Performance Analysis of Sensor Fusion Techniques for Heading Estimation Using Smartphone Sensors

Abstract

Efficient indoor positioning requires accurate heading and step length estimation algorithms. Therefore, in order to improve the indoor position accuracy, it is necessary to estimate both the user heading and step length with minimal error. These include errors from the accelerometer, magnetometer and gyroscope of smartphone sensors. Fusing different sensor data has a high impact on improving heading accuracy. In this paper, we present a comparative analysis of different sensor fusion techniques for heading estimation using smartphone sensors. The performance of different sensor fusion techniques is discussed in terms of root mean square error and cumulative distribution functions of heading errors. The experimental results show the effects of different sensor fusion techniques for heading estimation. The performance of five sensor fusion techniques such as a linear Kalman filter (LKF), extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filters (PF) and complementary filters (CF) were analyzed. The UKF fusion algorithm shows better results compared to EKF and LKF fusion algorithms. The EKF approach is better than LKF and CF approaches. The experimental results show that the PF fusion technique has poor performance for heading estimation.