Indoor Location Algorithm Based on WIFI Location Fingerprint

With the development of Internet of Things technology, more and more devices are connected to the Internet, which enables people to obtain the necessary information and services anytime and anywhere. The commonly used WIFI (Wireless Fidelity) location fingerprint indoor positioning algorithm is mainly based on the received signal strength (RSSI) to calculate user positions. Due to the presence of obstacles in buildings and interference between different channels, the accuracy of RSSI is often low, resulting in inaccurate positioning results. This study aims to solve the problems of existing WIFI location fingerprint indoor positioning algorithms. It introduces a location fingerprint positioning algorithm based on Weighted Nearest Neighbor Network (WNN). This algorithm collects the arrival time and intensity information of WIFI signals, and processes this information to achieve user location fingerprint extraction and indoor positioning. In order to verify the effectiveness of the position fingerprint localization algorithm based on weighted noise network in indoor position localization, the final experimental results show that the average positioning accuracy of the position fingerprint localization algorithm based on weighted noise network is 93.19%, and the average indoor positioning time is 4.32 seconds. Based on experimental data, it can be concluded that the position fingerprint localization algorithm based on weighted noise network is superior to traditional indoor localization algorithms in indoor localization.