Distributed Cubature Kalman Filter Cooperative Localization Based on Parameterized-belief Propagation

Distributed Cubature Kalman Filter Cooperative Localization Based on Parameterized-belief Propagation

Location awareness has been widely used for cooperative localization of target in wireless sensor networks (WSN). But as the number of agent node increases, cooperative localization based on nonparametric belief propagation (BP) causes high communication and computation burden. In addition, high localization accuracy is also the goal of this paper. To this end, this paper based on parameterized BP strategy proposed a distributed cubature Kalman filter (DCKF) algorithm named BP-DCKF. Firstly, basing on joint posterior probability density function of all nodes, this paper constructs a factor graph (FG) model, then the edge posterior distribution of the nodes is obtained by BP strategy. Secondly, considering Gaussian parameterized BP which reconstruct the parameterized message of agents transmitting, this paper proposed the improved DCKF method, moreover, obtaining the locating model of agent node related to posterior distribution of each node in the FG. Finally, based on the localization model, the location estimation of mobile agent node can be obtained utilizing DCKF method to iteratively solve the edge posterior distribution of agent node. Simulation results show that comparing with traditional algorithm, the proposed algorithm is higher on localization accuracy, lower on communication burden.