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.

Radio environment maps (REMs) have demonstrated their significance in spectrum sensing, control, and sharing, particularly in relation to unmanned systems, the Internet of Things (IoT), and 5G communication. However, they encounter challenges related to meeting the increasing accuracy requirements. This paper introduces the BHM-RK algorithm, a novel approach for constructing REMs in scenarios with multiple transmitters. The algorithm utilizes spatial clustering for transmitter partitioning, Bayesian posterior inference within partitions, and residual Kriging interpolation to address challenges posed by diverse transmitter influences. Experimental validation demonstrates the superior performance of the BHM-RK algorithm in terms of construction accuracy compared to existing methods, highlighting its effectiveness in enhancing precision in REM construction for scenarios with multiple transmitters.