•  
  •  
 

Subject Area

Electronics and Communication Engineering

Article Type

Original Study

Abstract

Positioning of pedestrians is a challenging problem. Especially, in case of global positioning system (GPS) signal outage inside buildings. The inertial navigation system (INS) systems are always used to detect the motion of the human body by placing inertial measurement unit (IMU) in a specific part of the human body. However, using IMU alone will not produce proper navigation solution with sufficient accuracy due to gradually accumulated errors of IMU stochastic drift, noise, and state integration with time. Which lead the recent researchers to enhance the performance of indoor navigation systems using aiding sources such as received signal strength (RSS) from beacon sources recorded on database, and maintained on a server for collecting signals from certain locations then using classification methods such as k-nearest neighbors (KNN) and Support vector machines (SVM) for identifying locations that are not in the database to provide fingerprint systems-based localization. However, it was discovered that this approach presents drawbacks, of low precision and an excessive cost. In this paper, the proposed system is based on integrating both INS and Wi-Fi access to mitigate errors. Further, Kalman filter is utilized for optimally fusing the data from the INS and Wi-Fi signals. Additionally, the algorithm is modified by the bubble technique to build more accurate Kalman filter observation model to remove the need for database to determine the position using fingerprints method. The proposed algorithm is validated using simulation and real experiments at the Faculty of Engineering in Mansoura. The proposed work presented a low cost and less complex pedestrian indoor navigation system with sufficient performance with accuracy of less than 7.2905 cm.

Keywords

RSS, inertial navigation system (INS), Zero Update Position and Timing (ZUPT), Pedestrian dead reckoning (PDR).

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Download

Share

COinS