Open Journal Systems




Most of the developed pedestrian navigators rely on the use of satellite positioning (GNSS), sometimes also in combination with other sensors and positioning methods. In the project “Ubiquitous Cartography for Pedestrian Navigation” (UCPNAVI) we have integrated active Radio Frequency Identification (RFID) in
combination with GNSS and Inertial Navigation Systems (INS) for continuous positioning. RFID can be employed in areas where no satellite positioning is possible due to obstructions, e.g. in urban canyons and indoor environments. In RFID positioning the location estimation  is based on Received Signal Strength Indication (RSSI) which is a measurement of the power present in a received radio
signal. The receiver can compute its position using various methods based on RSSI. In total, three different methods have been developed and investigated, i.e., cell-based positioning, trilateration and RFID  location fingerprinting. These methods
can be employed depending on the density of the RFID tags in the surrounding environment providing different levels of positioning accuracies. By integrating the three methods for positioning into an intelligent software package and developing a knowledge-based system it is possible  to determine the pedestrian position automatically and ubiquitously. The concept of the intelligent software package is presented and described in the paper. For improvement of the positioning accuracy of cell-based positioning a modification has been developed, the so-called time-ased Cell of Origin (CoO) positioning method. This method uses also the
measured RSSI above a certain threshold which is measured only if the user is located very close to the RFID tag. The  test results showed  that the accuracy of positioning using time-based CoO is in the range of 1.30 m. For continuous positioning of the pedestrian user, a low-cost INS is employed in addition. Since the
INS components produce small measurement errors that accumulate over time and cause drift errors, the positions determined by RFID would be needed regularly for update. For the combined positioning of  RFID and INS a time-varying Kalman
filter is employed. The approach is tested in indoor environment in an office building of our university. For the combined positioning, an accuracy of around 1.00 m for continuous position determination is achieved. The new approach and the test results are also described in this paper. 


GPS/INS; Integration; Navigation; Mobile; Multi-Sensor