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

摘要/Abstract

摘要：

在现代实际战场环境中，雷达目标识别面临着双重挑战：一是先进目标的数据集获取难度较大，二是对决策的可解释性要求较高。为此，结合统计模式识别的高精度优势与模糊模式识别的抗噪特性，提出一种基于高分辨距离像特征统计特性的模糊概率模式识别方法。在统计特性方面，对χ²分布进行广义p-范拓展，深入研究并分析了χ^p分布的统计特性，实现了更高的识别精度。在模糊属性方面，采用隶属度函数使所提方法在低信噪比条件下仍能保持良好的识别性能。通过电磁仿真软件对国外先进战机进行数据生成，实验结果表明，所提方法在识别精度与噪声鲁棒性上均优于多种现有模式识别技术。

关键词: 雷达目标识别, 高分辨距离像, 模式识别, 特征提取, 模糊概率

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

中图分类号:

TN 957.52

董健, 邹俊杰, 侯斐斐, 肖程望. 基于HRRP特征统计特性的雷达目标识别方法[J]. 系统工程与电子技术, 2026, 48(7): 2206-2217.

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.

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参考文献 34

1	ZHANG F, BI X Y, ZHANG Z X, et al. HIFR-Net: a HRRP-infrared fusion recognition network capable of handling modality missing and multisource data misalignment[J]. IEEE Sensors Journal, 2025, 25 (3): 5769- 5781.
2	JIANG W, WANG Y P, LI Y, et al. Radar target characterization and deep learning in radar automatic target recognition: a review[J]. Remote Sensing, 2023, 15 (15): 3742. doi: 10.3390/rs15153742
3	LI S, LI W T, HUANG P J, et al. MTBC: masked vision transformer and brown distance covariance classifier for cross-domain few-shot HRRP recognition[J]. IEEE Sensors Journal, 2025, 25 (9): 16440- 16454.
4	柴晶, 刘宏伟, 保铮. 加权KNN分类器在HRRP库外目标拒判中的应用[J]. 系统工程与电子技术, 2010, 32 (4): 718- 723.
	CHAI J, LIU H W, BAO Z. Application of a weighted KNN classifier for HRRP out-of-database target rejection[J]. Systems Engineering and Electronics, 2010, 32 (4): 718- 723.
5	DU L, WANG P H, LIU H W, et al. Bayesian spatiotemporal multitask learning for radar HRRP target recognition[J]. IEEE Trans. on Signal Processing, 2011, 59 (7): 3182- 3196. doi: 10.1109/TSP.2011.2141664
6	DU L, LIU H W, WANG P H, et al. Noise robust radar HRRP target recognition based on multitask factor analysis with small training data size[J]. IEEE Trans. on Signal Processing, 2012, 60 (7): 3546- 3559. doi: 10.1109/TSP.2012.2191965
7	DU L, HE H, ZHAO L, et al. Noise robust radar HRRP target recognition based on scatterer matching algorithm[J]. IEEE Sensors Journal, 2015, 16 (6): 1743- 1753.
8	查海刚, 齐向阳, 范怀涛. 基于SVM的小样本不均衡HRRP舰船目标分类方法[J]. 现代电子技术, 2024, 47 (15): 109- 114.
	ZHA H G, QI X Y, FAN H T. Small-sample imbalanced HRRP ship target classification method based on SVM[J]. Modern Electronic Technology, 2024, 47 (15): 109- 114.
9	LIU Y C, LONG T, ZHANG L, et al. SDHC: joint semantic-data guided hierarchical classification for fine-grained HRRP target recognition[J]. IEEE Trans. on Aerospace and Electronic Systems, 2024, 60 (4): 3993- 4009. doi: 10.1109/TAES.2024.3373378
10	吴强, 姜礼平, 季傲. 基于模糊集和D-S证据理论的空中作战目标识别[J]. 指挥控制与仿真, 2015, 37 (4): 54- 58. doi: 10.3969/j.issn.1673-3819.2015.04.012
	WU Q, JIANG L P, JI A. Aircraft target identification based on fuzzy sets and D-S evidence theory in air operation[J]. Command Control & Simulation, 2015, 37 (4): 54- 58. doi: 10.3969/j.issn.1673-3819.2015.04.012
11	LIU X, YANG J, LU J, et al. Evaluation method for HRRP imaging quality based on intuitionistic interval fuzzy set[J]. SN Applied Sciences, 2020, 2, 79. doi: 10.1007/s42452-019-1875-4
12	LIU Y F, ZHANG Y J. Application of weighted fuzzy C-means algorithm in high resolution range profile target detection[C]//Proc. of the International Conference on Electronic Information Engineering and Computer Communication, 2022: 307−312.
13	陈士超, 魏靖彪, 范俊, 等. 基于判别性无穷模糊受限玻尔兹曼机模型的HRRP序列识别[J]. 兵工学报, 2024, 45 (S1): 43- 50. doi: 10.12382/bgxb.2024.0552
	CHEN S C, WEI J B, FAN J, et al. Research on HRRP sequences recognition based on discriminative infinite fuzzy restricted Boltzmann machine model[J]. Acta Armamentarii, 2024, 45 (S1): 43- 50. doi: 10.12382/bgxb.2024.0552
14	LUNDÉN J, KOIVUNEN V. Deep learning for HRRP-based target recognition in multistatic radar systems[C]//Proc. of the IEEE Radar Conference, 2016.
15	FENG B, CHEN B, LIU H W. Radar HRRP target recognition with deep networks[J]. Pattern Recognition, 2017, 61, 379- 393. doi: 10.1016/j.patcog.2016.08.012
16	PAN M, LIU A L, YU Y Z, et al. Radar HRRP target recognition model based on a stacked CNN-Bi-RNN with attention mechanism[J]. IEEE Trans. on Geoscience and Remote Sensing, 2021, 60, 5100814.
17	HE J X, WANG X D, SONG Y F, et al. A multi-scale radar HRRP target recognition method based on pyramid depthwise separable convolution network[C]//Proc. of the 7th International Conference on Image, Vision and Computing, 2022: 579−585.
18	CHEN J, DU L, GUO G B, et al. Target-attentional CNN for radar automatic target recognition with HRRP[J]. Signal Processing, 2022, 196, 108497. doi: 10.1016/j.sigpro.2022.108497
19	吴文静, 王中训, 洪梓榕, 等. 基于多域特征融合的HRRP目标识别方法[J]. 舰船电子工程, 2024, 44 (11): 60- 64，75. doi: 10.3969/j.issn.1672-9730.2024.11.013
	WU W J, WANG Z X, HONG Z R, et al. HRRP target recognition method based on multi domain feature fusion[J]. Ship Electronic Engineering, 2024, 44 (11): 60- 64，75. doi: 10.3969/j.issn.1672-9730.2024.11.013
20	但波, 宋伟健, 薛永华, 等. 基于Transformer的舰船高分辨距离像目标识别[J]. 舰船电子工程, 2024, 44 (7): 25- 28. doi: 10.3969/j.issn.1672-9730.2024.07.007
	DAN B, SONG W J, XUE Y H, et al. Target recognition of ship high resolution range profile based on Transformer[J]. Ship Electronic Engineering, 2024, 44 (7): 25- 28. doi: 10.3969/j.issn.1672-9730.2024.07.007
21	SRUTI S, GIRIDHAR K. Emerging trends in radar: perspective on future integrated multistatic radar[J]. IEEE Aerospace and Electronic Systems Magazine, 2025, 40 (6): 108- 113.
22	翟应虎, 李祖光, 张旭, 等. 基于模糊概率理论的地层流体精确识别方法[J]. 中国石油大学学报（自然科学版）, 2010, 34 (1): 70- 72.
	ZHAI Y H, LI Z G, ZHANG X, et al. Exact identification approach to volcanics formation fluid based on fuzzy probability theory[J]. Journal of China University of Petroleum （Edition of Natural Science）, 2010, 34 (1): 70- 72.
23	孙海燕, 胡宏昌. P-范分布及其抽样分布[J]. 应用概率统计, 2003, 19 (4): 424- 428. doi: 10.3969/j.issn.1001-4268.2003.04.012
	SUN H Y, HU H C. P-value distribution and its sampling distribution[J]. Chinese Journal of Applied Probability and Statistics, 2003, 19 (4): 424- 428. doi: 10.3969/j.issn.1001-4268.2003.04.012
24	RAN G T, LIU J, LI C J, et al. Fuzzy-model-based asynchronous fault detection for Markov jump systems with partially unknown transition probabilities: an adaptive event-triggered approach[J]. IEEE Trans. on Fuzzy Systems, 2022, 30 (11): 4679- 4689. doi: 10.1109/TFUZZ.2022.3156701
25	LIU W H, MA Q, LU Y, et al. Adaptive fixed-time event-triggered fuzzy control for time-delay nonlinear systems with disturbances and quantization[J]. IEEE Trans. on Fuzzy Systems, 2023, 31 (8): 2848- 2860. doi: 10.1109/TFUZZ.2023.3238410
26	LIU X D, WANG L, BAI X R. End-to-end radar HRRP target recognition based on integrated denoising and recognition network[J]. Remote Sensing, 2022, 14 (20): 5254. doi: 10.3390/rs14205254
27	ZHANG X W, WEI Y, WANG W Y. Patch-wise autoencoder based on transformer for radar high-resolution range profile target recognition[J]. IEEE Sensors Journal, 2023, 23 (23): 29406- 29414. doi: 10.1109/JSEN.2023.3327552
28	DENG J, SU F L. Deep hybrid fusion network for inverse synthetic aperture radar ship target recognition using multi-domain high-resolution range profile data[J]. Remote Sensing, 2024, 16 (19): 3701.
29	TONG S C, ZHOU H D. Finite-time adaptive fuzzy event-triggered output-feedback containment control for nonlinear multiagent systems with input saturation[J]. IEEE Trans. on Fuzzy Systems, 2023, 31 (9): 3135- 3147. doi: 10.1109/TFUZZ.2023.3245222
30	CHEN L F, HU P H, PAN Z L, et al. Large language models can achieve explainable and training-free one-shot HRRP ATR[EB/OL]. [2025-04-20]. https://arxiv.org/pdf2506.02465.
31	LI W T, TIAN B, MA J, et al. OSFSM: a systematic open set framework for radar automatic target recognition using HRRP[J/OL]. IEEE Trans. on Aerospace and Electronic Systems, 2025. DOI: 10.1109/TAES.2025.3575045.
32	LIU Q, ZHANG X Y, LIU Y X. Target-aspect-guided neural network with geometric constraints for imbalanced radar target recognition[J]. IEEE Trans. on Aerospace and Electronic Systems, 2025.
33	ABDULLAHI M B, ALI M H. Additively manufactured metastructure design for broadband radar absorption[J]. Beni-Suef University Journal of Basic and Applied Sciences, 2021, 10, 24. doi: 10.1186/s43088-020-00091-7
34	SALEHI A, IMANI M, ZAIMBASHI A, et al. Learning and model-based approaches for radar target detection[J]. IEEE Trans. on Cognitive Communications and Networking, 2024, 10 (5): 1817- 1830. doi: 10.1109/TCCN.2024.3391327

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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