A Two-Step ML-Based Approach for Multiple RF Sources Sensing: Localization and Power Estimation

The localization and power estimation of radio frequency (RF) interference sources is increasingly becoming an important topic in RF sensing. This article proposes a novel two-step approach based on machine learning (ML), which employs a nonconvex sparse recovery (NCSR) algorithm and a fusion neural network (FNN) specifically designed for the localization and power estimation of multiple RF sources. In the first step, the improved NCSR algorithm for the direction of arrival (DOA) and received signal strength (RSS) joint estimation is proposed based on an iterative maximally sparse convex (IMSC) algorithm, which offers enhanced DOA and RSS estimation accuracy. In the second step, a DOA and RSS fusion artificial neural network is constructed to accurately determine the location and power of the sources by inputting the DOA and RSS estimated in the first step. Simulation results show that both the first step and the second step perform better when used alone or in combination. The performance of the proposed two-step approach is verified through a sensing experiment involving two sources and four sensor arrays with an uncrewed vehicle-based testing system.