Multistage 2D DoA Estimation in Low SNR

Direction of arrival (DoA) estimation has an important role in various applications and is widely used in modern communication, where signal power is higher than noise power for it to be decoded. Existing methods do not perform as accurately in low signal to noise ratio (SNR) as they do in higher SNR. However, passive sensing like radio astronomy or remote sensing operates at extremely low SNR, where the weakest radio frequency interference (RFI) from communication system impairs the scientific observations. Hence, it is essential to estimate the DoA of RFI at low SNR so that it can be removed at the telescopes or radiometers. In this paper, we propose a three stage algorithm that methodically exploits digital beamforming, creates virtual subarrays, inspects multiple options and introduces clustering to estimate the DoA in low SNRs. The proposed algorithm is simulated with sinusoidal as well as Automatic Dependent Surveillance-Broadcast (ADS-B) signals in Additive White Gaussian Noise (AWGN) and multipath fading channels. Experimental results show the proposed method outperforms well-known MUSIC (MUltiple SIgnal Classification) algorithm in low SNR.