An Indoor Positioning Method Based on Range Measuring and Location Fingerprinting

With the rapid development of WiFi technology, the WiFi network has been in existence widely today around the world. Various indoor positioning technologies based on WiFi have constantly emerged, and already aroused wide attention due to their virtues of low cost and easy implementation. Among of them, the WiFi-based passive fingerprint indoor positioning has become a key interest of research since it is cheap, non-invasive, and easy to extend. In other words, it can be directly deployed on the existing business WiFi equipment without any extra devices binding on the objective. Currently, the existing works on passive fingerprint positioning, such as Nuzzer and Pilot, generally contain two phases, offline and online. The offline phase is mainly responsible for collecting the signals corresponding to the objective, and storing the fingerprint data of all the reference points, thus constructing the offline fingerprint database. In the online phase, the measured fingerprints of the target entity are obtained by the same way, and matched with all the fingerprints in the whole offline fingerprint database, thereby estimating the target position. Nonetheless, the timeliness and accuracy of the existing works are not satisfying because they ignore two important facts including: (1) in the online phase performing the match actions throughout the whole offline fingerprint database spends too much time during the localization, especially when fingerprint database is huge; (2) the offline fingerprint database generally involves some fingerprints which are actually far from the current position of the target, but may interfere with the fingerprint matching. These fingerprints are easy to increase the localization errors, which in turn causes the inaccurately positioning. To address the above two problems, this paper proposes ILLFRM (Indoor Localization Method Based on Location Fingerprint and Range Measurement), which is a novel passive fingerprint indoor positioning method that combines the location fingerprint technology with range measurement algorithm. ILLFRM proactively introduces a heuristic operation, namely coarse positioning, in the localization process. Before the fingerprint matching in the online phase, the coarse positioning in advance filters out those fingerprints irrelevant to the current location of the target in the offline fingerprint database, hence the computation load can be significantly reduced, and the interference from those irrelevant fingerprints in the current offline fingerprint database is greatly eliminated at the same time. By this way, ILLFRM ensures not only the accuracy of the positioning results, but the timeliness of the localization process as well. We have run a series of real implementations of ILLFRM in two typical scenarios. The first one is an empty hall whose area is approximately 80 square meters, while the other one is a corridor with the area of 84 square meters. The former scenario can be seen as the typical open environment, and the latter one can be considered as the representative multi-wall environment. The test results show that ILLFRM has better performance compared to the existing passive fingerprint positioning methods. To be specific, the performance revenue of positioning accuracy is about 28% and 51% compared to Pilot and Nuzzer. Besides, the time duration of match process in ILLFRM is less than 200 milliseconds, which testifies that the timeliness of ILLFRM is satisfying. © 2019, Science Press. All right reserved.