Indoor positioning method for pedestrian dead reckoning based on multi-source sensors

Abstract

To solve the problems of severe error accumulation and low accuracy of pedestrian trajectory estimation in traditional Pedestrian Dead Reckoning (PDR) positioning technology, this paper proposes a multi-sensor fusion indoor PDR algorithm. Firstly, a generalized likelihood ratio multi-threshold detection algorithm is employed to detect the gait of pedestrians. Then, a linear multi-source information fusion model is constructed for step length estimation. Next, the quaternion strap-down attitude solution is utilized and coupled with an improved particle filter-unscented Kalman filter algorithm to correct heading angle deviations. Finally, integrate them into the PDR algorithm to estimate the pedestrian's position. The proposed PDR method's relative positioning errors for indoor two-dimensional plane and three-dimensional space walking are 0.36 % and 0.435 %, respectively. Compared to four traditional positioning algorithms, it reduces errors by approximately 0.77 %∼1.18 % and 5.42 %∼11.69 %, respectively. Experimental results indicate that the proposed PDR method effective suppression of error accumulation, achieving more accurate indoor PDR results.