Improving Fingerprint Indoor Localization Using Convolutional Neural Networks

Two obstacles lie in the traditional Signal Strength Fingerprint Positioning method. Initially, the algorithm cannot converge quickly and accurately due to massive data generated by large indoor environment. Secondly, it is difficult to determine a specific floor in a building using the received Signal Strength(RSS). This article proposes a method, which uses convolutional neural network (CNN) to classify the floor and location of Bluetooth RSS as well as magnetic field data to calculate the final coordinates, could apply Fingerprint Positioning into indoor environment with large areas and multiply floors. The method involves converting the collected Bluetooth RSS into the “fingerprint image” required for calculation and establishing the CNN for classification training. Subsequently, the real-time Bluetooth RSS are imported into the CNN to classify the floor and determine the transmitters' location. Additionally, the observer's coordinates are matched using the magnetic field data. Our experiments suggested that the proposed method can classify floors and transmitters' locations with predictable bunds of 0.9667 and 0.9333, respectively. At the same time, the average positioning error is less than 1.2 m, which is 43.32% and 44.67% higher than the traditional Bluetooth and magnetic field fingerprint positioning. The accuracy of dynamic positioning is also within 1.55 meters.