
深度学习模式识别	[19]	CNN	94.12	93.45	98.23	95.78
深度学习模式识别	[20]	Transformer	94.87	94.21	99.12	96.60
模糊概率模式识别	所提方法	基于χ^p分布的方法	97.85	97.14	100.00	98.55

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Radar target recognition method based on statistical properties of HRRP features

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