A Dynamic Adaptive Indoor Ranging Model Based on RSSI

Range estimation based on received signal strength indicator (RSSI) is widely applied in indoor localization. However, the performance of the conventional RSSI based on ranging model degrades significantly when it is applied to a dynamic indoor environment due to the measuring noise, multipath effect and shadow effect. Therefore, an improved RSSI based on ranging model for the dynamic indoor environment is proposed by the slope approximation method (SAM). In the proposed ranging model, we analysis and extract the characteristics of the widely existing WiFi signal in indoor environment. Then, we create a slope database to match the real-time RSSI and calculate the distance in on-line stage. Experimental results show that the proposed method has better effect than the conventional RSSI based on ranging estimation methods for the dynamic indoor environment.