A new Kalman filter-based algorithm to improve the indoor positioning

Abstract

Nowadays, navigation is an important application in smartphones. However, locating a mobile is an imperative task, because the GPS signal is easily corrupted. Pedestrian Dead Reckoning (PDR), is a relative positioning approach using step length and heading estimation. Step length is changing while a pedestrian does different movements. To accurately estimate the step length, we used a model based on the readings of Accelerometer sensor by employ a relative threshold detection. PDR method relay on heading estimation which affected by many noises and more accurate the readings is the more accurate PDR location estimation. A new Kalman filter algorithm using PDR method is introduced which improves the heading estimation by using inputs from Accelerometer, Gyroscope and Magnetometer sensors. In the experiment, a test route were taken with the distance of 210 meters, and shows an average error of 0.14% of the total travelled distance, it is a higher accuracy compared with others [1–3].