基于稀疏分解的海面微动目标识别

doi:10.12305/j.issn.1001-506X.2023.04.10

Abstract

Abstract:

Clutter in the marine environment is strong, and the Doppler frequency of slow and weak targets often falls into the sea clutter Doppler bandwidth. It is difficult for the classic moving target detection methods to detect target echoes. In order to address such problems, this paper uses the tunable Q-factor wavelet transform (TQWT) algorithm to obtain the corresponding self-adaptive complete dictionaries according to the difference between the sea clutter and the target in the oscillation properties and sparse characteristics, and then uses the morphological component analysis (MCA) algorithm to obtain the corresponding target sparse coefficients and clutter sparse coefficients. Then, the target components and clutter components are obtained by multiplying the sparse coefficients with their respective adaptive dictionaries. Finally, the effectiveness of the algorithm is verified by using the radar sea detection dataset.

Key words: sea clutter, target recognition, sparse decomposition, tunable Q-factor wavelet transform (TQWT)

CLC Number:

TN957.54

Hanyi HUANG, Shiyou HU, Shenglong GUO, Shanjun LI, Qin SHU. Sea surface micro-moving target recognition based on sparse decomposition[J]. Systems Engineering and Electronics, 2023, 45(4): 1016-1023.

Figures/Tables 9

Fig.1

Fig.2

Fig.3

Table 1

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

References 33

1	ROSENBERG L . Parametric modeling of sea clutter Doppler spectra[J]. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 5105409.
2	YAN Y , XING H Y . A sea clutter detection method based on LSTM error frequency domain conversion[J]. Alexandria Engineering Journal, 2021, 61 (6): 883- 891.
3	GAO C , TAO R , KANG X J . Weak target detection in the presence of sea clutter using radon-fractional Fourier transform canceller[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 5818- 5830. doi: 10.1109/JSTARS.2021.3078723
4	SHI Y L , WANG L , YAO T T . Orthogonal projection constant false alarm rate algorithm in fractional domain for small surface targets[J]. IET Signal Processing, 2022, 16 (8): 975- 991. doi: 10.1049/sil2.12137
5	LIANG Z R, WANG Y, ZHANG X F, et al. Identification of ship and corner reflector in sea clutter environment[C]//Proc. of the 15th IEEE International Conference on Signal Processing, 2020: 622-626.
6	KIRSCHT M , MIETZNER J , BICKERT B , et al. An airborne radar sensor for maritime and ground surveillance and reconnaissance—algorithmic issues and exemplary results[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2016, 9 (3): 971- 979.
7	JI Y G , JIE Z , WANG Y M , et al. Vessel target detection based on fusion range-Doppler image for dual-frequency high-frequency surface wave radar[J]. IET Radar Sonar & Navigation, 2016, 10 (2): 333- 340.
8	LIU J , LIU S , LIU W , et al. Persymmetric adaptive detection of distributed targets in Compound-Gaussian sea clutter with Gamma texture[J]. Signal Processing, 2018, 152 (11): 340- 349.
9	ROSENBERG L , DUK V , NG W H . Detection in sea clutter using sparse signal separation[J]. IEEE Trans. on Aerospace and Electronic Systems, 2020, 56 (6): 4384- 4394. doi: 10.1109/TAES.2020.2989093
10	HUBEL D H , WIESEL T N . Receptive fields of single neurones in the cat's striate cortex[J]. The Journal of Physiology, 1959, 148, 574- 591. doi: 10.1113/jphysiol.1959.sp006308
11	OLSHAUSEN B A , FIELD D J . Emergence of simple-cell receptive field properties by learning a sparse code for natural images[J]. Nature, 1996, 381 (6583): 607- 609. doi: 10.1038/381607a0
12	STARCK J L , ELAD M , DONOHO D . Redundant multiscale transforms and their application for morphological component separation[J]. Advances in Imaging and Electron Physics, 2004, 132 (4): 287- 348.
13	陈小龙, 关键, 董云龙, 等. 稀疏域海杂波抑制与微动目标检测方法[J]. 电子学报, 2016, 44 (4): 860- 867. doi: 10.3969/j.issn.0372-2112.2016.04.015
	CHEN X L , GUAN J , DONG Y L , et al. Sea clutter suppression and micromotion target detection in sparse domain[J]. Acta Electronica Sinica, 2016, 44 (4): 860- 867. doi: 10.3969/j.issn.0372-2112.2016.04.015
14	FARSHCHIAN M, SELESNICK I. Application of a sparse time-frequency technique for targets with oscillatory fluctuations[C]//Proc. of the International Waveform Diversity & Design Conference, 2012: 191-196.
15	杨国铮, 禹晶, 肖创柏, 等. 基于形态成分分析的复杂背景SAR图像舰船尾迹检测[J]. 计算机辅助设计与图形学学报, 2016, 28 (10): 1662- 1671. doi: 10.3969/j.issn.1003-9775.2016.10.007
	YANG G Z , YU J , XIAO C B , et al. Ship wake detection in SAR images with complex backgrounds using morphological omponent analysis[J]. Journal of Computer-Aided Design & Computer Graphics, 2016, 28 (10): 1662- 1671. doi: 10.3969/j.issn.1003-9775.2016.10.007
16	NGUYEN S , AL-ASHWAL W A . Sea clutter mitigation using resonance-based signal decomposition[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12 (11): 2257- 2261. doi: 10.1109/LGRS.2015.2464807
17	TANG G , YANG Q , WANG H Q , et al. Sparse classification of rotating machinery faults based on compressive sensing strategy[J]. Mechatronics, 2015, 31, 60- 67. doi: 10.1016/j.mechatronics.2015.04.006
18	ZHU Y C , YANG K D , DUAN R , et al. Sparse spatial spectral estimation with heavy sea bottom reverberation in the fractional fourier domain[J]. Applied Acoustics, 2020, 160 (3): 107132.
19	HUANG G Q , XIAO Y W , YIN Z . Denoising method for underwater acoustic signals based on sparse decomposition[J]. Journal of Physics Conference Series, 2020, 1550 (3): 032139. doi: 10.1088/1742-6596/1550/3/032139
20	DU D , PAN Z B , ZHANG P H , et al. Compressive sensing image recovery using dictionary learning and shape-adaptive DCT thresholding[J]. Magnetic Resonance Imaging, 2018, 55 (7): 60- 71.
21	WU Y , ZHOU J , AKANKWASA N T , et al. Fabric texture representation using the stable learned discrete cosine transform dictionary[J]. Textile Research Journal, 2019, 89 (3): 294- 310. doi: 10.1177/0040517517743688
22	ZHANG X W, YANG D D, GUO J X, et al. Weak moving target detection based on short-time Fourier transform in sea clutter[C]//Proc. of the IEEE 4th International Conference on Signal and Image Processing, 2019: 415-419.
23	SELESNICK I W . Wavelet transform with tunable Q-factor[J]. IEEE Trans. on Signal Processing, 2011, 59 (8): 3560- 3575. doi: 10.1109/TSP.2011.2143711
24	潘美艳, 杨予昊, 李大圣, 等. 一种基于能量选择的改进TQWT海杂波抑制算法[J]. 现代雷达, 2018, 40 (10): 32- 37.
	PAN M Y , YANG Y H , LI D S , et al. An improved TQWT sea clutter suppression algorithm based on energy selection[J]. Modern Radar, 2018, 40 (10): 32- 37.
25	LI J L , WANG H Q , SONG L Y , et al. A novel feature extraction method for roller bearing using sparse decomposition based on self-Adaptive complete dictionary[J]. Measurement, 2019, 148, 106934. doi: 10.1016/j.measurement.2019.106934
26	ELAD M , STRACK J L , QUERRE P , et al. Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA)[J]. Applied and Computational Harmonic Analysis, 2005, 19 (3): 340- 358. doi: 10.1016/j.acha.2005.03.005
27	NG B, ROSENBERG L. Sparse detection in sea-clutter using orthogonal matching pursuit[C]//Proc. of the IEEE Radar Conference, 2019: 22-26.
28	AFONSO M V , BIOUCAS-Dias J M , FIGUEIREDO M . Fast image recovery using variable splitting and constrained optimization[J]. IEEE Trans. on Image Processing, 2010, 19 (9): 2345- 2356. doi: 10.1109/TIP.2010.2047910
29	SELESNICK I W . Resonance-based signal decomposition: a new sparsity-enabled signal analysis method[J]. Signal Processing, 2011, 91 (12): 2793- 2809. doi: 10.1016/j.sigpro.2010.10.018
30	FIGUEIREDO M, BIOUCAS-DIAS J M, AFONSO M V. Fast frame-based image deconvolution using variable splitting and constrained optimization[C]//Proc. of the 15th Workshop on Statistical Signal Processing, 2009: 109-112.
31	刘宁波, 丁昊, 黄勇, 等. X波段雷达对海探测试验与数据获取年度进展[J]. 雷达学报, 2021, 10 (1): 173- 182.
	LIU N B , DING H , HUANG Y , et al. Annual progress of sea-detecting X-band radar and data acquisition program[J]. Journal of Radars, 2021, 10 (1): 173- 182.
32	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.
	LIU N B , DONG Y L , WANG G Q , et al. Sea-detecting X-band radar and data acquisition program[J]. Journal of Radars, 2019, 8 (5): 656- 667.
33	OZKURT N. Signal adaptive Q factor selection for resonance based signal separation using tunable-Q wavelet transform[C]//Proc. of the 41st International Conference on Telecommunications and Signal Processing, 2018.

数据包名称	海况等级	工作模式	起始方位/(°)	脉冲个数	描述信息
20210106155330_01_staring.mat	3~4级	凝视	42.18	＞10⁴	纯海杂波
20210106155432_01_staring.mat	3~4级	凝视	42.18	＞10⁴	纯海杂波
20210106160919_02_staring.mat	3~4级	凝视	17.36	＞10⁴	2.778 km处有漂浮目标

[1]	Qingyuan ZHAO, Zhiqiang ZHAO, Chunmao YE, Yaobing LU. Micro-motion fusion recognition of double band early warning radar based on self-attention mechanism [J]. Systems Engineering and Electronics, 2023, 45(3): 708-716.
[2]	Junling ZHANG, Mei DONG, Baixiao CHEN. Sea clutter suppression algorithm based on tunable Q-factor wavelet transform [J]. Systems Engineering and Electronics, 2023, 45(2): 343-351.
[3]	Caiyun WANG, Yida WU, Jianing WANG, Lu MA, Huanyue ZHAO. SAR image target recognition based on combinatorial optimization convolutional neural network [J]. Systems Engineering and Electronics, 2022, 44(8): 2483-2487.
[4]	Jingming SUN, Shengkang YU, Jun SUN. Radar small sample target recognition method based on meta learning and its improvement [J]. Systems Engineering and Electronics, 2022, 44(6): 1839-1845.
[5]	Xiaoling ZHOU, Zhaoxia ZHANG, Ya LU, Qian WANG, Kunkun WANG. SAR image recognition based on improved R-FCN [J]. Systems Engineering and Electronics, 2022, 44(4): 1202-1209.
[6]	Jingming SUN, Shengkang YU, Jun SUN. Pose sensitivity analysis of HRRP recognition based on deep learning [J]. Systems Engineering and Electronics, 2022, 44(3): 802-807.
[7]	Yanling SHI, Lei WANG, Junhao LI. CFAR detection for small targets on sea surface based on singular value decomposition in projection space [J]. Systems Engineering and Electronics, 2022, 44(2): 512-519.
[8]	Yu LEI, Xiangguang LENG, Xiaoyan ZHOU, Zhongzhen SUN, Kefeng JI. Recognition method of ship target in complex SAR image based on improved ResNet network [J]. Systems Engineering and Electronics, 2022, 44(12): 3652-3660.
[9]	Chunling XUE, Fei CAO, Qing SUN, Jianqiang QIN, Xiaowei FENG. Sea-surface weak target detection based on multi-feature information fusion [J]. Systems Engineering and Electronics, 2022, 44(11): 3338-3345.
[10]	Qi LIU, Xinyu ZHANG, Yongxiang LIU. Few-shot SAR target recognition based on gated multi-scale matching network [J]. Systems Engineering and Electronics, 2022, 44(11): 3346-3356.
[11]	Caiyun WANG, Chen YAO, Yida WU, Jianing WANG, Xiaofei LI, Panpan HUANG. Radar target recognition based on improved Dijkstra algorithm with time-frequency domain filtering [J]. Systems Engineering and Electronics, 2022, 44(10): 3090-3095.
[12]	Xin GUAN, Haotian YU, Xiao YI. Research on evidence independence and its influence on target recognition [J]. Systems Engineering and Electronics, 2022, 44(1): 192-198.
[13]	Yongzhen LI, Yemin LIU, Chen PANG, Datong HUANG. Chaff clouds recognition method based on layered polarization characteristics [J]. Systems Engineering and Electronics, 2021, 43(8): 2099-2107.
[14]	Chunsi XIE, Zhiying LIU, Yu SANG. Target recognition model of ship-to-land missile based on feature matching [J]. Systems Engineering and Electronics, 2021, 43(8): 2244-2253.
[15]	Yuan YAN, Jun SUN, Jingming SUN, Junpeng YU. Radar few shot target recognition method and application analysis [J]. Systems Engineering and Electronics, 2021, 43(3): 684-692.

Sea surface micro-moving target recognition based on sparse decomposition

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 33

Related Articles 15

Recommended Articles

Metrics

Comments