文献类型：期刊文献

英文题名：RNN With High Precision and Noise Immunity: A Robust and Learning-Free Method for Beamforming

作者：Lin, Cong[1];Jiang, Zhihui[1];Cong, Jingyu[2,3];Zou, Lilan[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Sun Yat Sen Univ, Sch Elect & Commun Engn, Shenzhen 518107, Peoples R China;[3]Sun Yat Sen Univ, Sch Elect & Commun Engn, Guangzhou, Peoples R China

年份：2025

卷号：12

期号：11

起止页码：15779

外文期刊名：IEEE INTERNET OF THINGS JOURNAL

收录：SCI-EXPANDED(收录号：WOS:001492213100004)、、EI(收录号：20250417766104)、Scopus(收录号：2-s2.0-85215952115)、WOS

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62272109; in part by the Stable Supporting Fund of Acoustic Science and Technology Laboratory under Grant JCKYS2024604SSJS00301; and in part by the Undergraduate Innovation Team Project of Guangdong Ocean University under Grant CXTD2024011.

语种：英文

外文关键词：Beamforming; neural network; Newton model; weight vector; Beamforming; neural network; Newton model; weight vector

外文摘要：Recurrent neural networks (RNNs), recognized for their high accuracy and strong robustness. However, the adoption of RNN-based solutions for array signal beamforming is still in its infancy, as RNNs are very sensitive to noise and cannot easily overcome the impact of environmental noise on the solution. To address these limitations, this study proposes the dynamic integrated enhanced neural network (DIENN) for array signal beamforming, which incorporates an error integral feedback mechanism. This mechanism enhances the robustness and noise immunity of the model, enabling it to maintain stable performance under dynamic noise environments. Compared with state-of-the-art (SOTA) methods, the proposed model has higher stability in beamforming tasks while providing excellent results under three interference conditions where the other algorithms of the comparison failed. The residual accuracy achieved in the case of time-varying disturbance was 10(-15). The feasibility of the model was verified by applying it to experimental data. To our knowledge, this is the first work to develop a zero-reset RNN for array signal processing.