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Front. Neurorobot. | doi: 10.3389/fnbot.2019.00019

Solving Gravity Anomaly Matching Problem under Large Initial Errors in Gravity Aided Navigation by Using an Affine Transformation Based Artificial Bee Colony Algorithm

 Tian Dai1*, Lingjuan Miao1, Haijun Shao1 and Yongsheng Shi1
  • 1Beijing Institute of Technology, China

Gravity aided inertial navigation system (GAINS), which uses earth gravitational anomaly field for navigation, holds strong potential as an underwater navigation system. The gravity matching algorithm is one of the key factors in GAINS. Existing matching algorithms cannot guarantee the matching accuracy in the matching algorithms based gravity aided navigation when the initial errors are large. Evolutionary algorithms, which are mostly have the ability of global optimality and fast convergence, can be used to solve the gravity matching problem under large initial errors. However, simply applying evolutionary algorithms to GAINS may lead to false matching. Therefore, in order to deal with the underwater gravity matching problem, it is necessary to improve the traditional evolutionary algorithms. In this paper, an affine transformation based artificial bee colony (ABC) algorithm, which can greatly improve the positioning precision under large initial errors condition, is developed. The proposed algorithm introduces affine transformation to both initialization process and evolutionary process of ABC algorithm. The single-point matching strategy is replaced by the strategy of matching a sequence of several consecutive position vectors. In addition, several constraints are introduced to the process of evolution by using the output characteristics of the inertial navigation system (INS). Simulations based on the actual gravity anomaly base map have been performed for the validation of the proposed algorithm.

Keywords: Gravity aided navigation, bio-inspired navigation, navigation systems, Optimization & artificial technique, Underwater vehicle (UV), evolutionary algorithm

Received: 16 Nov 2018; Accepted: 17 Apr 2019.

Edited by:

Xiaofeng Xiong, University of Southern Denmark, Denmark

Reviewed by:

Ning Sun, Nankai University, China
Chin-Shiuh Shieh, National Kaohsiung First University of Science and Technology, Taiwan  

Copyright: © 2019 Dai, Miao, Shao and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Tian Dai, Beijing Institute of Technology, Beijing, China, daitian@bit.edu.cn