低信噪比下面向时空特征融合的雷达目标智能检测方法

doi:10.12305/j.issn.1001-506X.2026.05.08

Abstract

Abstract:

To address the problem of significant performance degradation of traditional radar target detection methods under low signal-to-noise ratio （SNR） conditions, a multi-frame deep learning detection method based on feature enhancement and fusion strategies is proposed. The method utilizes multi-scale convolutional layers to extract abstract representations of radar frames. By mining spatio-temporal features from both current and historical coherent processing interval （CPI） echo data, it reconstructs and predicts moving targets across consecutive CPIs. Experiments conducted on radar-measured data from civil aviation aircraft tracking demonstrate that, at a detection SNR of 6 dB, the proposed method achieves over 50% improvement in detection probability compared to traditional constant false alarm rate （CFAR） and track-before-detect （TBD） methods under the same false alarm conditions, providing a solution for radar target detection.

Key words: deep learning, multi-frame detection, feature fusion, low signal-to-noise ratio （SNR）

CLC Number:

TN 958.2

Yuanguang CHEN, Xiaokai LIU, Siyao XIAO, Shunsheng ZHANG, Hongyuan CUI, Xiaoying CHEN, Ying LIU. Radar intelligent target detection method using spatio-temporal feature fusion under low signal-to-noise ratio[J]. Systems Engineering and Electronics, 2026, 48(5): 1523-1531.

Figures/Tables 12

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Table 1

Table 2

Fig.9

Fig.10

References 31

1	JIANG W, REN Y H, LIU Y, et al. A method of radar target detection based on convolutional neural network[J]. Neural Computing and Applications, 2021, 33 (16): 9835- 9847. doi: 10.1007/s00521-021-05753-w
2	DAVEY J S, RUTTEN G M, CHEUNG B. A comparison of detection performance for several track-before-detect algorithms[J]. EURASIP Journal on Advances in Signal Processing, 2008, 2008 (1): 428036- 10.
3	GANDHI P P, KASSAM S A. Analysis of CFAR processors in nonhomogeneous background[J]. IEEE Trans. on Aerospace and Electronic systems, 1988, 24 (4): 427- 445. doi: 10.1109/7.7185
4	BALL J E. Low signal-to-noise ratio radar target detection using linear support vector machines (L-SVM)[C]//Proc. of the IEEE Radar Conference, 2014: 1291−1294.
5	王治飞, 于俊朋, 杨予昊. 基于帧间多维特征的深度学习雷达目标检测技术[J]. 现代雷达, 2022, 44 (12): 48- 54.
	WANG Z F, YU J P, YANG Y H, et al. Radar target detection with multi-frame multi-dimensional features based on deep learning[J]. Modern Radar, 2022, 44 (12): 48- 54.
6	WANG L, TANG J, LIAO Q M. A study on radar target detection based on deep neural networks[J]. IEEE Sensors Letters, 2019, 3 (3): 7000504.
7	DELAMOU M, BAZZI A, CHAFII M, et al. Deep learning-based estimation for multitarget radar detection[C]//Proc. of the IEEE 97th Vehicular Technology Conference, 2023.
8	WANG Y Z, JIANG Z Y, LI Y D, et al. RODNet: a real-time radar object detection network cross-supervised by camera-radar fused object 3D localization[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15 (4): 954- 967. doi: 10.1109/JSTSP.2021.3058895
9	JIANG T Z, ZHUANG L, AN Q, et al. T-RODNet: Transformer for vehicular millimeter-wave radar object detection[J]. IEEE Trans. on Instrumentation and Measurement, 2022, 72, 5003912.
10	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Proc. of the 31st International Conference on Neural Information Processing Systems, 2017, 30, 6000- 6010.
11	陈佳音, 郭山红, 朱海锐, 等. 基于多特征融合的海面目标智能检测算法[J]. 现代雷达, 2024, 46 (12): 24- 30.
	CHEN J Y, GUO S H, ZHU H R, et al. Intelligent detection algorithm for sea surface targets based on multi-feature fusion[J]. Modern Radar, 2024, 46 (12): 24- 30.
12	施端阳, 林强, 胡冰, 等. 基于YOLO的航管一次雷达目标检测方法[J]. 系统工程与电子技术, 2024, 46(01): 143−151.
	DUAN S Y, LIN Q, HU B, et al. Target detection method of primary surveillance radar based on YOLO[J]. Systems Engineering and Electronics, 2023, 46(1): 143−151.
13	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779−788.
14	施赛楠, 姜苏桐. 基于改进1D-AlexNet的海面小目标高维特征检测[J]. 信号处理, 2024, 40 (6): 1098- 1110.
	SHI S N, JIANG S T. High-dimensional feature detection of small sea-surface targets via improved 1D-AlexNet[J]. Journal of Signal Processing, 2024, 40 (6): 1098- 1110.
15	DALBAH Y, LAHOUD J, CHOLAKKAL H. RadarFormer: lightweight and accurate real-time radar object detection model[C]//Proc. of the Scandinavian Conference on Image Analysis, 2023: 341−358.
16	XIAO Y H, LIU Y F, LUAN K, et al. Deep LiDAR-radar-visual fusion for object detection in urban environments[J]. Remote Sensing, 2023, 15 (18): 4433. doi: 10.3390/rs15184433
17	汪翔, 汪育苗, 陈星宇, 等. 基于深度学习的多特征融合海面目标检测方法[J]. 雷达学报, 2023, 13 (3): 554- 564.
	WANG X, WANG Y M, CHEN X Y, et al. Deep learning-based marine target detection method with multiple feature fusion[J]. Journal of Radars, 2023, 13 (3): 554- 564.
18	LUO D Q, ZHU J H. Radar target detection in strong clutter using spatial-temporal U-NET[C]//Proc. of the IEEE 32nd International Workshop on Machine Learning for Signal Processing, 2022.
19	RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C]//Proc. of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015: 234−241.
20	WEN L W, DING J S, XU Z. Multiframe detection of sea-surface small target using deep convolutional neural network[J]. IEEE Trans. on Geoscience and Remote Sensing, 2021, 60, 5107116.
21	DECOURT C, VANRULLEN R, SALLE D, et al. A recurrent CNN for online object detection on raw radar frames[J]. IEEE Trans. on Intelligent Transportation Systems, 2024, 25 (10): 13432- 13441. doi: 10.1109/TITS.2024.3404076
22	WANG K Q, WANG Z Y. A triple-channel network for maritime radar targets detection based on multi-modal features[J]. Remote Sensing, 2024, 16 (24): 4662. doi: 10.3390/rs16244662
23	司凌宇, 强文文, 李港, 等. 基于对比学习的航海雷达目标检测方法[J]. 电子学报, 2023, 51 (7): 1791- 1802. doi: 10.12263/DZXB.20221088
	SI L Y, QIANG W W, LI G, et al. Marine radar object detection method based on contrastive learning[J]. Acta Electronica Sinica, 2023, 51 (7): 1791- 1802. doi: 10.12263/DZXB.20221088
24	HUANG J, YANG X, ZHOU F, et al. A deep learning framework based on improved self-supervised learning for ground-penetrating radar tunnel lining inspection[J]. Computer-Aided Civil and Infrastructure Engineering, 2024, 39 (6): 814- 833. doi: 10.1111/mice.13042
25	CHEN F L, LI Y, ZHAO B, et al. Unsupervised domain adaption on category level by pseudo label for radar target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2025, 22, 3501105.
26	ZHUANG L, JIANG T Z, WANG J H, et al. Effective mmWave radar object detection pretraining based on masked image modeling[J]. IEEE Sensors Journal, 2023, 24 (3): 3999- 4010.
27	GROSSI E, LOPS M, VENTURINO L. A novel dynamic programming algorithm for track-before-detect in radar systems.[J]. IEEE Trans. Signal Processing, 2013, 61 (10): 2608- 2619. doi: 10.1109/TSP.2013.2251338
28	ZHANG Y, YE M, ZHU G Y, et al. FFCA-YOLO for small object detection in remote sensing images[J]. IEEE Trans. on Geoscience and Remote Sensing, 2024, 62, 5611215.
29	YU F, KOLTUN V. Multi-Scale context aggregation by dilated convolutions[EB/OL]. [2025-04-08]. https://arxiv.org/abs/1511.07122.
30	XIAO Y, XU T F, YU X, et al. A lightweight fusion strategy with enhanced interlayer feature correlation for small object detection[J]. IEEE Trans. on Geoscience and Remote Sensing, 2024, 62, 4708011.
31	GIRSHICK R. Fast R-CNN[C]//Proc. of the IEEE International Conference on Computer Vision, 2015: 1440−1448.

检测方法	检测概率
检测方法	SNR=6 dB	SNR=10 dB
CFAR	0.001	0.870
DP-TBD^[27]	0.348	0.925
ECE^[1]	0.005	0.033
DCNN^[20]	0.461	0.939
所提方法	0.883	0.952

检测方法	推理时间
CFAR	3.82
DP-TBD^[27]	7.02
ECE^[1]	0.12
DCNN^[20]	2.83
所提方法	0.36

[1]	Qiongdan HUANG, Lulu LIU, Jiejing HAN, Jiapeng WANG, Shilin KANG. Image super-resolution reconstruction based on differential feature back-projection fusion [J]. Systems Engineering and Electronics, 2026, 48(6): 1809-1818.
[2]	Shengzhi YUAN, Shaolei WANG, Jing LI, Renkai XIAO, Haotian TAN, Jianwei LAI, Jiangtao XU. Fault diagnosis method based on wavelet packet transform and improved residual neural network [J]. Systems Engineering and Electronics, 2026, 48(6): 2031-2041.
[3]	Penghao WANG, Di HE, Mingyi YOU, Xiping GUO. Modulation recognition method based on stochastic resonance and multi-scale features fusion [J]. Systems Engineering and Electronics, 2026, 48(6): 2096-2104.
[4]	Zeying YANG, Zhuo CHEN, Zhiming XU, Xiaofeng AI, Qihua WU, Ling WANG. Forward labeling method for ISAR image components of space targets [J]. Systems Engineering and Electronics, 2026, 48(4): 1165-1173.
[5]	Jiong LIN, Peng GAO, Wenxing HONG. Applications and prospects of physics-informed neural networks in aerospace [J]. Systems Engineering and Electronics, 2026, 48(4): 1264-1282.
[6]	Kun CHENG, Hongtao LEI, Zhixuan LYU. Lightweight unmanned aerial vehicle tracking method of improved YOLOv8 [J]. Systems Engineering and Electronics, 2026, 48(3): 737-750.
[7]	Long ZHANG, Lianhong ZHU, Bo YANG, Zhen LEI, Xuanming FENG. MFA-Net: a multimodal adaptive fusion network for intelligent recognition of anti-ship missiles in complex adversarial environments [J]. Systems Engineering and Electronics, 2026, 48(3): 894-907.
[8]	Hongyu TIAN, Songhu GE, Yu GUO, Zhongpu CUI, Xiao LIANG. Communication interference recognition method based on multi-feature fusion under low interference-to-noise ratio conditions [J]. Systems Engineering and Electronics, 2026, 48(3): 1061-1071.
[9]	Yao WANG, Huiqi XU, Silei CAO, Lei WANG. Detection algorithm of ship critical parts based on deep learning [J]. Systems Engineering and Electronics, 2026, 48(2): 410-421.
[10]	Hengyan LIU, Jun FANG, Qing LING, Wenjun YAN, Keyuan YU, Limin ZHANG. Emitter individual identification method based on 1D-2D-GRU-ResNet [J]. Systems Engineering and Electronics, 2026, 48(2): 727-735.
[11]	Yugang BAO, Haoxiang JIA, Danfeng ZHAO. Single image raindrop removal model based on high-order recursive network [J]. Systems Engineering and Electronics, 2026, 48(1): 12-21.
[12]	Haiqing CHEN, Liuying WANG, Gu LIU, Long WANG, Chaoqun GE, Mengzhou CHEN. Research progress on denoising algorithms for synthetic aperture radar images [J]. Systems Engineering and Electronics, 2026, 48(1): 94-105.
[13]	Kaiming LI, Yuanpeng ZHANG, Ying LUO, Xiaonan DAI. Research progress on radar target recognition of ballistic missile [J]. Systems Engineering and Electronics, 2025, 47(9): 2870-2889.
[14]	Ce JI, Xiangyu MA, Xiaoyu MU, Jiayi ZHAO. TS-GRU-VTA: vehicle channel estimation scheme based on deep learning [J]. Systems Engineering and Electronics, 2025, 47(9): 3093-3098.
[15]	Weihong FU, Wenhong PENG, Naian LIU. SAR image small target detection method with hybrid attention optimization [J]. Systems Engineering and Electronics, 2025, 47(8): 2519-2526.

Radar intelligent target detection method using spatio-temporal feature fusion under low signal-to-noise ratio

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 12

References 31

Related Articles 15

Recommended Articles

Metrics

Comments