A Real-Time Indoor Positioning System Based on Wi-Fi RTT and Multi-source Information

In order to solve the problem of locating pedestrians in indoor environments, an indoor real-time high-precision positioning system based on smart phones was constructed. Aiming at the non-line-of-sight and multipath problems in the wireless signal-based indoor positioning technology, a method using deep convolutional neural network (CNN) to learn the nonlinear mapping relationship between indoor spatial position and Wi-Fi-FTM ranging information is proposed. At the same time, a fingerprint gray-scale construction method combined with a specific AP location is designed for pedestrian location prediction. Considering the large fluctuations and poor continuity of fingerprint-based positioning results, a particle filter positioning algorithm with adaptive update of state parameters is proposed, which improves the degree of freedom and positioning accuracy of pedestrian positioning. Finally, a large number of tests were conducted in an indoor test environment of about 800 m2. Compared with the traditional fingerprint positioning method, the fusion positioning algorithm based on the CNN network improves the positioning accuracy by about 30%. Compared with the millimeter-level precision optical dynamic calibration system, the particle filter fusion method has 94.2% results less than 1 m, and the average positioning error is 0.41 m. Moreover, in the real indoor environment of a commercial supermarket, it is further verified that the positioning system has high precision and high continuous positioning performance in practical applications, and has great application and promotion value.