Simultaneous Unknown Input and State Estimation for the Linear System with a Rank-Deficient Distribution Matrix

The classical recursive three-step filter can be used to estimate the state and unknown input when the system is affected by unknown input, but the recursive three-step filter cannot be applied when the unknown input distribution matrix is not of full column rank. In order to solve the above problem, this paper proposes two novel filters according to the linear minimum-variance unbiased estimation criterion. Firstly, while the unknown input distribution matrix in the output equation is not of full column rank, a novel recursive three-step filter with direct feedthrough was proposed. Then, a novel recursive three-step filter was developed when the unknown input distribution matrix in the system equation is not of full column rank. Finally, the specific recursive steps of the corresponding filters are summarized. And the simulation results show that the proposed filters can effectively estimate the system state and unknown input.

Automated summary

This paper proposes two novel filters based on the linear minimum-variance unbiased estimation criterion to address the issue of the classical recursive three-step filter's inability to be applied in certain scenarios. One filter is designed for cases where the unknown input distribution matrix in the output equation is not of full column rank, while the other filter is developed for situations where the unknown input distribution matrix in the system equation is not of full column rank. Simulation results demonstrate that the proposed filters effectively estimate the system state and unknown input.