局部低秩张量补全的无线频谱地图鲁棒重构方法OA

Wireless spectrum maps serve as an effective method for the visual characterization of complex electromagnetic environments.To address the reconstruction of two-dimensional planar spectrum maps under spatially sparse sampling and various noise interferences,a reconstruction algorithm based on adaptive threshold block-term tensor decomposition(ATBTD)is proposed.The algorithm introduces an adaptive thresholding mechanism into the Frobenius norm loss function to suppress impulsive noise,and efficiently solves the factor matrices under the block successive upper-bound minimization(BSUM)framework combined with the projected gradient(PG)method.Furthermore,to reduce computational complexity,a fast adaptive threshold block-term tensor decomposition(F-ATBTD)algorithm is proposed to replace traditional singular value decomposition,and an accelerated gradient method is incorporated to enhance convergence efficiency.Compared with baseline algorithms,F-ATBTD significantly reduces computational overhead while maintaining reconstruction accuracy.Simulation results show that under low signal-to-noise ratio conditions,ATBTD and F-ATBTD achieve approximately 15%lower normalized mean square error than baseline methods in Gaussian noise scenarios,and about 30%lower in mixed-noise scenarios.