基于角域特征PSO的海面目标HRRP识别方法

doi:10.12305/j.issn.1001-506X.2023.06.07

Abstract

Abstract:

To solve the problems of serious aliasing of various sea surface target features in feature space and low utilization rate of angle features in high resolution range profile(HRRP), a method for recognizing sea surface target HRRP based on angle domain feature particle swarm optimization(PSO) is proposed. In this method, HRRP angle information is introduced to optimize the feature space and increase the overall separability of the feature space. In order to improve the local separability of feature space, adaptive frame segmentation algorithm is used to divide feature space into angle domains. At the same time, the PSO algorithm is used to determine the optimal minimum number of samples per frame in the angular division of feature space, which enhances the robustness and applicability of the method. Experimental results show that the combination of feature space optimization and region division can effectively improve the classification and recognition performance of multi-class sea surface targets. PSO algorithm can effectively enhance the anti-error and anti-noise robustness of the method.

Key words: sea surface target recognition, high resolution range profile(HRRP), feature space optimization, adaptive frame segmentation, particle swarm optimization(PSO) algorithm

CLC Number:

TN957.1

Zhehao WANG, Tao JIAN, Xiaodong HUANG, Haipeng WANG, Yu LIU. HRRP recognition method for sea surface targets based on angular domain feature PSO[J]. Systems Engineering and Electronics, 2023, 45(6): 1642-1650.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Table 1

Fig.9

References 30

1	WEI C, LIANG Z, YING X, et al. Polarimetric HRRP target re-cognition based on convlstm[C]//Proc. of the IEEE International Geoscience and Remote Sensing Symposium, 2019: 334-337.
2	GUO P C , LIU Z , WANG J J . Radar group target recognition based on HRRPs and weighted mean shift clustering[J]. Journal of Systems Engineering and Electronics, 2020, 31 (6): 1152- 1159. doi: 10.23919/JSEE.2020.000087
3	LIU W B , YUAN J W , ZHANG G , et al. HRRP target recognition based on kernel joint discriminant analysis[J]. Journal of Systems Engineering and Electronics, 2019, 30 (5): 703- 708.
4	LIN Y Q, CAI B A, LIN C L, et al. Feature space optimization of multispectral imagery and LiDAR waveform data[C]//Proc. of the IEEE International Geoscience and Remote Sensing Symposium, 2015: 589-592.
5	LI L , LIU Z , LI T . Radar high resolution range profile recognition via multi-SV method[J]. Journal of Systems Engineering and Electronics, 2017, 28 (5): 879- 889. doi: 10.21629/JSEE.2017.05.07
6	杜兰, 刘宏伟, 保铮. 利用目标方位信息改善雷达距离像识别性能[J]. 系统工程与电子技术, 2004, 26 (8): 1040- 1043. doi: 10.3321/j.issn:1001-506X.2004.08.010
	DU L , LIU H W , BAO Z . Improving the performance of radar range image recognition using target azimuth information[J]. Systems Engineering and Electronics, 2004, 26 (8): 1040- 1043. doi: 10.3321/j.issn:1001-506X.2004.08.010
7	袁莉, 刘宏伟, 保铮. 雷达高分辨距离像分类器的参数自适应学习算法[J]. 电子与信息学报, 2008, 4 (1): 198- 202.
	YUAN L , LIU H W , BAO Z . A parameter adaptive learning algorithm for radar high-resolution range image classifier[J]. Electronics and Information Technology, 2008, 4 (1): 198- 202.
8	LU J B, TIAN S S, XI Z M. Frame segmentation and recognition algorithm for ship's HRRPs based on hypothesis testing[C]//Proc. of the CIE International Conference on Radar, 2016.
9	刘敬, 张军英, 杜兰. 基于相关系数的雷达高分辨距离像分帧方法[J]. 电子与信息学报, 2008, 4 (9): 2060- 2064.
	LIU J , ZHANG J Y , DU L . High resolution range image framing method for radar based on correlation coefficient[J]. Electronics and Information Technology, 2008, 4 (9): 2060- 2064.
10	但波, 姜永华, 李敬军, 等. 雷达高分辨距离像自适应角域划分方法[J]. 系统工程与电子技术, 2014, 36 (11): 2178- 2185. doi: 10.3969/j.issn.1001-506X.2014.11.11
	DAN B , JIANG Y H , LI J J , et al. Adaptive angle domain division method for radar high resolution range profile[J]. Systems Engineering and Electronics, 2014, 36 (11): 2178- 2185. doi: 10.3969/j.issn.1001-506X.2014.11.11
11	WANG G S, WANG W Y, LEI Z Y, et al. A method to improve the generalization ability of HRRP recognition model-deep adaptation networks[C]//Proc. of the IEEE 4th Information Technology Networking Electronic and Automation Control Conference, 2020: 79-803.
12	SLOMKA S, GIBBINS D, GRAY D, et al. Features for high resolution radar range profile based ship classification[C]//Proc. of the International Symposium on Signal Processing, 1999: 329-332.
13	WU J, ZHOU J J, ZHU J H. A radar target recognition method based on wavelet power spectrum and power offset[C]//Proc. of the International Asia Conference on Informatics in Control, 2010: 130-134.
14	WANG L Y, CUI Z Y, CAO Z J, et al. Fine-grained gesture recognition based on high resolution range profiles of terahertz radar[C]//Proc. of the IEEE International Geoscience and Remote Sensing Symposium, 2019: 1470-1473.
15	WANG Y. MING H. An integration method for detection and classification of sea surface targets[C]//Proc. of the IET International Radar Conference, 2020: 990-993.
16	UPADHYAY P C, KARANAM L, LORY J A, et al. Classi-fying cover crop residue from RGB images: a simple SVM versus a SVM ensemble[C]//Proc. of the IEEE Symposium Series on Computational Intelligence, 2021.
17	LU F G, CHEN S C, LIU J S, et al. Achieving target identification for the MMW seeker based on scanning matching and beam pointing[C]//Proc. of the IEEE International Geoscience and Remote Sensing Symposium, 2019: 2236-2239.
18	ZHANG R , XI Z W , LI X , et al. Analysis about the speckle of radar high resolution range profile[J]. Science China Technological Sciences, 2011, 54 (1): 226- 236. doi: 10.1007/s11431-010-4207-x
19	DU L , LIU H W , ZHENG B . Radar HRRP statistical recognition: parametric model and model selection[J]. IEEE Trans.on Signal Processing, 2008, 56 (5): 1931- 1944. doi: 10.1109/TSP.2007.912283
20	MATEJKA S. Noise power ratio calculation based on the power spectra measurement[C]//Proc. of the 19th International Conference Radio elektronika, 2009: 239-242.
21	LI J H, KUAI Z Z, ZHAI M. Research on space-based sequence image recognition based on markov model[C]//Proc. of the Chinese Automation Congress, 2020: 4799-4803.
22	黄孟俊, 陈建军, 赵宏钟. 海上目标航向航速解算新方法[J]. 信号处理, 2011, 27 (7): 1034- 1038. doi: 10.3969/j.issn.1003-0530.2011.07.012
	HUANG M J , CHEN J J , ZHAO H Z . A new method for calculating sea target course speed[J]. Signal Processing, 2011, 27 (7): 1034- 1038. doi: 10.3969/j.issn.1003-0530.2011.07.012
23	XING Y , YOU P , WANG H Q , et al. Adaptive translational motion compensation method for rotational parameter estimation under low SNR based on HRRP[J]. IEEE Sensors Journal, 2019, 19 (7): 2553- 2561. doi: 10.1109/JSEN.2018.2889752
24	HUANG C L , DUN J F . A distributed PSO-SVM hybrid system with feature selection and parameter optimization[J]. Applied Soft Computing, 2008, 8 (4): 1381- 1391. doi: 10.1016/j.asoc.2007.10.007
25	ROSA W D , GEREZ C , BELATI E . Optimal distributed generation allocating using particle swarm optimization and linearized AC load flow[J]. IEEE Latin America Transactions, 2019, 16 (10): 2665- 2670.
26	YANG Y N, LU H P. Short-term traffic flow combined forecasting model based on SVM[C]//Proc. of the International Conference on Computational and Information Sciences, 2011: 262-265.
27	SUN J , LI Q , CHEN M Q , et al. Optimization of models for a rapid identification of lithology while drilling-A win-win strategy based on machine learning[J]. Journal of Petroleum Science & Engineering, 2019, 176, 321- 341.
28	WANG X H . Multi-class SVMs based on probability voting strategy and its application[J]. Computer Engineering, 2009, 35 (2): 180- 183.
29	JOSHI R , PRASAD R , MEWADA P , et al. Modified LDA approach for cluster based gene classification using K-mean method[J]. Procedia Computer Science, 2020, 171, 2493- 2500.
30	ZHANG S Z, SUN P F. A new short-text categorization algorithm based on improved KSVM[C]//Proc. of the IEEE 3rd International Conference on Communication Software and Networks, 2011: 154-157.

平均识别率/%	特征空间优化前			特征空间优化后
平均识别率/%	K-mean	未优化SVM	未优化AFS-SVM	优化SVM	优化AFS-SVM (本文方法)
0°误差	20.00	20.00	35.26	46.11	96.60
5°误差	20.00	20.00	29.06	45.83	94.47
10°误差	20.00	20.00	27.64	45.56	89.69
15°误差	20.00	20.00	27.04	44.72	86.23
算法运行时间/s	0.29	2.26	2.01	9.94	1.99

HRRP recognition method for sea surface targets based on angular domain feature PSO

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 30

Related Articles 0

Recommended Articles

Metrics

Comments