Abstract
Localization of autonomous underwater vehicles (AUVs) is a very important and challenging task for the AUVs applications. In long baseline underwater acoustic localization networks, the accuracy of single-way range measurements is the key factor for the precision of localization of AUVs, whether it is based on the way of time of arrival (TOA), time difference of arrival (TDOA), or angle of arrival (AOA). The single-way range measurements do not depend on water quality and can be taken from long distances; however, there are some limitations which exist in these measurements, such as the disturbance of the unknown current velocity and the outliers caused by sensors and errors of algorithm. To deal with these problems, an AUV self-localization algorithm based on particle swarm optimization (PSO) of outliers elimination is proposed, which improves the performance of angle of arrival (AOA) localization algorithm by taking account of effects of the current on the positioning accuracy and eliminating possible outliers during the localization process. Some simulation experiments are carried out to illustrate the performance of the proposed method compared with another localization algorithm.
Highlights
In a three-dimensional underwater environment, the problem of localization of autonomous underwater vehicles (AUVs) has been widely investigated in recent years, as they make it accessible to those untouchable areas for human beings and assist with complex and arduous underwater tasks, which has important theory and application value in various robotic applications, such as the underwater target detection [1], the underwater target tracking [2, 3], and underwater search and rescue missions [4, 5]
A particle swarm optimization (PSO) algorithm based on the outliers elimination is proposed in this paper and the details will be given in the following paragraphs
Localization of autonomous underwater vehicles (AUVs) is investigated in this paper, and a single-way range localization approach based on PSO of outliers elimination is proposed
Summary
Localization of autonomous underwater vehicles (AUVs) is a very important and challenging task for the AUVs applications. The single-way range measurements do not depend on water quality and can be taken from long distances; there are some limitations which exist in these measurements, such as the disturbance of the unknown current velocity and the outliers caused by sensors and errors of algorithm. To deal with these problems, an AUV self-localization algorithm based on particle swarm optimization (PSO) of outliers elimination is proposed, which improves the performance of angle of arrival (AOA) localization algorithm by taking account of effects of the current on the positioning accuracy and eliminating possible outliers during the localization process. Some simulation experiments are carried out to illustrate the performance of the proposed method compared with another localization algorithm
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