Abstract
We address the problem of estimating the state of a multiagent system based on measurements corrupted by impulsive noise and whose dynamics are subjected 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 modeled as mixtures of Gaussian and Laplacian processes, leading to a maximum-likelihood estimator that can be computed by solving a convex sum-of-norms optimization that can be solved online very efficiently. The approach has been validated both in simulation using synthetic data and in real hardware using a team of unmanned air vehicles equipped with an onboard video camera, inertial sensors, and Global Positioning System to cooperatively geolocate and track a ground-moving target agent.
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