KF-KNN: Low-Cost and High-Accurate FM-Based Indoor Localization Model via Fingerprint Technology

Localization and tracking of personnel and equipment are technical issues that urgently need to be solved for Indoor positioning. To improve the accuracy and environmental adaptability of personnel and equipment localization algorithms in the construction and operation of smart water platform, this paper proposes a fingerprint localization algorithm (KF-KNN) based on FM signals. Firstly, use FM data collection device to obtain RSSI fingerprint information within the coverage area, and train them to build a fingerprint database; secondly, KNN technology is used to complete the rough localization calculation based on the RSSI data received by the module to be located, the RSSI fingerprint database and environmental noise parameters; finally, the Kalman filter model is used to predict and optimize the rough position information, so as to have better environmental adaptability and effectively improve the accuracy of localization. The analysis results show that: compared with the original KNN and WKNN fingerprint localization models, the KF-KNN algorithm has better performance in localization, and its average localization error can be as low as 1.9 meters.