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G. Retscher:
"An Intelligent Personal Navigator Integrating GNSS, RFID and INS";
in: "Geodesy for Planet Earth", S. Kenyon, M. Pacino, U. Marti (ed.); issued by: IAG; Springer Verlag, Berlin Heidelberg, 2012, ISBN: 978-3-642-20337-4, 949 - 956.

Personal navigation services usually rely on GNSS positioning and therefore their use is limited to open areas where enough satellite signals can be received. If the user moves in obstructed urban environment or indoors, alternative location methods are required to be able to locate the user continuously. In our approach GNSS positioning is combined with a MEMS-based Inertial Measurement Unit for continuous position determination. In addition, Radio Frequency Identification (RFID) Location Methods are employed. In RFID positioning the location estimation can be based on signal strength measurements (i.e., received signal strength indication RSSI) which is a measurement of the power present in a received radio signal. Then the mobile receiver can compute its position using various methods based on RSSI. Three different methods have been developed and investigated, i.e., cell-based positioning, trilateration using ranges to the surrounding RFID transponders (so-called RFID tags) deduced from RSSI measurements, and RFID location fingerprinting. In most common RFID applications positioning is performed using cell-based positioning. In this case, RFID tags can be installed as active landmarks with known location. The user is carrying a RFID reader and is positioned using Cell of Origin (CoO). GNSS and RFID are then integrated with INS positioning for continuous position determination. INS measurements would be utilized to calculate the trajectory of the user based on the method of strap down mechanization. Since the INS components produce small measurement errors that accumulate over time and cause drift errors, the positions determined by RFID or GNSS are needed regularly to reduce the drift. All observations are integrated in a Kalman filter to estimate the userīs position and velocity. By integrating the above mentioned measurements into an intelligent software package the developed personal navigator will enable to determine the mobile userīs position continuously, automatically and ubiquitously.

Pedestrian Navigation, Multi-sensor, RFID, Inertial Navigation, Sensor Fusion

http://dx.doi.org/10.1007/978-3-642-20338-1_119