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| Citation: | 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
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Target Detection in Sea Clutter Using a Three-feature Prediction-based Method
DOI: 10.12000/JR23037
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Abstract
Feature-based detection methods are often employed to address the challenges related to small-target detection in sea clutter. These methods determine the presence or absence of a target based on whether the feature value falls within a certain judgment region. However, such methods often overlook the temporal information between features. In fact, the temporal correlation between historical and current frame data can provide valuable a priori information, thereby enabling the calculation of the feature value of the current frame. To this end, this paper proposes a novel method for time-series modeling and prediction of radar echoes using an Auto-Regressive (AR) model in the feature domain, leveraging a priori information from historical frame features. To verify the feasibility of AR modeling and prediction of feature sequences, the AR model was first employed in the modeling and 1-step prediction analysis of Average Amplitude (AA), Relative Doppler Peak Height (RDPH), and Frequency Peak-to-Average Ratio (FPAR) feature sequences. Next, a technique for extracting feature values by utilizing the temporal information of historical frame features as a priori information was proposed. Based on this approach, a small-target detection method predicated on three-feature prediction, which can effectively utilize the temporal information of historical frame features for AA, RDPH, and FPAR, was proposed. Finally, the validity of the proposed method was verified using a measured data set. -
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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/JR20114GUAN Jian. Summary of marine radar target characteristics[J]. Journal of Radars, 2020, 9(4): 674–683. doi: 10.12000/JR20114[2] 张坤, 水鹏朗, 王光辉. 相参雷达K分布海杂波背景下非相干积累恒虚警检测方法[J]. 电子与信息学报, 2020, 42(7): 1627–1635. doi: 10.11999/JEIT190441ZHANG Kun, SHUI Penglang, and WANG Guanghui. Non-coherent integration constant false alarm rate detectors against k-distributed sea clutter for coherent radar systems[J]. Journal of Electronics &Information Technology, 2020, 42(7): 1627–1635. doi: 10.11999/JEIT190441[3] 许述文, 白晓惠, 郭子薰, 等. 海杂波背景下雷达目标特征检测方法的现状与展望[J]. 雷达学报, 2020, 9(4): 684–714. doi: 10.12000/JR20084XU Shuwen, BAI Xiaohui, GUO Zixun, et al. Status and prospects of feature-based detection methods for floating targets on the sea surface[J]. Journal of Radars, 2020, 9(4): 684–714. doi: 10.12000/JR20084[4] 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[5] 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[6] FAN Yifei, TAO Mingliang, SU Jia, et al. Weak target detection based on joint fractal characteristics of autoregressive spectrum in sea clutter background[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(12): 1824–1828. doi: 10.1109/LGRS.2019.2912329[7] 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[8] 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[9] 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[10] 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/JR20072CHEN 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[11] YAN Kun, BAI Yu, WU H C, et al. Robust target detection within sea clutter based on graphs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 7093–7103. doi: 10.1109/TGRS.2019.2911451[12] 时艳玲, 姚婷婷, 郭亚星. 基于图连通密度的海面漂浮小目标检测[J]. 电子与信息学报, 2021, 43(11): 3185–3192. doi: 10.11999/JEIT201028SHI Yanling, YAO Tingting, and GUO Yaxing. Floating small target detection based on graph connected density in sea surface[J]. Journal of Electronics &Information Technology, 2021, 43(11): 3185–3192. doi: 10.11999/JEIT201028[13] XIE Jianda and XU Xiaojian. Phase-feature-based detection of small targets in sea clutter[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 3507405. doi: 10.1109/LGRS.2021.3093620[14] WU Xijie, DING Hao, LIU Ningbo, et al. A method for detecting small targets in sea surface based on singular spectrum analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5110817. doi: 10.1109/TGRS.2021.3138488[15] 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/JEIT210408GUAN 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[16] WU Xijie, DING Hao, LIU Ningbo, et al. Priori information-based feature extraction method for small target detection in sea clutter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5115315. doi: 10.1109/TGRS.2022.3188046[17] HAYKIN S, BAKKER R, and CURRIE B W. Uncovering nonlinear dynamics-the case study of sea clutter[J]. Proceedings of the IEEE, 2002, 90(5): 860–881. doi: 10.1109/JPROC.2002.1015011[18] NOHARA T J and HAYKIN S. AR-based growler detection in sea clutter[J]. IEEE Transactions on Signal Processing, 1993, 41(3): 1259–1271. doi: 10.1109/78.205728[19] 黄红梅. 应用时间序列分析[M]. 北京: 清华大学出版社, 2016: 17–57.HUANG Hongmei. Apply Time Series Analysis[M]. Beijing: Tsinghua University Press, 2016: 17–57.[20] 郑宁. 基于多源数据的高速铁路轨道几何异常状态检测方法研究[D]. [硕士论文], 北京交通大学, 2021: 22–40.ZHENG Ning. Research on high speed railway track geometric anomaly detection method based on multisource data[D]. [Master dissertation], Beijing Jiaotong University, 2021: 22–40.[21] 范剑青, 姚琦伟, 陈敏, 译. 非线性时间序列: 建模、预报及应用[M]. 北京: 高等教育出版社, 2005: 21–92.FAN Jianqing, YAO Qiwei, CHEN Min. translation. Nonlinear Time Series: Modeling, Forecasting, and Applications[M]. Beijing: Higher Education Press, 2005: 21–92.[22] SHUI Penglang, LI Dongchen, and XU Shuwen. Tri-feature-based detection of floating small targets in sea clutter[J]. IEEE Transactions on Aerospace & Electronic Systems, 2014, 50(2): 1416–1430. doi: 10.1109/TAES.2014.120657[23] 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[24] GUO Zixun and SHUI Penglang. Anomaly based sea-surface small target detection using K-nearest neighbor classification[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(6): 4947–4964. doi: 10.1109/TAES.2020.3011868[25] The IPIX radar database[EB/OL]. http://soma.ece.mcmaster.ca/ipix/, 2021.[26] SHI Yanling, XIE Xiaoyan, and LI Dongchen. Range distributed floating target detection in sea clutter via feature-based detector[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1847–1850. doi: 10.1109/LGRS.2016.2614750 -
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- Figure 1. Schematic diagram of the temporal relationship of the features between frames
- Figure 2. Average autocorrelation coefficient and average partial autocorrelation coefficient for the three features
- Figure 3. Optimal order of AR models for the three feature sequences estimated by the BIC
- Figure 4. AR model fitting results for three features (sea clutter)
- Figure 5. 1-step prediction results of AR models with three features
- Figure 6. AR model fitting results for three features (target echo)
- Figure 7. The schematic diagram of the feature extraction method using prior information
- Figure 8. The workflow diagram of the detector based on three-feature prediction
- Figure 9. Comparison of B-distance in two feature spaces
- Figure 10. The effect of weighting coefficient on detection probabilities
- Figure 11. Detection probabilities of the four detectors