摘要：Due to the wide application of range-based location algorithm for received signal strength, and according to the requirements of high accuracy and low power cost in the location algorithm for WSNs, in this paper, a Bayesian optimization RSSI and an indoor location algorithm for ILS were introduced by setting RRS ranging as location framework. Firstly, through analyzing the RSSI-based ranging model, an indoor location model was introduced. Secondly, in view of the influence on RSSI value caused by the indoor environment,the Bayesian probabilistic model was adopted to process the RSSI measured value and to screen out the "big probability" of RSSI value. Thirdly, Obtaining accurate measured data by estimating distance using method of minimum mean square error. Finally, Estimating the node location using least square method, and according to the TelosB node of Telos Series produced by company Crossbow, the ranging experiment can be designed and thus groups of experimental data were obtained and analyzed..The experimental results showed that the proposed location project greatly increased the location accuracy and decreased the computation complexity, and has obviously more advantage of running time over other location projects.
关键词：Wireless Sensor Network; Node Localization; Bayesian Estimation; ; Maximum Likelihood Estimation; Iterative Least Square
