Improved Wi-Fi Indoor Localization Based on Signal Quality Parameters and RSSI Smoothing Algorithm

The wireless indoor positioning system (IPS) has high demand due to wide applications such as asset and personal tracking, location analysis, and mapping. Global Positioning System (GPS) doesn’t work within an indoor environment due to the non-line-of-sight (NLOS), to overcome this limitation we are using the most demanded technology Wi-Fi due to its high range, high data throughput with high availability and accessibility. The main problem is the positioning estimation accuracy in the Wi-Fi-based positioning system due to noise and interference in the environment. The received signal strength (RSSI) and signal quality (SQ) become low due to the multipath effects, reflections, signal loss, fluctuations, and other unknown factors. The objective of the proposed method is to reduce the fluctuations and the noise of the received signal and improve the signal strength and signal quality to increase the positioning estimation accuracy and distance estimation accuracy. RSSI smoothing technique of weighted moving average and feedback filter is used to make signal cleaner by removing the fluctuations and signal quality parameters SNR margin, frequency, and power noise to improve the signal quality. The trilateration technique is used to realize distance estimation and position estimation. To analyze the positioning estimation accuracy and distance estimation accuracy of the proposed method we implement the regression accuracy metrics based on mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). By doing this we achieved an accuracy below 0.5 meter.