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In this letter, we propose a heuristic method to address sensor bias estimation to improve track-to-track association accuracy. A novel multi-parameter cost function is derived from rigid transformation function and it is minimized by the covariance matrix adaptation evolution strategies algorithm.

We study the problem of distributed filtering for state space models over networks, which aims to collaboratively estimate the states by a network of nodes. Most of existing works on this problem assume that both process and measurement noises are Gaussian and their covariances are known in advance. In some cases, this assumption breaks down and no longer holds.

A key challenge in designing distributed particle filters is to minimize the communication overhead without compromising tracking performance. In this paper, we present two distributed particle filters that achieve robust performance with low communication overhead.

This paper studies resilient distributed estimation under measurement attacks. A set of agents each makes successive local, linear, noisy measurements of an unknown vector field collected in a vector parameter. The local measurement models are heterogeneous across agents and may be locally unobservable for the unknown parameter.