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Dead Reckoning has successfully been used as a navigational method in the maritime field for positioning between two fixed locations. This approach can be also implemented for smartphones using acceleration sensor and magnetic field sensor. The reliability of the compass values obtained is important in order to calculate the new position. Different tests of accuracy of compass readings of two different smartphones under various test conditions are presented. Moreover, the accuracy of the reconstruction of two walked indoor/outdoor paths using Dead Reckoning will be investigated.

This paper presents an algorithm for RSSI fingerprint positioning based on Euclidean distance for the use in a priori existing larger and dynamically changing WLAN infrastructure environments. Symptomatical for such environments are changing sets of base stations for different calibration points and for calibration phase and positioning phase. The presented algorithm has an accuracy of 2.06m median location estimation error. The algorithm uses four threshold parameters to adapt the calculation to the specific measuring environment. Furthermore the reduction of calibration effort is investigated. It is shown that an enlargement of the calibration grid size from 1m to 6m increases the median location estimation error from 2.06m to 3.5m. Regular calibration measurements include measurements in four rectangular bearings. Reducing the number of calibration bearings results in less calibration effort, but worsens estimation quality.