A RSSI Localization Algorithm Based on Interval Analysis for Indoor Wireless Sensor Networks

RSSI-based localization is popular for its low cost and low complexity. Aiming at the noisy indoor environment that affects the accuracy of RSSI-based localization, this paper proposes a novel algorithm based on interval analysis to reduce the localization error and improve its robustness. In the ranging phase, we utilize Bootstrap resample to construct the confidence interval of measured distance, which lays the foundation for interval analysis. In the localization phase, we cast all the environment noise into unknown but bounded (UBB) noise and use interval analysis to compute the coordinate in set-membership framework. The simulation results show that our method has higher localization accuracy and localization coverage than two other popular localization schemes. Meanwhile, our method can accommodate different noise interference better.