Abstract
With the increasing exploration of the marine resources, cooperative navigation (CN) of multi autonomous underwater vehicles (AUVs) can improve exploration efficiency and accuracy. In order to improve the positioning accuracy in the CN, we propose the Information Entropy (IE) algorithm, which can evaluate the measurement informations, and choosing the optimal measurement informations to update the positions of the AUVs, decreasing the influences of the low quality measurement informations to the CN. In this paper, we study the influences of choosing different AUVs’ states and the amounts of the measurement informations, and we consider two scenarios, the first is that AUVs equip the equal accuracy navigation equipment; the second is that AUVs equip the different accuracy navigation equipment, which there are several AUVs have the high accuracy equipment, and the others have the low accuracy equipment. Combining with the nonlinear filtering technique, we find using the IE algorithm can reduce the positioning errors of the AUVs compared with selecting measurement informations randomly by the simulation results.
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