A Hybrid Method for Source Direction Finding With Radio Frequency Interference and Gaussian White Noise
Yanming Zhang, Wenchao Xu, Antonios Argyriou, A-Long Jin, Tianquan Tang, Peifeng Ma, Lijun Jiang, Steven Shichang Gao · IEEE Internet of Things Journal · 2025
This paper presents a hybrid data-driven method, termed moving average-Hankel-dynamic mode decomposition (MAHankDMD), for joint direction of arrival (DOA) and frequency estimation in environments affected by both radio frequency interference (RFI) and Gaussian white noise. The proposed approach integrates two key components: (1) a moving average-DMD filter that effectively mitigates Gaussian white noise and separates RFI from the source signal, and (2) a Hankel-DMD method that accurately estimates the DOA of the filtered signal and associates it with the corresponding frequency. The moving average-DMD stage first enhances the signal-to-noise ratio and improves the robustness of the estimation process through noise and inference mitigation, while the subsequent Hankel-DMD stage enables reliable parameter extraction even for overlapping sginals or strong interference conditions. Numerical simulations demonstrate the robustness of MAHankDMD, showing its ability to precisely estimate both DOA and frequency under challenging conditions involving RFI and Gaussian white noise interference. The proposed algorithm thus provides an effective solution for channel parameter estimation in complex noisy environments.