Time-varying thresholds with Gaussian smoothing for one-bit DOA estimation in unequal power signals

In the field of wireless localization, one-bit quantization faces significant challenges in unequal power signal scenarios due to fixed thresholds and amplified quantization noise. This paper proposes a time-varying (TV) threshold strategy combined with Gaussian smoothing to enhance the Direction of Arrival (DOA) estimation. The method dynamically divides time windows and selects the median value of sub-intervals as the quantization threshold, enabling the algorithm to adapt to signal power variations and reduce interference between strong and weak signals. By reconstructing the covariance matrix using Newton’s iteration method and the gradient descent method, the signal subspace can be accurately recovered. Gaussian smoothing further suppresses high-frequency noise, enhancing the robustness while preserving the angular resolution of the Multiple Signal Classification (MUSIC) algorithm. Experimental results show that under a low Signal-to-Noise Ratio (SNR, −5 dB), compared with the unsmoothed method, the Root Mean Square Error (RMSE) is reduced by 25.6%, and sub-degree-level accuracy can be achieved even when strong and weak signals coexist. This approach provides a more effective solution for one-bit DOA estimation in practical unequal power signal scenarios and promotes the development of wireless localization.