Fingerprint localization method based on multi-source processing fusion

With the widespread popularity of mobile devices and the extensive coverage of corresponding infrastructure, wireless signal based fingerprint positioning has become the most attractive location calculation method in indoor location-based services. However, traditional WiFi RSSI fingerprint positioning is susceptible to multipath effects and signal noise in complex and changing indoor environments, making it difficult to meet high-precision positioning requirements. This article focuses on the key issues of indoor positioning technology in complex and changing environments, and designs a high-precision indoor positioning system based on Kalman filtering, particle swarm optimization (PSO) algorithm, and bidirectional long short-term memory network (Bi LSTM). The system first uses Kalman filtering to preprocess the RSSI signal, effectively reducing the impact of noise interference and outliers, and improving data stability. Secondly, the PSO algorithm is used to optimize the partitioning of the fingerprint database, improve the location update strategy and fitness function, and enhance the efficiency and accuracy of fingerprint matching. In addition, the system uses Bi LSTM network to mine the temporal characteristics of RSSI data, further improving the positioning accuracy. In order to enhance the processing capability of multi-source signals, this paper introduces variational autoencoder (VAE) technology to achieve deep fusion of WiFi and Bluetooth signals, complete feature extraction and data dimensionality reduction, and verify its applicability in large-scale and multi terminal scenarios. The experimental results show that the system proposed in this study has significantly improved signal stability, positioning accuracy, and computational efficiency, providing new ideas and technical support for the research and application of indoor positioning technology.