Localization of autonomous underwater vehicles (AUVs) is a very important and challenging task for the AUVs applications. In\nlong baseline underwater acoustic localization networks, the accuracy of single-way range measurements is the key factor for the\nprecision of localization of AUVs, whether it is based on the way of time of arrival (TOA), time difference of arrival (TDOA), or\nangle of arrival (AOA). The single-way range measurements do not depend on water quality and can be taken from long distances;\nhowever, there are some limitationswhich exist in thesemeasurements, such as the disturbance of the unknown current velocity and\nthe outliers caused by sensors and errors of algorithm. To deal with these problems, an AUV self-localization algorithm based on\nparticle swarm optimization (PSO) of outliers elimination is proposed, which improves the performance of angle of arrival (AOA)\nlocalization algorithm by taking account of effects of the current on the positioning accuracy and eliminating possible outliers\nduring the localization process. Some simulation experiments are carried out to illustrate the performance of the proposed method\ncompared with another localization algorithm
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