Kalman-Like Filter under Binary Sensors

TL;DR

This paper develops a Kalman-like filtering approach for systems with binary sensors, introducing a novel measurement model and optimal filter design for both linear and nonlinear systems, validated through simulations.

Contribution

It proposes a new uncertain measurement model for binary sensors and derives optimal filters with adjustable parameters for linear and nonlinear systems.

Findings

Effective estimation of $O_2$ content demonstrated

Optimal filter gain minimizes estimation error

Method outperforms existing approaches in simulations

Abstract

This paper is concerned with the linear/nonlinear Kalman-like filtering problem under binary sensors. Since innovation represents new information in the sensor measurement and serves to correct the prediction for the Kalman-like filter (KLF), a novel uncertain measurement model is proposed such that the innovation generated from binary sensor can be captured. When considering linear dynamic systems, a conservative estimation error covariance with adjustable parameters is constructed by matrix inequality, and then an optimal filter gain is derived by minimizing its trace. Meanwhile, the optimal selection criterion of an adjustable parameter is developed by minimizing the upper bound of the conservative estimation error covariance. When considering nonlinear dynamic systems, a conservative estimation error covariance with adjustable parameters is also constructed via unscented transform and matrix inequalities. Then, following the idea of designing KLF in linear dynamic systems, the nonlinear filter gain and the optimal adjustable parameter are designed. Finally, $O_2$ content estimation and nonlinear numerical system are employed to show the effectiveness and advantages of the proposed methods.