An Improved Indoor RSSI Based Positioning System Using Kalman Filter and MultiQuad Algorithm

The object position plays an important role in many applications of wireless sensor network (WSN) and Internet of Things (IoT). Hence, positioning system is the main concern of many researchers to achieve the highest accuracy especially in indoor environments. However, RSSI-based indoor positioning system can be easily affected by physical obstacle of the environment which can make it unstable and fluctuate. High instability of RSSI is directly influenced to the estimated position performance obtained from distance calculation with path loss exponent (PLE) value. In this paper, we propose improved indoor positioning system using Kalman filter (KF) for reducing inconsistent of RSSI transmission from Bluetooth low energy (BLE) as the wearable device in each unit of time and MultiQuad algorithm formed by multilateration and quadratic weighted combination as the estimated position determination. Using a combination of KF and MultiQuad algorithms is capable proven to increase the accuracy of estimated position up to 90.32% with mean square error (MSE) 1.15 m. This combination algorithm has capability to reduce error of estimated position compared with only using conventional multilateration reached high error estimation up to 6.99 m.