Smart Phone Based Sensor Fusion by Using Madgwick Filter for 3D Indoor Navigation

In this paper, we proposed an enhanced pedestrian dead reckoning (PDR) system based on sensor fusion schemes using a smartphone. PDR is an effective technology for 3D indoor navigation. However, still, there are some obstacles to be overcome in its practical application. To track and simulate pedestrian's position, which is confronted by environmental errors, walls, Bayesian errors, and other obstacles, our proposed PDR system enables estimation of stride based on the vertical accelerometer data and orientation from sensor fusion technique of magnetic angular rate and gravity sensor data by Madgwick filter. This localization system is independent of the received signal strength-based fingerprinting system. In addition, to estimate the current floor level, we make use of barometer information. To collect ground truth accurately and efficiently a prototype is implemented with the benchmark. We perform the same distance estimation for four different pedestrians to evaluate the accuracy of the proposed system. The real indoor experimental results demonstrate that the proposed system performs well while tracking the test subject in a 2D scenario with low estimation error (< 2 m). The 3D evaluation of the system inside a multi-story building shows that high accuracy can be achieved for a short range of time without position update from external sources. Then we compared localization performance between our proposed system and an existing (extended Kalman filter based) system.