Distributed Error Correction of EKF Algorithm in Multi-Sensor Fusion Localization Model

Fengjun Hu,Gang Wu

doi:10.1109/access.2020.2995170

Abstract

In order to solve the problem that the standard extended Kalman filter (EKF) algorithm has large errors in Unmanned Aerial Vehicle (UAV) multi-sensor fusion localization, this paper proposes a multi-sensor fusion localization method based on adaptive error correction EKF algorithm. Firstly, a multi-sensor navigation localization system is constructed by using gyroscopes, acceleration sensors, magnetic sensors and mileage sensors. Then the information detected by the sensor is compared and adjusted, to reduce the influence of error on the estimated value. The nonlinear observation equation is linearized by Taylor, and the normal distribution hypothesis is carried out in two steps of prediction and correction respectively. Finally, the parameters of system noise and measurement noise covariance in EKF are optimized by using the evolutionary iteration mechanism of genetic algorithm. The adaptive degree is obtained according to the absolute value of the difference between the estimated value and the real value of EKF. The individual evaluation results of EKF algorithm parameters are used as the measurement standard for iteration to obtain the optimal value of EKF algorithm parameters. Experimental simulation results show that the improved algorithm proposed has higher real-time localization accuracy and higher robustness than those of the standard EKF algorithm.

Highlights

Location Based Services (LBS) is a basic service that obtains the current location and provides information resources through various mobile location technologies [1]–[3]
Al Hage et al [19] proposed an optimal thresholding method based on Kullback-Leibury criterion (KLC), which improves Kalman filter and realizes cooperative localization of robots
(1) Proposed a multi-sensor fusion localization based on adaptive error correction extended Kalman filter (EKF) algorithm to improve the real-time localization accuracy

Summary

INTRODUCTION

Location Based Services (LBS) is a basic service that obtains the current location and provides information resources through various mobile location technologies [1]–[3]. Wu: Distributed Error Correction of EKF Algorithm in Multi-Sensor Fusion Localization Model nonlinear real-time localization model by probability weighting method. Al Hage et al [19] proposed an optimal thresholding method based on Kullback-Leibury criterion (KLC), which improves Kalman filter and realizes cooperative localization of robots. Ruotsalainen et al [21] introduced the error probability density function in particle filter, and used the model fitting method to verify the measurement error, improving the accuracy of multi-sensor fusion localization. Cappello et al [25] implemented a new hybrid controller using fuzzy logic and proportionalintegral-derivative (PID) technology and proposed a real-time localization system based on improved unscented Kalman filter. (1) Proposed a multi-sensor fusion localization based on adaptive error correction EKF algorithm to improve the real-time localization accuracy. The rest of this paper is arranged as follows: Section 2 summarizes the related work; Section 3 performs adaptive error correction on the EKF algorithm; Section 4 performs simulation testing on the improved algorithm; Section 5 summarizes the paper

PROBLEM DESCRIPTION

DISTRIBUTED ERROR SECONDARY CORRECTION

PERFORMANCE SIMULATION

CONCLUSION