Sinusoidal Similarity Among Electromagnetic Reconnaissance Signals and Its Application in Blind Signal Separation

In nowadays information era, the types of communication or radar electronic equipment for both military and civilian applications are increasing significantly, which leads to various communication and radar signals overcrowded in the time domain, overlapped in the frequency domain, and intertwined in the space domain, shaping a more complicated electromagnetic environment. In order to accurately capture the interested signal or discover the interference signal, separating these time-frequency overlapped signals and extracting the information implied in the useful signal has become a research task with significance for the electromagnetic surveillance domain. This article investigated the sinusoidal similarity among electromagnetic reconnaissance signals and proposed a time–frequency overlapped signals separation method based on sinusoidal similarity for complex electromagnetic monitoring environments. First, we mathematically derived the sinusoidal similarity among electromagnetic reconnaissance signals for three different mixed scenarios, to be specific, digital modulation communication signals mixing scene, digital modulation communication signal(s) and pulse repetition interval radar signal(s) mixing scene, and pulse repetition interval radar signals mixing scene. Then, we formulated a blind signal separation (BSS) objective/cost function utilizing sinusoidal similarity (SSim) among electromagnetic reconnaissance signals. In addition to the SSim constraint, we introduced another constraint term into the objective function, accounting for the unique characteristics of source signals in various mixed scenarios. After that, we successfully optimized the constructed objective function by leveraging advanced machine learning optimization algorithms. Then, we substantiated the validity of the proposed method through simulation experiments, showcasing its performance from various perspectives, and we underscored its advantages by conducting comparisons with classical methods such as fast Independent component analysis and joint approximative diagonalization of eigenmatrices. Furthermore, the proposed BSS method has broader applications in the realm of artificial intelligence. It can be employed for intelligent tasks such as speech signal separation and biomedical signal separation, showcasing its versatility in diverse domains.