Indoor Localization Using Bayesian Filter
Ambient Intelligent (AmI) Wireless Sensor Networks (WSN) provide intelligent services based on user and environment data obtained by sensors. Such networks are developed to give environmental monitoring and indoor localization services. In this work, Zigbee which is a wireless communication technology is used for localization based on Received Signal Strength Indicator (RSSI) method. In practice, Extended Kalman Filter (EKF) is adapted to filter RSSI values influenced by multi-path fading and noise. Log-Normal Shadowing Method (LNSM) together with the Trilateration method was implemented to locate the position of the unknown node or entity. In addition, Cramer Rao Lower Bound (CRLB) is derived for the position estimation, that can be used to evaluate the performance of the system in terms of localization accuracy. Along with indoor localization, the deployed WSN could also monitor environment parameters like temperature and humidity surrounding entity using Digital Humidity and Temperature (DHT11) sensor. Using Zigbee location coordinates of entity and environment parameters are transmitted to remote desktop where visualization of data is done using Matrix Laboratory (MATLAB).