An alternative approach for distributed parameter estimation under Gaussian settings
This work addresses distributed estimation for sensor networks, but it appears incremental as it builds on existing consensus and innovation concepts.
The paper tackles distributed linear parameter estimation in multi-agent networks with Gaussian assumptions by developing a novel algorithm that fuses consensus and innovation terms. It achieves consistent estimates with fast convergence under a new distributed parameter observability condition.
This paper takes a different approach for the distributed linear parameter estimation over a multi-agent network. The parameter vector is considered to be stochastic with a Gaussian distribution. The sensor measurements at each agent are linear and corrupted with additive white Gaussian noise. Under such settings, this paper presents a novel distributed estimation algorithm that fuses the the concepts of consensus and innovations by incorporating the consensus terms (of neighboring estimates) into the innovation terms. Under the assumption of distributed parameter observability, introduced in this paper, we design the optimal gain matrices such that the distributed estimates are consistent and achieves fast convergence.