Low-Complexity Fingerprint Matching for Real-Time Indoor Localization Systems

Due to their robust performance in complex non- line-of-sight environments, fingerprinting-based approaches have long been favored for indoor localization. However, many state-of-the-art schemes rely on a very dense fingerprint map. A larger fingerprint database means a higher achievable localization accuracy, at the same time it means a larger number of required fingerprint comparisons and longer computation delays in the system. This paper presents a novel low-effort approach, that uses a restructured fingerprint database to precisely calculate the location of a user, while comparing the data measured by the user to only a subset of database entries. Several novel approximations of the algorithm are also presented, that trade-off computation complexity and localization accuracy. For comparison, the complexities of a number of state-of-the-art fingerprinting schemes are derived. The effectiveness of the proposed approach is demonstrated through simulation. The presented results show that if the allowed number of fingerprint comparisons is set, the proposed approach and its approximations produce up to 34% lower localization errors than the traditional approach applied to a reduced database.