基于微机电系统与径向基函数的水下航行器姿态检测

doi:10.3969/j.issn.1000-1158.2022.09.05

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Abstract In order to realize the autonomous motion control of the underwater vehicle, it is very important to master the motion attitude of underwater vehicle accurately. Current micro-electro-mechanical system (MEMS) inertial sensors have small size and low cost. It is widely used in attitude detection systems because of its low power consumption and other characteristics. Due to the complex working environment of the seabed, the underwater vehicle is set to be non-cooperative. Radial basis function neural network algorithm is used to train and classify the detection data. The experimental results show that the average recall rate of micro-electro-mechanical system inertial sensor equipped with radial basis function neural network algorithm for basic behavior recognition of underwater vehicle is 94%, which has certain engineering practical significance.

Key words： metrology attitude detection inertial sensor MEMS radial basis function neural network algorithm

Received: 12 April 2021 Published: 19 September 2022

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TB92

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	Articles by authors
	ZHANG Chun-yu
	LIU Fu-cai
	CHENG Xue-cong

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ZHANG Chun-yu,LIU Fu-cai,CHENG Xue-cong. Attitude Detection of Underwater Vehicle Based on MEMS and Radial Basis Function[J]. Acta Metrologica Sinica, 2022, 43(9): 1135-1141.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.09.05 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I9/1135

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