Quadrilateral Weighted Localization for Wireless Sensor Networks Based on Bayesian Filtering

Abstract

In this paper, an improved weighted centroid location algorithm for quadrilateral ranging based on received signal strength indication (RSSI) is used to reduce the measurement error of RSSI effectively. Furthermore, this paper puts forward the localization algorithm of indoor optimization RSSI based on Bayesian probability model and combines the least squares curve fitting to estimate the location of unknown nodes, which further improves the accuracy of the algorithm. In addition, the simulation results show that the improved centroid localization algorithm not only improves the accuracy greatly, and the robustness is also relatively high.