Adaptive Extended Kalman Filter (ROSE-Filter) for Positioning System

TL;DR

This paper presents the ROSE-Filter, an adaptive Extended Kalman Filter variant, for vehicle positioning that dynamically estimates measurement noise covariance to improve accuracy in nonlinear systems.

Contribution

It introduces the ROSE-Filter, a novel adaptive EKF approach that enhances vehicle pose and velocity estimation by dynamically adjusting measurement noise covariance.

Findings

Improved accuracy in vehicle position and orientation estimation.

Effective adaptive noise covariance estimation demonstrated.

Enhanced robustness in nonlinear vehicle tracking scenarios.

Abstract

This paper illustrates the way for estimating position and orientation of a vehicle with an Extended Kalman Filter (EKF). For this purpose a non-linear model is designed and an adaptive calculation of measurement noise covariance matrix is used, a so called ROSE-Filter (Rapid Ongoing Stochastic covariance Estimation-Filter). Input of the system is the measured position from a two dimensional position system. Estimated is the pose (position and orientation) and the velocity of the vehicle.