基于监测数据的电磁频谱地图构建与验证

提出基于广义回归神经网络拟合和聚类克里金的构建方法,通过趋势面拟合,将电磁频谱地图构建分解为路径衰减和阴影衰落分量的估计问题,以提升构建精度；设计监测数据聚类和自适应最优邻域选取机制,在保证构建精度的条件下减小计算数据量,以提升构建速度,从而利用数量有限的电磁环境监测数据,在不需要先验信息的条件下实现电磁频谱地图的准确、快速构建。设计并实现电磁频谱地图验证系统,搭建车载数据采集设备,利用实测电磁环境监测数据,验证所提方法的可行性及构建性能。

Construction and verification of spectrum map by using monitoring data

A spectrum map construction method based on general regression neural network fitting and clustering Kriging was proposed, in which the path-loss and shadowing components were estimated by general regression nerual network for trend-surface fitting to improve the construction accuracy. In order to improve construction efficiency and guarantee the accuracy meanwhile, monitoring data clustering and optimal neighborhood selection were utilized to reduce the amount of calculated data. The proposed method can realize the accurate and fast construction without prior information by only using limited amount electromagnetic environment monitoring data. A spectrum map prototype verification system was designed and implemented, real measured data from vehicle-based collection system was utilized for testification of the feasibility and performance of the proposed method.