A Detection Method of Small Target in Sea Clutter Environment Based on Feature Temporal Sequence

DONG Yunlong; LUO Xiao; DING Hao; WANG Guoqing; LIU Ningbo

doi:10.11999/JEIT240528

Volume 47 Issue 3

Mar. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(3): 707-719

DONG Yunlong, LUO Xiao, DING Hao, WANG Guoqing, LIU Ningbo. A Detection Method of Small Target in Sea Clutter Environment Based on Feature Temporal Sequence[J]. Journal of Electronics & Information Technology, 2025, 47(3): 707-719. doi: 10.11999/JEIT240528

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DONG Yunlong, LUO Xiao, DING Hao, WANG Guoqing, LIU Ningbo. A Detection Method of Small Target in Sea Clutter Environment Based on Feature Temporal Sequence[J]. Journal of Electronics & Information Technology, 2025, 47(3): 707-719. doi: 10.11999/JEIT240528

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A Detection Method of Small Target in Sea Clutter Environment Based on Feature Temporal Sequence

doi: 10.11999/JEIT240528 cstr: 32379.14.JEIT240528

Naval Aviation University, Yantai 264001, China

Funds: The National Natural Science Foundation of China (62388102, 62101583)

Received Date: 2024-06-25
Rev Recd Date: 2025-02-15

Available Online: 2025-02-24

Publish Date: 2025-03-01

Abstract

Abstract

Objective Feature detection has become an effective approach for detecting small targets in sea clutter environments, attracting significant attention and research. Previous studies primarily focused on extracting differential features between targets and clutter from the current pulse frame for detection. Recent methods have integrated temporal information from multiple frames with current frame features, demonstrating improved detection performance. However, these methods rely on fixed-order Auto Regressive (AR) models, which do not effectively adapt to the time-varying nature of sea clutter. Moreover, the use of static weighting algorithms for feature fusion fails to account for clutter characteristics in the current scene, leading to suboptimal utilization of temporal information. To address these issues, this study proposes a feature AR modeling and one-step prediction method based on a model-stable modified Burg algorithm, enabling adaptive pole distribution adjustment and enhancing the accuracy of sea clutter feature prediction. Additionally, a dynamic weighting algorithm is developed by solving multivariable extreme value problems to obtain minimum variance fused features, fully leveraging historical frame temporal information and improving radar target detection performance. Methods This study employs a modified Burg method to predict sea clutter, incorporating a stability factor in the derivation of reflection coefficients to constrain the model’s poles within the unit circle. This enhances model stability, improving its adaptability to the time-varying nature of sea clutter and increasing the accuracy of feature prediction. A dynamic weighting algorithm is introduced to adaptively adjust fusion weights based on data volatility around the current frame by solving a multivariable extremum problem, thereby minimizing the local variance of fused features. Temporal fusion is performed using the features Relative Average Amplitude (RAA), Frequency Peak to Average Ratio (FPAR), and Relative Doppler Peak Height (RDPH) to generate a fused feature. The fused clutter features are then used to construct a three-dimensional convex hull decision region, where target presence is determined by assessing whether the detection unit’s feature point lies within this region. Detection results are compared with commonly used feature detection methods. Additionally, the study evaluates the boundary performance of the proposed method and contrasts it with the traditional energy-domain CFAR method, providing a comprehensive analysis of its usability and effectiveness. Results and Discussions The proposed method achieves the following results: (1) For clutter data, the temporal fusion algorithm reduces data variance by an average of 0.024 5 compared to no temporal fusion and by 0.003 5 compared to the original temporal fusion algorithm. For target data, it reduces data variance by an average of 1.126 6 compared to no temporal fusion and by 0.179 compared to the original temporal fusion algorithm. (2) The Bhattacharyya distance of the proposed temporal fusion algorithm improves by an average of 0.237 3 compared to no temporal fusion and by 0.109 3 compared to the original temporal fusion algorithm. Under VV polarization, the Bhattacharyya distance improves by an average of 0.219 9 compared to no temporal fusion and by 0.090 8 compared to the original temporal fusion algorithm. (3) The proposed method outperforms other feature detectors in detection performance by effectively utilizing temporal information from historical frames, thereby enhancing the echo information used. Compared to energy-domain CFAR methods, it maintains a strong competitive advantage. Conclusions This study presents innovative solutions to two key challenges in existing sea clutter feature modeling and fusion methods. First, to address the time-varying nature of sea clutter features, a model-stable modified Burg method is proposed for Autoregressive (AR) feature modeling. This approach enables adaptive adjustment of model pole distribution, improving the accuracy of one-step sea clutter feature predictions and simplifying model order estimation. Second, to enhance the utilization of inter-frame temporal information during feature fusion, a dynamic weighted fusion algorithm is introduced to integrate predicted and observed features. This method reduces the variance of fused features and fully exploits historical temporal information. Validation using the IPIX dataset and the shared dataset from the Naval Aeronautical University demonstrates that the fused features obtained through these methods exhibit improved separability compared to the original features, significantly enhancing detector performance.
- Small target detection,
- Sea clutter,
- Temporal feature information,
- Modified burg,
- Dynamic weighting

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References(17)

References

[1]	关键. 雷达海上目标特性综述[J]. 雷达学报, 2020, 9(4): 674–683. doi: 10.12000/JR20114. GUAN Jian. Summary of marine radar target characteristics[J]. Journal of Radars, 2020, 9(4): 674–683. doi: 10.12000/JR20114.
[2]	BI Xiaowen, GUO Shenglong, YANG Yunxiu, et al. Adaptive target extraction method in sea clutter based on fractional Fourier filtering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5115609. doi: 10.1109/TGRS.2022.3192893.
[3]	SHI Sainan and SHUI Penglang. Sea-surface floating small target detection by one-class classifier in time-frequency feature space[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(11): 6395–6411. doi: 10.1109/ TGRS.2018.2838260. doi: 10.1109/TGRS.2018.2838260.
[4]	XU Shuwen, ZHENG Jibin, PU Jia, et al. Sea-surface floating small target detection based on polarization features[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(10): 1505–1509. doi: 10.1109/LGRS.2018.2852560.
[5]	陈世超, 高鹤婷, 罗丰. 基于极化联合特征的海面目标检测方法[J]. 雷达学报, 2020, 9(4): 664–673. doi: 10.12000/ JR20072. doi: 10.12000/JR20072. CHEN Shichao, GAO Heting, and LUO Feng. Target detection in sea clutter based on combined characteristics of polarization[J]. Journal of Radars, 2020, 9(4): 664–673. doi: 10.12000/JR20072.
[6]	LO T, LEUNG H, LITVA J, et al. Fractal characterisation of sea-scattered signals and detection of sea-surface targets[J]. IEE Proceedings F: Radar and Signal Processing, 1993, 140(4): 243–250. doi: 10.1049/ip-f-2.1993.0034.
[7]	FAN Yifei, TAO Mingliang, and SU Jia. Multifractal correlation analysis of autoregressive spectrum-based feature learning for target detection within sea clutter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5108811. doi: 10.1109/TGRS.2021.3137466.
[8]	关键, 伍僖杰, 丁昊, 等. 基于对角积分双谱的海面慢速小目标检测方法[J]. 电子与信息学报, 2022, 44(7): 2449–2460. doi: 10.11999/JEIT210408. GUAN Jian, WU Xijie, DING Hao, et al. A method for detecting small slow targets in sea surface based on diagonal integrated bispectrum[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2449–2460. doi: 10.11999/JEIT210408.
[9]	关键, 姜星宇, 刘宁波, 等. 海杂波背景下的双极化最大特征值目标检测[J]. 系统工程与电子技术, 2024, 46(11): 3715–3725. doi: 10.12305/j.issn.1001-506X.2024.11.13. GUAN Jian, JIANG Xingyu, LIU Ningbo, et al. Target detection using dual-polarization maximum eigenvalue in sea clutter background[J]. Systems Engineering and Electronics, 2024, 46(11): 3715–3725. doi: 10.12305/j.issn.1001-506X.2024.11.13.
[10]	董云龙, 张兆祥, 丁昊, 等. 基于三特征预测的海杂波中小目标检测方法[J]. 雷达学报, 2023, 12(4): 762–775. doi: 10.12000/JR23037. DONG Yunlong, ZHANG Zhaoxiang, DING Hao, et al. Target detection in sea clutter using a three-feature prediction-based method[J]. Journal of Radars, 2023, 12(4): 762–775. doi: 10.12000/JR23037.
[11]	SHUI Penglang, LI Dongchen, and XU Shuwen. Tri-feature-based detection of floating small targets in sea clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1416–1430. doi: 10.1109/TAES.2014.120657.
[12]	IPIX Radar. The IPIX radar database[EB/OL]. http://soma.ece.mcmaster.ca/ipix/, 2021.
[13]	关键, 刘宁波, 王国庆, 等. 雷达对海探测试验与目标特性数据获取——海上目标双极化多海况散射特性数据集[J]. 雷达学报, 2023, 12(2): 456–469. doi: 10.12000/JR23029. GUAN Jian, LIU Ningbo, WANG Guoqing, et al. Sea-detecting radar experiment and target feature data acquisition for dual polarization multistate scattering dataset of marine targets[J]. Journal of Radars, 2023, 12(2): 456–469. doi: 10.12000/JR23029.
[14]	BARBARESCO F. Algorithme de burg regularise fsds (fonctionnelle stabilisatrice de douceur spectrale) Comparaison avec l'algorithme de burg mfe (Minimum free energy)[C]. Quinzieme Colloque Gretsi - Juan-Les-Pins, 1995: 29–32.
[15]	LI Yuzhou, XIE Pengcheng, TANG Zeshen, et al. SVM-based sea-surface small target detection: A false-alarm-rate-controllable approach[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(8): 1225–1229. doi: 10.1109/LGRS.2019.2894385.
[16]	王鑫, 吴际, 刘超, 等. 基于LSTM循环神经网络的故障时间序列预测[J]. 北京航空航天大学学报, 2018, 44(4): 772–784. doi: 10.13700/j.bh.1001-5965.2017.0285. WANG Xin, WU Ji, LIU Chao, et al. Exploring LSTM based recurrent neural network for failure time series prediction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4): 772–784. doi: 10.13700/j.bh.1001-5965.2017.0285.
[17]	胡学骏, 罗中良. 基于统计理论的多传感器信息融合方法[J]. 传感器技术, 2002(8): 38–39,43. doi: 10.13873/j.1000-97872002.08.013. HU Xuejun and LUO Zhongliang. Method of multi-sensor information fusion based on statistics theory[J]. Transducer and Microsystem Technologies, 2002(8): 38–39,43. doi: 10.13873/j.1000-97872002.08.013.