Adaptive Signal Decomposition Methods

Digital signal processing methods are crucial in analyzing radio signals, especially in complex radio electronic environments (REE). With advancements in radio communication, navigation, and control systems, REE often hosts multiple sources operating on the same or adjacent spectrum segments, representing various radio systems. Consequently, signal mixing from different sources with additive noise occurs on the receiving end. Adaptive signal decomposition methods like Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), and Empirical Wavelet Transform (EWT) are employed to address such complexities. These methods enable the breakdown of intricate signals into simpler components without predefining basis decomposition functions. For instance, EMD identifies intrinsic modes, VMD separates modes using a variational approach, while EWT adaptively selects wavelets for decomposition. Each method boasts distinct advantages and limitations, and the selection of a specific approach hinges on signal characteristics and the required precision of analysis. This article offers a concise examination of these adaptive signal decomposition algorithms, outlining their descriptions and exploring their potential applications in radio engineering research.