基于差分平稳时序的Ka波段雨衰预测

Rain attenuation prediction at Ka band based on difference stationary timeseries

针对经典模型在Ka波段雨衰预测时存在涉及参数多、计算量大的问题,提出了基于差分平稳时序的预测方法.该方法利用前导雨衰值的差分变换建立预测模型,并通过对平稳时序的参数估计得到各频点的雨衰预测值,进而实现将传统的非线性预测转化为简便的线性预测.仿真结果表明:不同预测间隔、时序个数、差分次数下的预测精度不同,与Dissanayake-Allnut-Haidara模型相比在满足预测间隔0.1 GHz、时序数20、二次差分条件时预测误差可达10-3以下,同时表明极化方式对模型参数的影响可以忽略,验证了所提方法具备参数计算简单、预测精度高的优点.

To cope with the issue of multi-parameter and intricate calculation of classical models in predicting rain attenuation in Ka band, a method is presented to reduce the complexity by utilizing difference stationary time-series. By this method, a prediction model is established based on the difference transform of leader rain attenuation data, and parameters of stationary time-series are estimated to compute the rain attenuation at each frequency point in Ka band for achieving linear prediction. Simulations show that the prediction precision depends on prediction interval, the number of time-series and the frequency of difference. Compared with Dissanayake-Allaut-Haidara model, the proposed model has a prediction error of less than 10^-3 when prediction interval is 0.1 GHz and the number of time-series is 20 with quadratic difference. Furthermore, the simulation results also indicate that the proposed method is simple and practical due to the fact that influence of polarization mode on model parameters may be ignored.