Integration of Machine Learning and Kalman Filter Approach for Fingerprint Indoor Positioning

Fingerprint-based indoor positioning systems are simple and widely used to determine the location of a device inside a building or other enclosed area. However, the accuracy and reliability are still a major concern due to the turbulence in the environment and the presence of noise in the data. This paper presents a machine learning integrated with Kalman filter approach for improving the accuracy of fingerprint-based indoor positioning systems. The proposed approach combines the power of machine learning techniques for feature extraction and classification with the noise-filtering capabilities of the Kalman filter. Implementation is achieved by a real-world dataset collected from multiple Bluetooth low energy access points. The experiment results indicate that the proposed approach significantly improves the accuracy of fingerprint-based indoor positioning compared to traditional machine learning approaches. This study also offers a potential of cost-effective and high accuracy algorithm for the indoor positioning applications.