Huang Xin

Research on pedestrian navigation system aided by indoor geomagnetic matching

Indoor pedestrian navigation technology is playing a more and more important role in our life. For low cost strap-down navigation system, error is large and spreads rapidly. Hence, this paper proposes a zero velocity update (ZUPT) detection algorithm based on acceleration magnitude square variance detector (AMSVD), angular rate magnitude square variance detector (ARMSVD), acceleration magnitude square detector (AMSD) and angular rate magnitude square detector (ARMSD) to distinguish pedestrian kinetic characteristics and restrain the divergence of errors. Meanwhile, this paper studies the pseudo measurement information in ZUPT and builds a Kalman filter for optimal estimation of errors. Simultaneously, we study the indoor magnetic anomaly and put forward a kind of indoor geomagnetic matching algorithm based on k-nearest neighbor algorithm (KNN) and a multi-layer constraint which is suitable for engineering application. Finally, we construct an integrated navigation system to realize optimal fusion of MEMS pedestrian navigation system and geomagnetic matching model to improve accuracy and adaptation and provide continuous position information, besides, it weakens the effect of MEMS sensor over range. Tests show that the accuracy of the integrated navigation system is increased by more than 55% and the final position error is only 0.4%.