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Direction of arrival (DoA) estimation plays a crucial role in various areas of signal processing. Although existing methods perform satisfactorily at moderately high signal-to-noise ratio (SNR) levels of the order of 0 dB, they often lack accuracy in low SNR scenarios ([Formula: see text]10 dB), which is critical in science applications like radio frequency interference (RFI) mitigation for radio astronomy. In this context, it is essential to estimate the DoA of a source of RFI at low SNR to prevent high-gain radio telescope receivers from saturating. In this paper, a novel DoA estimation method is proposed, which is designed specifically for low SNR scenarios. This method involves multiple subarrays and decomposition techniques to enhance the SNR of the RFI. By comparing the signal subspace with the noise subspace, we can identify the potential incident direction and perform refinement in subsequent steps. Accuracy is further improved by combining all DoA candidates together and utilizing a clustering method to remove outliers. We conducted simulations using Automatic Dependent Surveillance-Broadcast (ADS-B) signals and sine waves in Additive White Gaussian Noise (AWGN) and multipath fading channels for signals that will be detected by a 100[Formula: see text]m radio telescope. The results demonstrate that our proposed method outperforms prior algorithms in low SNR conditions.