A UWB/Improved PDR Integration Algorithm Applied to Dynamic Indoor Positioning for Pedestrians

• Published in: Sensors (Basel, Switzerland) Vol. 17; no. 9; p. 2065
• Main Authors: Chen, Pengzhan, Kuang, Ye, Chen, Xiaoyue
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.09.2017; MDPI
• Subjects: Accuracy; Error correction; Human mechanics; Human motion; indoor positioning; Inertial coordinates; inertial navigation; Kalman filters; Sensors; symmetrical features; Ultrawideband; UWB; Walking; inertial navigation; UWB; indoor positioning; symmetrical features; error correction
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s17092065

Inertial sensors are widely used in various applications, such as human motion monitoring and pedestrian positioning. However, inertial sensors cannot accurately define the process of human movement, a limitation that causes data drift in the process of human body positioning, thus seriously affecting positioning accuracy and stability. The traditional pedestrian dead-reckoning algorithm, which is based on a single inertial measurement unit, can suppress the data drift, but fails to accurately calculate the number of walking steps and heading value, thus it cannot meet the application requirements. This study proposes an indoor dynamic positioning method with an error self-correcting function based on the symmetrical characteristics of human motion to obtain the definition basis of human motion process quickly and to solve the abovementioned problems. On the basis of this proposed method, an ultra-wide band (UWB) method is introduced. An unscented Kalman filter is applied to fuse inertial sensors and UWB data, inertial positioning is applied to compensation for the defects of susceptibility to UWB signal obstacles, and UWB positioning is used to overcome the error accumulation of inertial positioning. The above method can improve both the positioning accuracy and the response of the positioning results. Finally, this study designs an indoor positioning test system to test the static and dynamic performances of the proposed indoor positioning method. Results show that the positioning system both has high accuracy and good real-time performance.