System error iterative identification for underwater positioning based on spectral clustering

doi:10.23919/JSEE.2024.000069

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (4): 1028-1041.doi: 10.23919/JSEE.2024.000069

• CONTROL THEORY AND APPLICATION • Previous Articles

System error iterative identification for underwater positioning based on spectral clustering

Yu LU(), Jiongqi WANG(), Zhangming HE(), Haiyin ZHOU(), Yao XING(), Xuanying ZHOU()

College of Sciences, National University of Defense Technology, Changsha 410073, China

Received:2022-08-11 Online:2024-08-18 Published:2024-08-06
Contact: Zhangming HE E-mail:luyu_gfkd@163.com;wjq_gfkd@163.com;hzmnudt@163.com;gfkd_zhy@sina.com;y_1326@163.com;Julia_chow07@163.com
About author:
LU Yu was born in 1997. He received his B.S. degree in information and computational science from Shandong University, Ji’nan, China, in 2020. Currently, he is an M.S. candidate at the College of Sciences, National University of Defense Technology. His main research interests include data fusion and underwater acoustic positioning. E-mail: luyu_gfkd@163.com

WANG Jiongqi was born in 1979. He received his B.S. degree in applied mathematics from Zhejiang University, Hangzhou, China, in 2002 and M.S. and Ph.D. degrees in applied mathematics from National University of Defense Technology, in 2004 and 2008, respectively. He is a professor in the College of Sciences, National University of Defense Technology, Changsha, China. His main research interests include measurement data analysis, parameter estimation, system identification, and space target state filter and its applications. E-mail: wjq_gfkd@163.com

HE Zhangming was born in 1985. He received his B.S. and M.S. degrees in applied mathematics from National University of Defense Technology, Changsha, China, in 2008 and 2010, respectively. From 2013 to 2014, he was a visiting scholar in Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Duisburg, Germany. He received his Ph.D. degree in system science from National University of Defense Technology, in 2015. He is an associate professor in the College of Sciences, National University of Defense Technology, Changsha, China. His main research interests include fault diagnosis and prognosis, signal processing, and system identification. E-mail: hzmnudt@163.com

ZHOU Haiyin was born in 1965. He received his B.S. degree in applied mathematics from Wuhan University, Wuhan, China, in 1986, M.S. degree from Hunan University, Changsha, China in 1989 and Ph.D. degree in systems engineering from National University of Defense Technology, Changsha, China, in 2003. Since 2009, he has been a professor in the College of Sciences, National University of Defense Technology. His main research interests include data driven diagnosis approaches, power system dynamics and control, advanced signal processing, and data information fusion and its application. E-mail: gfkd_zhy@sina.com

XING Yao was born in 1997. He received his B.S. degree in applied mathematics from National University of Defense Technology, Changsha, China, in 2020. Currently, he is an M.S. candidate at the College of Sciences, National University of Defense Technology. His main research interests are data fusion, systematic error identification, and target localization using underwater sound. E-mail: y_1326@163.com

ZHOU Xuanying was born in 1991. She received her B.S., M.S., and Ph.D. degrees in applied mathematics respectively in 2013, 2016, and 2019 from National University of Defense Technology, Changsha, China. She is a lecturer at the College of Sciences, National University of Defense Technology, Changsha, China. Her main research interests include system modelling, missiles, and signal process and its applications. E-mail: Julia_chow07@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (61903086;61903366;62001115), the Natural Science Foundation of Hunan Province (2019JJ50745;2020JJ4280;2021JJ40133), and the Fundamentals and Basic of Applications Research Foundation of Guangdong Province (2019A1515110136).

Abstract

Abstract:

The observation error model of the underwater acoustic positioning system is an important factor to influence the positioning accuracy of the underwater target. For the position inconsistency error caused by considering the underwater target as a mass point, as well as the observation system error, the traditional error model best estimation trajectory (EMBET) with little observed data and too many parameters can lead to the ill-condition of the parameter model. In this paper, a multi-station fusion system error model based on the optimal polynomial constraint is constructed, and the corresponding observation system error identification based on improved spectral clustering is designed. Firstly, the reduced parameter unified modeling for the underwater target position parameters and the system error is achieved through the polynomial optimization. Then a multi-station non-oriented graph network is established, which can address the problem of the inaccurate identification for the system errors. Moreover, the similarity matrix of the spectral clustering is improved, and the iterative identification for the system errors based on the improved spectral clustering is proposed. Finally, the comprehensive measured data of long baseline lake test and sea test show that the proposed method can accurately identify the system errors, and moreover can improve the positioning accuracy for the underwater target positioning.

Key words: acoustic positioning, reduced parameter, system error identification, improved spectral clustering, accuracy analysis

Yu LU, Jiongqi WANG, Zhangming HE, Haiyin ZHOU, Yao XING, Xuanying ZHOU. System error iterative identification for underwater positioning based on spectral clustering[J]. Journal of Systems Engineering and Electronics, 2024, 35(4): 1028-1041.

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Test scenario	Polynomial order	RSS	$ {n_\beta } $	$Q$	$ S $
Lake test (m=11, N=9999)	1	1423.70	18	109971	311.28
	2	1129.02	21	109968	16.66
	3	1112.54	24	109965	0.24
	4	1125.68	27	109962	13.45
Sea test (m=9, N=8460)	1	1456.43	16	76124	618.73
	2	1402.20	19	76121	564.57
	3	837.81	22	76118	0.24
	4	845.77	25	76115	8.26

Test scenario	Method	$\hat \sigma $/m	$\hat \theta $/(°)	GDOP	$\eta $/m
Lake test	SEI-ISC	0.0982	179.97	4.8129	0.4727
	Method 1	0.3293	−	4.9212	1.6207
	Method 2	0.4327	74.57	5.2131	2.2560
	Method 3	0.1304	55.80	4.7909	0.6248
	Method 4	0.1232	55.90	4.7904	0.5904
Sea test	SEI-ISC	0.1017	180.24	4.4101	0.4487
	Method 1	0.3397	−	4.2362	1.4392
	Method 2	0.6546	55.59	4.0927	2.6792
	Method 3	0.1816	158.27	4.4894	0.8154
	Method 4	0.1480	152.39	4.3844	0.6490

System error iterative identification for underwater positioning based on spectral clustering

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