| Special Forces play an important role in small-scale operations such as regional conflicts,unexpected events and so on.For locating the members in Special Forces in GNSS-challenged environment,this paper proposed a novel localization method-All source Cooperative Positioning(ACP).Compared with the standard cooperative positioning algorithm,the ACP not only employs the distance information for cooperative positioning,but also integrates other information such as motion information and Doppler information.In this paper,we first propose a Bayesian-based cooperative localization framework based on Bayesian estimation,and then adapt the parametric information model to represent the four types of distribution: single Gaussian distribution,mixed Gaussian distribution,single donut distribution and banana-like distribution,to obtain the prediction model of location.Secondly,the proposed Information-Fusion algorithm fusions the pseudo-range,carrier phase,neighbor distance,Doppler measurement and Time Difference of Arrival(TDOA)to refine the predicted distribution.Finally,the accuracy of the information model and the effectiveness of the information fusion model are evaluated by the simulation experiments.The performance of the ACP method is also verified.The simulation results show that the ACP method can meet the basic positioning requirement of the special-purpose soldiers in the satellite signal rejection environment.Meanwhile,the ACP method is also suitable for multi-node cooperative positioning in other scenarios,such as the multi-UAV positioning problem,implying the widely availability of the proposed algorithm. |
