Approximate Distributed Kalman Filtering for Cooperative Multi-agent Localization

Abstract. We consider the problem of estimating the locations of mobile agents by fusing the measurements of displacements of the agents as well as relative position measurements between pairs of agents. We propose an algorithm that computes an approximation of the centralized optimal (Kalman filter) estimates. The algorithm is distributed in the sense each agent can estimate its own position by communication only with nearby agents. The problem of distributed Kalman filtering for this application is reformulated as a parameter estimation problem. The graph structure underlying the reformulated problem makes it computable in a distributed manner using iterative methods of solving linear equations. With finite memory and limited number of iterations before new measurements are obtained, the algorithm produces an approximation of the Kalman filter estimates. As the memory of each agent and the number of iterations between each time step are increased, the approximation improves. Simulati...

• Type: Conference
• Year: 2010
• Where: DCOSS
• Authors: Prabir Barooah, Wm. Joshua Russell, João P. Hespanha
• Keywords: Centralized Optimal, DCOSS 2010, Distributed And Parallel Computing, Kalman Filter, Relative Position Measurements