A Scalable Modularized Synthesis Method for Distributed Kalman Filters*

Abstract

This paper presents a scheme to construct distributed observers for a system consisting of agents interconnected in a graph structure. The scheme is an iterative procedure to improve the observers with respect to global performance. It is modular in the sense that each agent iterates using only local model information. As a consequence, the complexity of the scheme scales linearly with the size of the system. The resulting observers estimate states for each agent using only local measurements and model knowledge of its neighbors. Distributed observers are suboptimal to centralized ones and it is desirable to measure the amount of performance degradation. We show how to use the variables of the synthesis scheme to also determine such a measure of the suboptimality.

A decentralized optimal LQ state observer based on an augmented Lagrangian approach

“…The design of decentralized or distributed state estimation or control algorithms is an important research topic which has attracted a great interest for more than 30 years [1][2][3][4][5][6][7][8][9][10][11]. On the one hand, estimation of large-scale complex systems is of the greatest importance for monitoring applications in various engineering fields, such as power grids, road traffic networks, environmental systems (open-channel hydraulic systems, water supply networks, weather, ...).…”