Adaptive Beamforming Based on Dual Convolutional Autoencoder

JIANG Yilin; LI Shuai; ZHENG Pei; TANG Yuanbo

doi:10.11999/JEIT240486

Volume 47 Issue 2

Feb. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(2): 510-518

LIU Jing, SHENG Mingxing, SONG Dawei, SHANG She, HAN Chongzhao. Compact Sensing Matrix Pursuit Algorithm for Radars with High Resolution[J]. Journal of Electronics & Information Technology, 2016, 38(8): 1950-1955. doi: 10.11999/JEIT151135

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JIANG Yilin, LI Shuai, ZHENG Pei, TANG Yuanbo. Adaptive Beamforming Based on Dual Convolutional Autoencoder[J]. Journal of Electronics & Information Technology, 2025, 47(2): 510-518. doi: 10.11999/JEIT240486

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Adaptive Beamforming Based on Dual Convolutional Autoencoder

doi: 10.11999/JEIT240486

JIANG Yilin^{1, 2},
LI Shuai^{1
,
,},
ZHENG Pei³,
TANG Yuanbo¹

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
AVIC United Technology Center for Electromagnetic Spectrum Collaborative Detection and Intelligent cognition, Harbin 150001, China
3.
State Key Laboratory of Experimental Physics and Computational Mathematics, Beijing 100876, China

Funds: The National Defense Science and Technology Strengthening Program (2019-JCJQ-ZD-067-00)

Received Date: 2024-06-14
Rev Recd Date: 2025-02-11

Available Online: 2025-02-19

Publish Date: 2025-02-28

Abstract

Abstract

Objective Most traditional beamforming techniques and adaptive beamforming methods rely on reference signals. These methods require prior knowledge of the signal frequency and Direction of Arrival (DOA) at the array for beamforming. However, in low Signal-to-Noise Ratio (SNR) environments, obtaining the frequency and DOA of the incident signals is extremely challenging. This difficulty leads to significant performance degradation in reference-signal-based beamforming, limiting its applicability in tasks such as electronic reconnaissance and electronic countermeasures in low SNR conditions. This paper addresses the challenge of enabling antenna arrays to perform adaptive beamforming for incident signals with unknown frequencies and DOAs in low-SNR environments. Methods This paper proposes a Dual Convolutional AutoEncoder-Adaptive Beamforming (DCAE-ABF) method for blind reception. The approach leverages dual Convolutional Autoencoders (CAEs) to extract features from both the array-received signal and the radiation source signal, utilizing extensive air-domain statistical information with joint time-frequency domain constraints. A Deep Neural Network (DNN) connects the feature encodings from the two CAEs to construct the DCAE network. This method enables adaptive beamforming in low SNR environments, even when the incident signal’s frequency and DOA are unknown, facilitating blind reception. Results and Discussions Simulation results demonstrate that the proposed DCAE-ABF method can rapidly and accurately adjust the beam direction for incident signals with unknown frequencies and directions of arrival in a low SNR environment, effectively orienting the beam towards the incident signals for optimal reception. This method improves the output signal’s SNR, with the SNR gain significantly exceeding that of traditional beamforming techniques (Fig. 4, Fig. 6). Furthermore, the SNR gain achieved by this method remains stable even when the frequency and angle of the incident signal vary (Fig. 5). Conclusions This paper presents an adaptive beamforming method based on dual convolutional autoencoders. The method outperforms the other three approaches discussed in this study when applied to incident signals with unknown directions of arrival in low SNR environments. Even when the DOA is unknown, the method effectively utilizes the spatial information accumulated during autoencoder training. It can extract features from the array signals and adaptively form beams directed at the incident signals, achieving optimal reception. This approach enables blind adaptive beamforming for signals with unknown frequencies and directions of arrival, significantly improving the SNR of the incident signals.
- Adaptive beamforming,
- Convolutional autoencoder,
- Blind beamforming,
- Signal-to-noise ratio gain

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References(18)

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