Cooperative localization for multiple AUVs based on the rough estimation of the measurements

Abstract

The accuracy of cooperative localization for multiple autonomous underwater vehicles (AUVs) equipped with low precise proprioceptive localization sensors can be improved by using relative location information between individuals and Bayesian filtering. However, when the relative location measurement errors are high, its accuracy will be reduced. Two measurement for rough estimation algorithms under the constraint environment of the cooperative structure are developed in this paper: the first algorithm is based on the underwater acoustic isotropic transmission. And the second algorithm is based on the common observation environment. In the first algorithm, it builds under the assumption that the distance errors calculated from the simultaneous omnidirectional response signals from the same transmitting source have correlated. Similarly, in the second algorithm, the assumption that “common observation environment” is correlated is made. First, the correlation between the errors is used roughly to estimate the measurement of information. Then, a suitable filter is applied to fuse the rough estimation measurement with dead-reckoning estimation that improves the location estimation accuracy. The final simulation, by changing the AUV formation navigation paths and the sensor observation noises, shows the proposed processing methods have effectiveness and consistency compared to the traditional algorithm.