Discrete-Time $H_{\infty}$ Filtering for Mobile Robot Localization Using Wireless Sensor Network

Abstract

This paper proposes a localization technique for mobile robots using a wireless sensor network (WSN), based on chirp spread spectrum ranging and an inertial measurement unit (IMU). A discrete-time <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter with input forcing function is newly derived for mobile robot localization. The position of the robot is estimated by the filter using an integration of position information collected by the WSN and absolute acceleration data obtained by the IMU. From the dynamics of the robot, the solution existence of the proposed filter is shown, and a low-complexity computational method to obtain a solution from the filter is proposed by a generalized eigenvector approach. Through simulation and experiments, we evaluate the performance of the proposed <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter and compare it with the standard Kalman filter.