Nonlinearity Approximation-Based Spectrum Map Fusion With Environmentally Adaptive Regularization

Fusing spectral data of multiple sensors can improve the construction accuracy of spectrum environment maps (SEMs) compared to single-sensor systems. However, most existing SEM fusion methods are inapplicable to electromagnetic environments with different change rates of electromagnetic strength. This paper presents a new nonlinearity approximation-based SEM fusion method that can dynamically adjust the regularization coefficients for SEM fusion according to the complexity of electromagnetic environments. We first estimate the nonlinearity of spectrum data by developing multi-dimensional local linear approximation (MD-LOLA), with which the spectrum variation intensity of the environment can be assessed. Then, we construct a new SEM fusion model with environmentally adaptive regularization. This model combines the nonlinearity of spectrum data with the least absolute shrinkage selection operator (LASSO) and Tikhonov regularization terms, and therefore, can adjust the regularization constraint strength according to the spectrum variation intensity of the environment, improving the SEM construction accuracy. Simulation results show that our proposed SEM fusion method can effectively eliminate outliers caused by noises and measurement errors, and achieve an accuracy improvement of more than 25% compared to state-of-the-art fusion methods in SEM construction.