Consensus-based distributed moving horizon estimation with constraints

This paper concerns distributed state estimation for linear systems over a sensor network. Two novel algorithms utilizing the framework of moving horizon estimation (MHE) are proposed, which are fully distributed, scalable and allow for taking into account constraints on the noises and the system states. The proposed methods estimate the state by minimizing a local quadratic objective function, which can be efficiently solved by quadratic programming (QP). Consensus technique is employed to fuse information to construct the local quadratic objective function for each node. The first algorithm, which minimizes a quadratic function involving consensus on measurement costs (CM), approaches the centralized MHE with a sufficiently large number of consensus steps. The second one involving consensus both on arrival costs (CA) and measurement costs, enjoys the benefits of both the CA and CM. To avoid directly running consensus on some functions, a novel consensus strategy for the CM is developed. The estimation errors of the proposed methods are proven to be stochastically ultimately bounded under certain conditions. Finally, numerical results are presented to verify the effectiveness of the developed algorithms.

Automated summary

This paper proposes two novel algorithms for distributed state estimation for linear systems over a sensor network, utilizing the framework of Moving Horizon Estimation (MHE). The algorithms are fully distributed, scalable, and consider constraints on the noises and system states. The algorithms estimate the state by minimizing a local quadratic objective function, which can be efficiently solved using quadratic programming (QP). Consensus technique is employed to fuse information and construct the local quadratic objective function for each node. The effectiveness of the algorithms is verified through numerical results.