Distributed suboptimal Cooperative Localization for Multiple Underwater Vehicles

In this paper, we present a new approach to the problem of simultaneous distributed localization for multiple autonomous underwater vehicles(MAUVs). Cooperative localization(CL) is a crucial cycle for long range navigation of MAUVs. In the leader-follower cooperative structure, a collective estimator is processed in the form of a extended Kalman filter. Different from the normal decentralized filter that only be decomposed of the collective filter, the distributed suboptimal method proposed in this paper is to re-construct new measurements to the filter, so as to be decoupled from other sub-filters. Additionally, considering the data reuse problem in the proposed method, the improvement is also proposed. Finally, simulation results are tested to verify the advantage of the distributed suboptimal algorithm, and some comparisons are also provided.