Machine Learning–Assisted Blind Emitter Candidate Generation in Power Spectrum Analysis

ABSTRACT

Blind emitter candidate generation from power‐spectrum measurements is a fundamental task in regulatory spectrum monitoring, where only spectral information is available, and signal components must be distinguished from background spectral variations. This letter proposes a machine learning–assisted approach to blind emitter‐candidate generation that operates directly on averaged power spectra. The method extracts local spectral features and applies supervised classification to distinguish signal‐bearing from noise‐only frequency bins. Performance is evaluated using a simulation framework comprising 100 randomised frequency‐modulated (FM) broadcast band monitoring scenarios under additive white Gaussian noise, with multiple simultaneously active emitters. Several supervised classifiers are evaluated and compared using detection and false alarm metrics. Experimental results show that the proposed approach achieves detection probabilities comparable to conventional peak‐based methods while significantly reducing the false alarm probability. Statistical analysis indicates that random forest classifier (RFC), support vector machine with radial basis function (SVM‐RBF) kernel and linear discriminant analysis (LDA) provide the best overall trade‐offs. The results demonstrate that machine learning–assisted candidate generation can effectively reduce false alarms without sacrificing detection performance, thereby alleviating the burden on subsequent filtering stages in blind spectrum monitoring systems. Source code and data will be available at https://github.com/eliezersflores/ml‐assisted‐emitter‐candidate‐generation .