Multi-scale attention residual deep convolutional dealiasing network-assisted unambiguous ultra-long baseline high-precision microwave photonic angle of arrival estimation
多尺度注意力残差深度卷积去模糊网络辅助的明确超长基线高精度微波光子到达角估计
マルチスケールアテンション残差ディープ畳み込みデイアライズネットワーク支援の無矛盾超長ベースライン高精度マイクロ波光子到達角推定
다중 스케일 어텐션 잔차 딥 컨볼루션 디앨리어싱 네트워크 보조 무의미 초장기 베이스라인 고정밀 마이크로파 광학 도달각 추정
Red de desaliasamiento residual de convolución profunda con atención multiescala asistida para estimación precisa de ángulo de llegada de microondas fotónicas de base ultra larga sin ambigüedades
Réseau de débruitage convolutif profond résiduel avec attention multi-échelle assisté par estimation précise d'angle d'arrivée micro-ondes phototonique sans ambiguïté sur très longue base
Многоуровневая остаточная глубокая сверточная сеть для устранения алиасинга с механизмом внимания, облегчающая однозначную оценку угла прихода микроволново-фотонного сигнала с сверхдлинной базовой линией высокой точности
Xianglin Chen ¹, Yin Li ², Shiru Song3, Yalin Yao ¹, He Cui ¹, Xuan Li ⁴, Zhe Guo ⁴, Yinlong Tan ³, Taolin Liu ⁴, Tian Jiang ⁴ ⁵
¹ College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China
中国 长沙 国防科技大学计算机学院
² Defense Innovation Institute, Academy of Military Sciences, Beijing 100071, China
中国 北京 中国人民解放军军事科学院国防科技创新研究院
³ College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China
中国 长沙 中国人民解放军国防科技大学前沿交叉学科学院
⁴ College of Science, National University of Defense Technology, Changsha 410073, China
中国 长沙 中国人民解放军国防科技大学理学院
⁵ Hunan Research Center of the Basic Discipline for Physical States, Changsha 410073, China
中国 长沙 湖南省物态模拟与调控基础学科研究中心
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.
