A Machine Learning Approach Based on Indoor Target Positioning by Using Sensor Data Fusion and Improved Cosine Similarity

Abstract

Indoor user positioning is a crucial problem in modern life. It has wide usage in health, security, smart homes, etc. Global positioning system (GPS) is used outdoors, and it does not work effectively in indoor areas since many things can degrade GPS positioning accuracy. All solutions for indoor areas aim to provide low-cost and high-accuracy positioning. In this study, a low-cost indoor positioning algorithm is developed. The fingerprint signal map of the building is measured with built-in digital sensors in smart devices. The measurements consist of Wi-Fi, bluetooth low energy, and magnetic field signals called data fusion. During the positioning phase, the proposed model, called improved cosine similarity, uses the cosine similarity and information gain method. Digital magnetometers measure magnetic fields with different approaches. In the proposed method, Kalman filter is used to reduce noise magnetic field signals since this variety can give rise to mistaken positioning. To compare the effectiveness of the proposed method, it was compared to K-nearest neighbor, support vector machines, linear discriminant analysis, artificial neural networks, decision trees, N-near neighbor, and binned neighbor algorithm. Based on the experimental data, it was concluded that the proposed architecture achieved higher accuracy rates by reducing distortion.