An improved neighbor-correlation-extended-Kalman-filter fusion method for indoor navigation

The received signal strength–based fingerprinting navigation system is able to provide location information with accuracies in the meter region under the assistance of inertial measuring units. However, the computational complexity in mobile terminal of this cooperation method is great for real-time position. The inertial measuring unit has the drawback of error drift, and not all the device has a self-contained unit. In order to obtain high-accurate and continuous navigation information for indoor general devices in small computations, a novel combination of fusing extended Kalman filter and fingerprinting navigation algorithm, including K-nearest neighbor and Pearson correlation coefficient, is proposed in this article. A prototype of the improved system has been worked in a real scenario. A laptop on a four-wheel handcart is moving at a constant speed in a building storey, and the measurement localization is acquired by fingerprinting algorithm during online phase. Meanwhile, the modification localization is produced by extended Kalman filter when the target is moving in the floor. Finally, compared to K-nearest neighbor, Pearson correlation coefficient, and a combination of both, the final modification localization value is more accurate. The results show that the mean error is 53.2%, 51%, and 25.8% lower than the other three methods.