基于多帧聚类的紧凑型HFSWR虚假点迹识别方法

doi:10.12305/j.issn.1001-506X.2024.02.06

摘要/Abstract

摘要：

紧凑型高频地波雷达发射功率低, 目标检测时信噪比低、虚警率高, 会产生大量虚假点迹, 影响后续目标跟踪性能。为了滤除虚假点迹, 利用目标的运动特性, 提出了一种多帧聚类与极限学习机分类两级级联的虚假点迹识别方法。首先, 利用基于最优邻域尺寸的多帧聚类方法, 将连续多帧中与待识别点迹属于同一潜在目标的点迹聚类成簇。然后, 计算簇内待识别点迹与其相邻帧内点迹的距离-多普勒速度的差分值, 以其为特征利用极限学习机辨识虚假点迹。实验结果表明, 所提方法能够准确将属于同一目标的点迹聚类, 虚假点迹识别率达到95%。

关键词: 紧凑型地波雷达, 虚假点迹识别, 多帧聚类, 极限学习机

Abstract:

Compact high-frequency surface wave radar (HFSWR) has low signal-to-noise ratio and high false alarm rate in target detection due to its low transmit power, a large number of false plots will be produced, which degrades the target tracking performance. In order to remove the false plots, a two-stage cascaded false plot identification method including multi-frame clustering module and extreme learning machine based classification module is proposed with target motion characteristics well explored. Firstly, the multi-frame plot clustering method based on optimal neighborhood size is utilized to cluster the potential plots belonging to the same target with the plot to be identified in consecutive multiple frames. Then, the differences in terms of range-Doppler velocity between the plot to be identified and plots in its neighbor frames are calculated as features, and the extreme learning machine is applied to these features to identify the false plots. Experimental results demonstrate that the proposed method can cluster the plots belonging to the same target accurately, and achieves a false plot identification rate of 95%.

Key words: compact high-frequency surface wave radar (HFSWR), false plot identification, multi-frame clustering, extreme learning machine

中图分类号:

TN953

孙伟峰, 赵林林, 纪永刚, 戴永寿. 基于多帧聚类的紧凑型HFSWR虚假点迹识别方法[J]. 系统工程与电子技术, 2024, 46(2): 419-427.

Weifeng SUN, Linlin ZHAO, Yonggang JI, Yongshou DAI. A false plot identification method based on multi-frame clustering for compact HFSWR[J]. Systems Engineering and Electronics, 2024, 46(2): 419-427.

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参考文献 31

1	SUN W F , HUANG W M , JI Y G , et al. A vessel azimuth and course joint re-estimation method for compact HFSWR[J]. IEEE Trans. on Geoscience and Remote Sensing, 2020, 58 (2): 1041- 1051. doi: 10.1109/TGRS.2019.2943065
2	CHEN J S , DAO D T , CHIEN H . Ship echo identification based on norm-constrained adaptive beamforming for an arrayed high frequency coastal radar[J]. IEEE Trans. on Geoscience and Remote Sensing, 2021, 59 (2): 1143- 1153. doi: 10.1109/TGRS.2020.3000903
3	YANG K X , ZHANG L , NIU J , et al. Analysis and estimation of shipborne HFSWR target parameters under the influence of platform motion[J]. IEEE Trans. on Geoscience and Remote Sensing, 2021, 59 (6): 4703- 4716. doi: 10.1109/TGRS.2020.3023025
4	PARK S , CHO C J , KU B , et al. Compact HF surface wave radar data generating simulator for ship detection and tracking[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14 (6): 969- 973. doi: 10.1109/LGRS.2017.2691741
5	JI Y G , WANG Y M , SUN W F , et al. Ship monitoring with bistatic compact HFSWR of small aperture[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15, 1139- 1149. doi: 10.1109/JSTARS.2022.3142008
6	SUN W F , PANG Z Z , HUANG W M , et al. Vessel velocity estimation and tracking from Doppler echoes of T/R-R composite compact HFSWR[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 4427- 4440. doi: 10.1109/JSTARS.2021.3071625
7	YANG Z Q , ZHOU H , TIAN Y W , et al. Improved CFAR detection and direction finding on time-frequency plane with high-frequency radar[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19, 3505005.
8	LYU Z , YU C J , LIU A J . Prediction method for ionospheric clutter suppression for HFSWR[J]. Electronics Letters, 2019, 55 (15): 857- 859. doi: 10.1049/el.2019.1160
9	LU B , WEN B Y , TIAN Y W , et al. A vessel detection method using compact-array HF radar[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14 (11): 2017- 2021. doi: 10.1109/LGRS.2017.2748142
10	YANG Y L , MAO X P , HOU Y G , et al. A two-step method for ionospheric clutter mitigation for HFSWR with two dimensional dual-polarized received array[J]. IEEE Access, 2020, 8, 105903- 105913. doi: 10.1109/ACCESS.2020.2999463
11	CONTE E , LOPS M . Clutter-map CFAR detection for range-spread targets in non-Gaussian clutter[J]. IEEE Trans. on Aerospace and Electronic Systems, 1997, 33 (2): 432- 443. doi: 10.1109/7.575877
12	WANG X Y , LI Y , ZHANG N , et al. An automatic target detection method based on multi-direction dictionary learning for HFSWR[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19, 3504105.
13	JANGAL F , SAILLANT S , HELIER M . Wavelet contribution to remote sensing of the sea and target detection for a high-frequency surface wave radar[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5 (3): 552- 556. doi: 10.1109/LGRS.2008.923211
14	LI Q Z , ZHANG W D , LI M , et al. Automatic detection of ship targets based on wavelet transform for HF surface wavelet radar[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14 (5): 714- 718. doi: 10.1109/LGRS.2017.2673806
15	WU M K , ZHANG L , NIU J , et al. Target detection in clutter/interference regions based on deep feature fusion for HFSWR[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 5581- 5595. doi: 10.1109/JSTARS.2021.3082044
16	ZHANG W D , LI Q Z , WU Q M J , et al. A novel ship target detection algorithm based on error self-adjustment extreme learning machine and cascade classifier[J]. Cognitive Computation, 2019, 11 (1): 110- 124. doi: 10.1007/s12559-018-9606-5
17	林强, 彭威, 胡先进. 基于改进KNN的雷达点迹真伪鉴别方法[J]. 现代雷达, 2020, 42 (4): 41- 45.
	LIN Q , PENG W , HU X J . Method of radar plot true and false identification based on improved KNN[J]. Modern Radar, 2020, 42 (4): 41- 45.
18	袁子寅. 天波雷达数据预处理与数据关联技术研究[D]. 哈尔滨: 哈尔滨工业大学, 2016.
	YUAN Z Y. Research on OTHR data pre-processing and data association technology[D]. Harbin: Harbin Institute of Technology, 2016.
19	CHENG Y, ZHAO Y. Radar false alarm plots elimination based on multi-feature evaluation[C]//Proc. of the 5th International Conference on Intelligent Autonomous Systems, 2022: 72-77.
20	张迪. 复杂条件下雷达点迹处理方法研究[D]. 西安: 西安电子科技大学, 2020.
	ZHANG D. Research on radar plot processing under complex conditions[D]. Xi'an: Xidian University, 2020.
21	张佳琦, 陶海红, 张修社, 等. 一种利用量测空间聚类的多帧检测前跟踪算法[J]. 西安电子科技大学学报, 2021, 48 (5): 231- 238.
	ZHANG J Q , TAO H H , ZHANG X S , et al. A multi-frame track before detect algorithm utilizing measurement space clustering[J]. Journal of Xidian University, 2021, 48 (5): 231- 238.
22	张佳琦, 陶海红, 张修社. 量测点迹空间聚类的多传感器多帧检测算法[J]. 系统工程与电子技术, 2021, 43 (6): 1533- 1540.
	ZHANG J Q , TAO H H , ZHANG X S . A multi-sensor multi-frame detection algorithm based on measurement plots space clustering[J]. Systems Engineering and Electronics, 2021, 43 (6): 1533- 1540.
23	ESTER M, KRIEDGEL H P, SANDER J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proc. of the 2nd International Conference on Knowledge Discovery and Data Mining, 1996: 226-231.
24	NIE F P , XUE J J , WU D Y , et al. Coordinate descent method for k-means[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2022, 44 (5): 2371- 2385.
25	FENG H L , DONG J Y . Reliability analysis for WSN based on a modular k-out-of-n system[J]. Journal of Systems Engineering and Electronics, 2017, 28 (2): 407- 412.
26	AREF Y , CEMAL K , ASEF Y , et al. Automatic fuzzy-DBSCAN algorithm for morphological and overlapping datasets[J]. Journal of Systems Engineering and Electronics, 2020, 31 (6): 1245- 1253. doi: 10.23919/JSEE.2020.000095
27	SMOLA A J , LKOPF B S . A tutorial on support vector regression[J]. Statistics and Computing, 2004, 14 (3): 199- 222.
28	RUMELHART D E , HINTON G E , WILLIAMS R J . Learning representations by back-propagating Errors[J]. Nature, 1986, 323 (6088): 533- 536. doi: 10.1038/323533a0
29	HUANG G B , ZHU Q Y , SIEW C K . Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70 (1/3): 489- 501.
30	ZHANG L, XIONG W, NIU J, et al. Track matching based on ELM for HFSWR[C]//Proc. of the Chinese Automation Congress, 2019: 1753-1757.
31	SUN W F, HUANG W M, JI Y G, et al. Vessel tracking with small-aperture compact high-frequency surface wave radar[C]// Proc. of the IEEE Oceans Marseille, 2019.

方法	C_T/%	C_F/%	平均每帧所耗时间/ms
DBSCAN	99.6	15.9	108
MFCONS(1层)	99.4	7.36	27.8
MFCONS(2层)	99.6	13.7	35
MFCONS(3层)	99.7	14.6	40.5

方法	Pf/%	Ft/%	平均每帧所耗时间/ms
ELM	1.15	96.51	6.15
BP	2.25	96.25	20.5
SVM	1.20	96.17	25.6

方法	Pf/%	Ft/%	平均每帧所耗时间/ms
本文方法	1.15	96.45	50.05
文献[18]方法	2.1	94.13	120.5
文献[19]方法	3.31	93.20	75.6

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