A novel RSSI-based geometric iterative centroid positioning optimization strategy using commercial Wi-Fi signals

The information interaction and positioning technology based on commercial Wi-Fi plays a crucial role in expanding the applications for the Internet of Things and smart homes. Achieving high-precision indoor positioning through the optimal deployment of Wi-Fi access points remains a significant challenge. This study proposes two indoor positioning optimization algorithms based on the received signal strength indicator (RSSI) of commercial Wi-Fi signals. The enhanced weighted centroid positioning (EWCP) algorithm introduces a novel weighting method that effectively leverages RSSI ranging errors to improve positioning accuracy and further minimize positioning errors. Meanwhile, the adaptive iterative weighted centroid positioning (AIWCP) algorithm incorporates a detailed weighting approach during the movement of the signal transmitter, providing specific movement indications and enhancing positioning performance. Both simulation and experimental results confirm the effectiveness of these two positioning methods.