Environmental-adaptive RSSI-based localization in intelligent space

To overcome the influence of dynamic environmental parameters on the accuracy of the object localization in intelligent space using the received signal strength indication (RSSI) method, an environmental-adaptive RSSI-based localization algorithm is proposed, and the reason why changes of environmental parameters are the main factors influencing the distance measurements and localization error is expounded. When using the proposed algorithm, firstly the measured data are preprocessed through singular value removing and average filtering, and then the adaptive RSSI distance estimating algorithm is utilized to estimate the distance between nodes. The location of unknown node is calculated using the maximum likelihood estimation. Finally, the Kalman filter is exploited to estimate the trajectory of moving targets. The experimental results show that the localization error of the proposed algorithm is less than one meter, meeting the requirements of moving-targets localization in intelligent space.