Research On Estimation Method Of Radio Signal Intensity

In the face of the increasingly complicated radio environment today,the demand for how to effectively protect the normal wireless communication becomes more and more impatient,radio signal strength estimation research is under the background of the birth of.It has been the focus of researchers to study the more abstract radio environment,and the accuracy of radio signal strength estimation is the most important.This paper summarizes the common methods of radio signal strength estimation,and focus on the radio environment interpolation method based on monitoring node sensing data,combined with the characteristics in the process of radio wave propagation loss rule and radio environment parameter itself,two improved methods are put forward:1.By using the radio wave propagation model to improve the variation function of Kriging interpolation algorithm,the improved variation function is more in line with the variation of the intensity attenuation of radio propagation signals.Based on proposed new Kriging algorithm of electric field strength in the radio environment for spatial interpolation estimation,the Kriging method of propagation model based on the estimation of the radio environment field has a better effect is presented in this paper is demonstrated by IDW,the traditional Kriging spatial interpolation algorithm of contrast experiment.2.According to sparse sampling points,with dense buildings under the traditional Kriging interpolation algorithm shows the inadequacy,take the to improved particle swarm optimization algorithm in combination with the characteristics of the radio wave propagation characteristics and variation function structure to adapt to the function,and put forward a kind of improved variogram fitting method,based on PSO weighted variation function fitting method.The accuracy of the fitting of the variation function is improved by the method of sample point weight coefficient,thus the Krikin estimation algorithm based on PSO is formed.Compared with the results of the least square method,the results show that the new algorithm has a high precision,and it still has certain accuracy in the case of sparse sampling points,and it shows some stability.