Multi-scale attention residual deep convolutional dealiasing network-assisted unambiguous ultra-long baseline high-precision microwave photonic angle of arrival estimation

Conventional interferometric angle of arrival (AOA) estimation faces a fundamental limitation: high-precision angle measurement relies on long baselines, which easily introduce phase ambiguity. This issue is particularly pronounced in ultra-wideband (UWB) systems, where traditional ambiguity resolution methods lack robustness. To overcome this challenge, this paper introduces a microwave photonic (MWP) AOA estimation algorithm enhanced by a multi-scale attention residual deep convolutional dealiasing network (MSAR-DCDN). The proposed method employs the MSAR-DCDN to directly learn the nonlinear relationship between the intermediate frequency (IF) phase and the signal's angle of arrival, thereby bypassing conventional ambiguity resolution and relaxing the traditional trade-off between baseline length and operational bandwidth. Simulations demonstrate that the algorithm maintains strong robustness across a wide signal-to-noise ratio (SNR) range from −10 dB to 25 dB, and achieves an angle estimation accuracy exceeding 93% even at a high baseline-to-wavelength ratio of 2. Outdoor experiments with an 821 mm ultra-long baseline (the baseline-to-wavelength ratio reaches 21.9) further validate the approach, yielding a root mean square error (RMSE) below 0.42°. These results demonstrate a significant performance improvement over both standard interferometric techniques and their ambiguity-resolved variants. By integrating the UWB capability of MWP with the advanced MSAR-DCDN-based deep learning mechanism, this work presents a novel and effective framework for high-precision AOA estimation in intelligent photonics sensing, which mitigates the baseline length constraint of traditional methods and realizes flexible baseline configuration.