Entangled Kalman filters for cooperative estimation

In this paper we propose a distributed estimation scheme for tracking the state of a Gauss-Markov model by means of independent observations at sensors connected in a network. Our emphasis is on low communication demands to alleviate the burden on eventually battery-powered sensors, which will limit the achievable performance with respect to an ideal centralized Kalman filter with access to all sensors measurements. The cooperation is performed in a distributed way to guarantee scalability and robustness to failures, and it is designed to reduce the detrimental effects of the channel noise on the sensor exchanges.