Robust Error Square Constrained Filter Design for Systems With Non-Gaussian Noises

In this letter, an error square constrained filtering problem is considered for systems with both non-Gaussian noises and polytopic uncertainty. A novel filter is developed to estimate the systems states based on the current observation and known deterministic input signals. A free parameter is introduced in the filter to handle the uncertain input matrix in the known deterministic input term. In addition, unlike the existing variance constrained filters, which are constructed by the previous observation, the filter is formed from the current observation. A time-varying linear matrix inequality (LMI) approach is used to derive an upper bound of the state estimation error square. The optimal bound is obtained by solving a convex optimization problem via semi-definite programming (SDP) approach. Simulation results are provided to demonstrate the effectiveness of the proposed method.