基于EMD能量占比的海面漂浮小目标特征检测

doi:10.12305/j.issn.1001-506X.2021.02.03

摘要/Abstract

摘要：

针对传统的时频分析方法对海杂波分析有限的问题，提出一种基于经验模态分解(empirical mode decomposition, EMD)能量占比的海面漂浮小目标特征检测方法。首先，采用EMD将接收回波分为独立不同尺度的若干个固有模态(intrinsic mode function, IMF)分量，实现对接收回波的频率从高频到低频的分解。然后，分别建立IMF分量与接收回波数据的相关系数，并利用平均均值-标准差之比作为筛选IMF分量的准则，自动筛选出能量较大且波动平稳的低阶IMF分量。最后，提取IMF分量在原始信号中的平均能量占比作为特征，利用蒙特卡罗方法设置门限，进行海面目标异常检测。实测数据的结果显示，所提算法的性能优于对比算法。

关键词: 经验模态分解, 相关系数, 能量占比, 漂浮小目标检测

Abstract:

Aiming at the problem that the traditional time-frequency analysis method is limited to the analysis of sea clutter, a feature detection method of floating small target on the sea surface based on empirical mode decomposition (EMD) energy proportion is proposed. Firstly, the received echo is divided into several independent intrinsic mode function (IMF) components with different scales by EMD, which can realize the decomposition of the frequency for the received echo from high frequency to low frequency. Secondly, the correlation coefficients between the IMF components and the received echo data are established respectively. And the ratio of mean to standard deviation is used as the criterion to filter the IMF components, which can automatically select the low-order IMF components with large energy and stable fluctuation. Finally, the average energy proportion of IMF component in the original signal is extracted as the feature, and the Monte Carlo method is used to set the threshold for sea surface target anomaly detection. The results of the measured data show that the performance of the proposed algorithm is better than that of the comparison algorithm.

Key words: empirical mode decomposition (EMD), correlation coefficient, energy ratio, floating small target detection

中图分类号:

TN911.23

时艳玲, 刘子鹏, 张学良, 顾为亮. 基于EMD能量占比的海面漂浮小目标特征检测[J]. 系统工程与电子技术, 2021, 43(2): 300-310.

Yanling SHI, Zipeng LIU, Xueliang ZHANG, Weiliang GU. Feature detection of floating small target on the sea surface based on EMD energy proportion[J]. Systems Engineering and Electronics, 2021, 43(2): 300-310.

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参考文献 25

1	ZHOU W , XIE J H , ZHANG B Q , et al. Maximum likelihood detector in Gamma-distributed sea clutter[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15 (11): 1705- 1709. doi: 10.1109/LGRS.2018.2859785
2	ZHOU W , XIE J H , LI G P , et al. Robust CFAR detector with weighted amplitude iteration in nonhomogeneous sea clutter[J]. IEEE Trans.on Aerospace and Electronic Systems, 2017, 53 (3): 1520- 1535. doi: 10.1109/TAES.2017.2671798
3	LIU H Y , SONG J , XIO NG W , et al. Analysis of amplitude statistical and correlation characteristics of high grazing angle sea- clutter[J]. The Journal of Engineering, 2019, (2): 6829- 6833.
4	SHI Y L . Three GLRT detectors for range distributed target in grouped partially homogeneous radar environment[J]. Signal Processing, 2017, 135, 121- 131. doi: 10.1016/j.sigpro.2016.12.030
5	时艳玲, 林毓峰, 宛汀. 部分均匀海杂波中雷达目标的平滑自适应检测[J]. 系统工程与电子技术, 2016, 38 (12): 2745- 2751.
	SHI Y L , LIN Y F , WAN T . Smooth adaptive detector for ra dar target in partially homogeneous sea clutter environment[J]. Systems Engineering and Electronic s, 2016, 38 (12): 2745- 2751.
6	时艳玲, 林毓峰, 梁丹丹. 非平稳海杂波背景下子带分段ANMF检测器[J]. 系统工程与电子技术, 2018, 40 (4): 782- 789.
	SHI Y L , LIN Y F , LIANG D D . Subband segmented ANMF detector in non-stationary sea clutter[J]. Systems Engineering and Electronics, 2018, 40 (4): 782- 789.
7	WANG Y H , LI Q , ZHANG Y M . A statistical distribution of quad-pol X-band sea clutter time series acquired at a grazing angle[J]. Acta Oceanologica Sinica, 2018, 37 (3): 94- 102. doi: 10.1007/s13131-018-1202-8
8	施赛楠, 水鹏朗, 杨春娇, 等. 基于逆高斯纹理空间相关性的雷达目标检测[J]. 系统工程与电子技术, 2017, 39 (10): 2215- 2220.
	SHI S N , SHUI P L , YANG C J , et al. Radar target detection based on spatial correlation of inverse-Gaussian texture[J]. Systems Engineering and Electronics, 2017, 39 (10): 2215- 2220.
9	LEUNG H , LO T . Chaotic radar signal processing over the sea[J]. IEEE Journal of Ocean Engineering, 1993, 18 (3): 287- 295. doi: 10.1109/JOE.1993.236367
10	XI C P, XIONG G, YANG Y H, et al. Simulating multi-fractal sea clutter and surface based on the random multiplicative model[C]//Proc.of the IET Radar Conference, 2019.
11	FAN Y F , TAO M L , SU J , 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
12	MCDONALD M , DAMINI A . Limitations of nonlinear chaotic dynamics in predicting sea clutter returns[J]. IEEE Procee-dings-Radar, Sonar and Navigation, 2004, 151 (2): 105- 113. doi: 10.1049/ip-rsn:20040261
13	HU J , TUNG W W , GAO J B . Detection of low observable targets within sea clutter by structure function based multifractal analysis[J]. IEEE Trans.on Antenna and Propagation, 2006, 54 (1): 136- 143. doi: 10.1109/TAP.2005.861541
14	WU X B , WU Y Q , CHENG F , et al. Extraction of sea-state parameters with an X-band radar[J]. Wuhan University Journal of Natural Sciences, 2008, 13 (1): 55- 61. doi: 10.1007/s11859-008-0111-7
15	PAN M Y , CHEN J J , XIE S D , et al. Sea clutter suppression algorithm based on signal resonance[J]. The Journal of Engineering, 2019, 21 (11): 7360- 7364.
16	于晓涵, 陈小龙, 黄勇, 等. 雷达动目标短时稀疏分数阶表示域探测方法[J]. 系统工程与电子技术, 2018, 40 (11): 2426- 2432. doi: 10.3969/j.issn.1001-506X.2018.11.06
	YU X H , CHEN X L , HUANG Y , et al. Radar detection for moving target in short-time sparse fractional representative domain[J]. Systems Engineering and Electronics, 2018, 40 (11): 2426- 2432. doi: 10.3969/j.issn.1001-506X.2018.11.06
17	HUANG N E , SHEN Z , LONG S R , et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-statio nary time series analysis[J]. Proceedings of the R oyal Society A: Mathematical, Physical and Engineering Science, 1998, 454 (1971): 903- 995. doi: 10.1098/rspa.1998.0193
18	LI Y , YANG Y H , ZHU X Y . Target detection in sea clutter based on multifractal characteristics after empirical mode decomposition[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14 (9): 1547- 1551. doi: 10.1109/LGRS.2017.2721463
19	张冰瑞, 雷志勇, 周海峰, 等. 基于HHT的高频雷达机动目标参数估计[J]. 系统工程与电子技术, 2018, 40 (3): 546- 551.
	ZHANG B R , LEI Z Y , ZHOU H F , et al. Maneuvering targets parameters estimation algorithm based on HHT in high-frequency radar[J]. Systems Engineering and Electronics, 2018, 40 (3): 546- 551.
20	张建, 黄勇, 关键, 等. 基于低频IMF能量比的微弱目标检测算法[J]. 信号处理, 2011, 27 (12): 1850- 1859. doi: 10.3969/j.issn.1003-0530.2011.12.009
	ZHANG J , HUANG Y , GUAN J , et al. Weak target detection based on the proportion of the low frequenc y IMF energy[J]. Signal Processing, 2011, 27 (12): 1850- 1859. doi: 10.3969/j.issn.1003-0530.2011.12.009
21	关键, 张建. 基于固有模态能量熵的微弱目标检测算法[J]. 电子与信息学报, 2011, 33 (10): 2494- 2499.
	GUAN J , ZHANG J . Weak target detection based on intrinsic mode energy entropy[J]. Journal of Electronics & Information Technology, 2011, 33 (10): 2494- 2499.
22	张林, 李秀友, 刘宁波, 等. 基于分形特性改进的EMD目标检测算法[J]. 电子与信息学报, 2016, 38 (5): 1041- 1046.
	ZHANG L , LI X Y , LIU N B , et al. Improved EMD target detection method based on mono fractal characteristics[J]. J ournal of Electronics & Information Technology, 2016, 38 (5): 1041- 1046.
23	SHUI P L , LI D C , XU S W . Tri-feature-based detection of floating small targets in sea clutter[J]. IEEE Trans.on Aerospace and Electronic Systems, 2014, 50 (2): 1416- 1430. doi: 10.1109/TAES.2014.120657
24	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.
	CHEN S C , LUO F , HU C , 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.
25	HAYKIN S. The McMaster IPIX radar sea clutter database[EB/OL].[2020-05-25]. http://soma.ece.mcmaster.ca/ipix/.

数据编号	数据名称	风速/(km/h)	浪高/m	角度/(°)	目标单元	受影响单元
1	19931107_135603_starea17	9	2.2	9	9	8, 10, 11
2	19931108_220902_starea26	9	1.1	97	7	6, 8
3	19931109_191449_starea30	19	0.9	98	7	6, 8
4	19931109_202217_starea31	19	0.9	98	7	6, 8, 9
5	19931110_001635_starea40	9	1.0	88	7	5, 6, 8
6	19931111_163625_starea54	20	0.7	8	8	7, 9, 10
7	19931118_023604_stareC0000280	10	1.6	130	8	7, 9, 10
8	19931118_162155_stareC0000310	33	0.9	30	7	6, 8, 9
9	19931118_162658_stareC0000311	33	0.9	40	7	6, 8, 9
10	19931118_174259_stareC0000320	28	0.9	30	7	6, 8, 9
11	TFA10_001	6.889 7	1.850 3	172.3	16	15, 17

分量	检测概率
	#17			#31
	p_f =0.001	p_f =0.01	p_f =0.1	p_f =0.001	p_f =0.01	p_f =0.1
IMF1	0.448	0.558	0.837	0.148	0.184	0.356
IMF2	0.819	0.860	0.925	0.232 3	0.396 8	0.582 7
IMF3	0.641	0.765	0.935	0.531	0.727	0.858
IMF4	0.236	0.385	0.631	0.211	0.332	0.576
IMF5	0.011	0.029	0.075	0.021	0.028	0.150

数据编号	HH		VV		HV		VH
数据编号	ASCR/dB	p_d	ASCR/dB	p_d	ASCR/dB	p_d	ASCR/dB	p_d
1	18.309	0.746 7	3.675	0.277 5	12.961	0.757 1	13.007	0.748 2
2	4.276	0.461 6	5.694	0.911 6	5.934	0.854 6	5.926	0.851 7
3	-0.343	0.597 9	1.974	0.774 2	3.637	0.854 4	3.552	0.821 8
4	6.476	0.553 5	8.173	0.847 7	7.388	0.683 9	7.362	0.853 6
5	9.533	0.098 4	10.950	0.992 6	12.890	0.934 9	12.846	0.964
6	17.977	0.999 1	8.847	1.000 0	16.143	0.996 7	16.184	1.000 0
7	3.986	0.806 8	4.397	0.981 2	7.327	0.910 3	7.360	0.914 4
8	2.261	0.304 4	-1.491	0.441 7	4.959	0.954 2	4.963	0.956
9	11.924	1.000 0	8.683	1.000 0	14.733	1.000 0	14.730	1.000 0
10	11.817	0.998 2	6.772	1.000 0	13.730	1.000 0	13.674	1.000 0
11	—	—	19.357	1.000 0	—	—	—	—

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