Accurate indoor localization with optimized fingerprinting algorithm

Global positioning systems, that proved their success in the outdoors, cannot perform as well in enclosed environments because they suffer from absence of line of sight or bad reception quality of base stations. In this regard, methods are being developed for highest accuracy indoor locating performance with least cost. Among these methods localization with fingerprinting is far more superior to other indoor localization methods as it uses the surrounding signals in the environment for accurate positioning and is available to most common mobile devices. In this work, indoor fingerprinting algorithms for localization of mobile devices based on Correlation Database (CDB) Filtering, Genetic Algorithm (GA) and Big Bang — Big Crunch (BB-BC) are compared. Results show that using adaptive GA or BB-BC < 3 m error of %95 can be achieved with 4 Bluetooth Low Energy (BLE) beacons distributed around a 40 m2 testbed, compared to < 5.4 m for CDB Filtering.