Dynamic active area clustering with inertial information for fingerprinting based indoor localization systems

Fingerprinting based localization is one of the most widely used indoor localization methods. This method is divided into two phases: during the off-line training phase, fingerprints within the area of interest are collected and stored in a fingerprint database; during the on-line mapping phase, the real-time location of a device is estimated by mapping itself to the most accurate fingerprint in the database. The efficiency of the mapping process is one of the key challenges of the on-line phase, and is mostly characterized by the localization accuracy and the response time. Clustering methods have been introduced to reduce computational overhead. In this paper, we propose a dynamic clustering method leveraging the inertial information of the target device. An active area is dynamically computed around the prior position. The target mapping space is significantly reduced with this active area. This method can be integrated with other clustering algorithms to overcome the edge problem and remove outliers. We evaluate this method compared with the state-of-the-art methods on a body sensor based localization system. The results show that the accuracy, precision and response time of the system are improved greatly.