Synthetization of Fingerprint Recognition and Trilateration for Wi-Fi Indoor Localization Through Linear Kalman Filtering

摘要

In traditional indoor localization, fingerprint localization algorithm fully considers the influences of multipath signals and static obstacles but degrades in case of the changes of observation environment. Trilateration localization has the better robustness to the signal variations but its performance degrades for achieving a certain level of accuracy. In this paper, for the problems above, firstly, a weighted fusion algorithm based on fingerprint recognition algorithm and trilateration algo-rithm was proposed. Then, adaptive fusion is added to filter the error localization point. Finally, linear Kalman filtering which based on the constant velocity state model assumption is introduced to smooth Wi-Fi localization error. By using the algorithms above, the experimental platform is set up to carry out the localization test. The test result justify that our proposed algorithm has performed better and achieved a better level of accuracy.