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This paper addresses the problem of estimating the states of a group of agents from noisy measurements of pairwise differences between agents' states. The agents can be viewed as nodes in a graph and the relative measurements between agents as the graph's edges. We propose a new distributed algorithm that exploits the existence of cycles in the graph to… (More)

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… (More)

We address the problem of estimating the state of a multi-agent system based on measurements corrupted by impulsive noise and whose dynamics are subject to impulsive disturbances. The qualifier " impulsive " refers to the fact that noise and disturbances are relatively small most of the time, but occasionally take large values. Noise and disturbances are… (More)

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