Discrete-time distributed Kalman filter design for formations of autonomous vehicles

Abstract

This paper addresses the problem of distributed state estimation in a multi-vehicle framework. Each vehicle aims to estimate its own state relying on locally available measurements and limited communication with other vehicles in the vicinity. The dynamics of the problem are formulated as a discrete-time Kalman filtering problem with a sparsity constraint on the gain, and two different algorithms for computation of steady-state observer gains for arbitrary fixed measurement topologies are introduced. Their application to the practical problem of distributed localization in a formation of Autonomous Underwater Vehicles (AUVs) is detailed, supported by simulation results.