A Fusion Algorithm of Indoor Positioning Based on PDR and RSS Fingerprint

Abstract

The pedestrian dead reckoning (PDR) positioning has small noise and high stability in the short term, but it cannot use for long term because of its cumulative error. The received signal strength (RSS) fingerprint positioning has wide coverage and no accumulative error, but the amount of data leads to the long positioning time and discontinuous trajectory in the fingerprint database, where the stability of wireless signal is poor to result in a positioning error. Based on above method’s issues, this paper proposes a fusion indoor positioning algorithm based on PDR and RSS fingerprint. This algorithm presents a new filtering method based on the comparison of two adjacent RSS values, which can be used to deal with the PDR positioning method to solve the noise point caused by the shaking because the tester handheld terminal is not stable enough in the test and be used for processing of RSS fingerprint positioning method to inhibit the effect of wireless signal instability in a certain extent, such as the jumping point noise. Moreover, a fusion algorithm based on adaptive parameters is proposed in this paper with differences from the fusion method proposed in the reference literature, which is adaptive parameter with different weights according to the accuracy of the two positioning methods in the testing. Xiaomi Mi Pad of the Android system verifies the proposed method, and experimental results show that the proposed fusion positioning algorithm can effectively improve the positioning accuracy.