Submit Manuscript
Peer Review
Editor Work
| Citation: | 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
|
Target Detection in Sea Clutter Based on Combined Characteristics of Polarization
DOI: 10.12000/JR20072
More Information-
Abstract
Polarization is a property applying to transverse waves that specifies the geometrical orientation of the oscillations. This paper proposes a method for detecting small targets on the sea surface based on the combination of polarization features of two models. The scattering mechanism of sea clutter is random scattering at low glazing angle or glancing angle and the randomness is high as the angles do not have any specified shape. However, a target has a specific shape, and thus, the randomness of scattering will be less. Clutter is a term used for unwanted echoes in electronic systems, particularly in reference to radars. Such echoes typically return from ground, sea, rain, and animals/insects. In this literature, the randomness of a scattering mechanism in an echo is obtained from the probability density functions of polarization entropy using the Cloude decomposition model. Further, the proportion of scattering at spherical, dihedral, and helicoid angles from the target echoes will be different in the sea clutter. Therefore, the relative coefficient of power of these three scattering components in each echo is extracted based on Krogager polarization decomposition. Then, polarization features with good separability and complementarity are selected to form the polarization feature vector, and the characteristics are verified by Principle Component Analysis (PCA). Finally, One Class Support Vector Machine (OCSVM) is used for classification and recognition based on the polarization decomposition feature vector. Instead of single-polarization detection methods, our method uses two polarization modes to extract the decomposition features with separability and complementarity through polarization coherent decomposition and incoherent decomposition, respectively. The experimental results of the IPIX data show the effectiveness of our method. Thus, the detection performance of our model is better than those methods based on single-polarization decomposition in complex and difficult sea conditions. -
-
References
[1] FARINA A and STUDER F A. A review of CFAR detection techniques in radar systems[J]. Microware Journal, 1986, 29(5): 115, 116, 118.[2] 丁昊, 刘宁波, 董云龙, 等. 雷达海杂波测量试验回顾与展望[J]. 雷达学报, 2019, 8(3): 281–302. doi: 10.12000/JR19006DING Hao, LIU Ningbo, DONG Yunlong, et al. Overview and prospects of radar sea clutter measurement experiments[J]. Journal of Radars, 2019, 8(3): 281–302. doi: 10.12000/JR19006[3] 刘宁波, 董云龙, 王国庆, 等. X波段雷达对海探测试验与数据获取[J]. 雷达学报, 2019, 8(5): 656–667. doi: 10.12000/JR19089LIU Ningbo, DONG Yunlong, WANG Guoqing, et al. Sea-detecting X-band radar and data acquisition program[J]. Journal of Radars, 2019, 8(5): 656–667. doi: 10.12000/JR19089[4] 丁昊, 王国庆, 刘宁波, 等. 逆Gamma纹理背景下两类子空间目标的自适应检测方法[J]. 雷达学报, 2017, 6(3): 275–284. doi: 10.12000/JR16088DING Hao, WANG Guoqing, LIU Ningbo, et al. Adaptive detectors for two types of subspace targets in an inverse gamma textured background[J]. Journal of Radars, 2017, 6(3): 275–284. doi: 10.12000/JR16088[5] 许述文, 石星宇, 水鹏朗. 复合高斯杂波下抑制失配信号的自适应检测器[J]. 雷达学报, 2019, 8(3): 326–334. doi: 10.12000/JR19030XU Shuwen, SHI Xingyu, and SHUI Penglang. An adaptive detector with mismatched signals rejection in compound Gaussian clutter[J]. Journal of Radars, 2019, 8(3): 326–334. doi: 10.12000/JR19030[6] 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[7] 左磊, 产秀秀, 禄晓飞, 等. 基于空域联合时频分解的海面微弱目标检测方法[J]. 雷达学报, 2019, 8(3): 335–343. doi: 10.12000/JR19035ZUO Lei, CHAN Xiuxiu, LU Xiaofei, et al. A weak target detection method in sea clutter based on joint space-time-frequency decomposition[J]. Journal of Radars, 2019, 8(3): 335–343. doi: 10.12000/JR19035[8] LUO Feng, ZHANG Danting, and ZHANG Bo. The fractal properties of sea clutter and their applications in maritime target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(6): 1295–1299. doi: 10.1109/LGRS.2013.2237750[9] 陈世超, 罗丰, 胡冲, 等. 基于多普勒谱非广延熵的海面目标检测方法[J]. 雷达学报, 2019, 8(3): 344–354. doi: 10.12000/JR19012CHEN Shichao, LUO Feng, HU Chong, et al. Small target detection in sea clutter background based on Tsallis entropy of Doppler spectrum[J]. Journal of Radars, 2019, 8(3): 344–354. doi: 10.12000/JR19012[10] 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[11] 许述文, 蒲佳. 基于四极化通道融合的海面漂浮微弱目标特征检测[J]. 信号处理, 2017, 33(3): 324–329. doi: 10.16798/j.issn.1003-0530.2017.03.011XU Shuwen and PU Jia. Floating small targets detection in sea clutter based on four-polarization-channels fusion[J]. Journal of Signal Processing, 2017, 33(3): 324–329. doi: 10.16798/j.issn.1003-0530.2017.03.011[12] 田玉芳, 尹志盈, 姬光荣, 等. 基于SVM的海面弱目标检测[J]. 中国海洋大学学报, 2013, 43(7): 104–109.TIAN Yufang, YIN Zhiying, JI Guangrong, et al. Weak targets detection in sea clutter based on SVM[J]. Periodical of Ocean University of China, 2013, 43(7): 104–109.[13] 武鹏, 王俊, 王文光. 基于极化特征分解的海上小目标检测算法研究[J]. 电子与信息学报, 2011, 33(4): 816–822. doi: 10.3724/SP.J.1146.2010.00678WU Peng, WANG Jun, and WANG Wenguang, et al. Small target detection in sea clutter based on polarization characteristics decomposition[J]. Journal of Electronics &Information Technology, 2011, 33(4): 816–822. doi: 10.3724/SP.J.1146.2010.00678[14] 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[15] MORRIS J T and ANDERSON S J. Aspect dependence of the polarimetric characteristics of sea clutter: I. Variation with elevation angle[C]. 2008 International Conference on Radar, Adelaide, Australia, 2008: 106–110.[16] ANDERSON S J and MORRIS J T. Aspect dependence of the polarimetric characteristics of sea clutter: II. Variation with azimuth angle[C]. 2008 International Conference on Radar, Adelaide, Australia, 2008: 581–585.[17] 张新勋, 周生华, 刘宏伟. 目标极化散射特性对极化分集雷达检测性能的影响[J]. 雷达学报, 2019, 8(4): 510–518. doi: 10.12000/JR18112ZHANG Xinxun, ZHOU Shenghua, and LIU Hongwei. Influence of target polarization scattering characteristics on the detection performance of polarization diversity radar[J]. Journal of Radars, 2019, 8(4): 510–518. doi: 10.12000/JR18112[18] AN Wentao, CUI Yi, and YANG Jian. Three-component model-based decomposition for polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(6): 2732–2739. doi: 10.1109/TGRS.2010.2041242[19] CLOUDE S R and POTTIER E. An entropy based classification scheme for land applications of polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(1): 68–78. doi: 10.1109/36.551935[20] AN Wentao, CUI Yi, ZHANG Weijie, et al. Data compression for multilook polarimetric SAR Data[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(3): 476–480. doi: 10.1109/LGRS.2009.2017498[21] KROGAGER E. New decomposition of the radar target scattering matrix[J]. Electronics Letters, 1990, 26(18): 1525–1527. doi: 10.1049/el:19900979[22] KROGAGER E, BOERNER W M, and MADSEN S N. Feature-motivated Sinclair matrix (sphere/diplane/helix) decomposition and its application to target sorting for land feature classification[C]. The SPIE 3120, Wideband Interferometric Sensing and Imaging Polarimetry, San Diego, USA, 1997. doi: 10.1117/12.300620.[23] CHAMUNDEESWARI V V, SINGH D, and SINGH K. An analysis of texture measures in PCA-based unsupervised classification of SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(2): 214–218. doi: 10.1109/LGRS.2008.2009954[24] ZHANG Wei, DU Lan, LI Liling, et al. Infinite Bayesian one-class support vector machine based on Dirichlet process mixture clustering[J]. Pattern Recognition, 2018, 78: 56–78. doi: 10.1016/j.patcog.2018.01.006[25] XU Huan and HUANG Deshuang. One class support vector machines for distinguishing photographs and graphics[C]. IEEE International Conference on Networking, Sensing and Control, Sanya, China, 2008. doi: 10.1109/ICNSC.2008.4525289.[26] CHO H W. Data description and noise filtering based detection with its application and performance comparison[J]. Expert Systems with Applications, 2009, 36(1): 434–441. doi: 10.1016/j.eswa.2007.09.053[27] TIAN Jiang, GU Hong, GAO Chiyang, et al. Local density one-class support vector machines for anomaly detection[J]. Nonlinear Dynamics, 2011, 64(1/2): 127–130. doi: 10.1007/s11071-010-9851-y[28] BOUNSIAR A and MADDEN M G. Kernels for one-class support vector machines[C]. 2014 International Conference on Information Science & Applications (ICISA), Seoul, Republic of Korea, 2014. doi: 10.1109/ICISA.2014.6847419. -
Proportional views
- Publishing Ethics
- Journal Insights
- Abstracting & Indexing
- Peer Review Policies
- Guide for Authors
- Conference
- ISSN 2095-283X (Print)ISSN 2097-339X (Online)
- CN 10-1030/TN
- CODEN LXEUAO
About Journal
- Sponsor: China Radio Detection and Ranging Industry Association (CRIA)
- Phone: 010-58887062
- Email：radars@aircas.ac.cn
- Publisher: Leida Xuebao Bianjibu (Editorial office of the Journal of Radars)
Contacts Us
京ICP备20021838号-8
Supported by: Beijing Renhe Information Technology Co. Ltd
Export File
Citation
Format
Content
DownLoad:
- Figure 1. The 2D scatter graph corresponding to the characteristics of clutter cell and target cell in each dataset
- Figure 2. Flow chart of surface target detection based on polarization joint feature
- Figure 3. Detection accuracy of different data sets in different observation time