Robust Heading Estimation Algorithm for Android Smartphones

Abstract

This article proposes a robust heading estimation algorithm for smartphones, which integrates the gradient descent (GD) approach and genetic algorithm (GA) to compute the heading angle of smartphones and reduce the heading error accumulation induced by the errors of sensors. In the proposed algorithm, the accelerometer, magnetometer, and gyroscope are used to estimate the heading angle. The sum of squared triaxial errors of geomagnetic strength and acceleration is regarded as the fitness of GA to improve the accuracy of heading angle and restrain the error accumulation. The angular velocity given by gyroscope is used to generate the init ial point of GA. To improve the convergence rate and local search capability of GA, a search area adjustment strategy based on GD is designed to obtain the optimal search area for each iteration of GA. Experimental results show that the proposed algorithm can achieve better heading estimation accuracy on three smartphones and four orientations, with a mean heading error of 1.577° on Mate 20 smartphone.