A Hybrid LSTM-ResNet Deep Neural Network for Noise Reduction and Classification of V-Band Receiver Signals

Arab, Homa; Ghaffari, Iman; Evina, Romaric Mvone; Tatu, Serioja Ovidiu; Dufour, Steven

doi:10.1109/access.2022.3147980

Cited by 9 publications

(4 citation statements)

References 31 publications

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“…Figure 1C depicts the unit structure of the LSTM neural network, which regulates data through a forget gate, input gate, and output gate [34]. The forget gate f t σ(W f [J t−1 , x t ] + b f ) governs the information preserved from the preceding neuron.…”

Section: Mf-lstm Algorithmmentioning

confidence: 99%

Enhancing signal-to-noise ratio in active laser imaging under cloud and fog conditions through combined matched filtering and neural network

Cui,

Zhang,

Wang

et al. 2024

Front. Phys.

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Active laser imaging utilizes time-of-flight and echo intensity measurements to generate distance and intensity images of targets. However, scattering caused by cloud and fog particles, leads to imaging quality deterioration. In this study, we introduce a novel approach for improving imaging clarity in these environments. We employed a matched filtering method that leverages the distinction between signal and noise in the time domain to preliminarily extract the signal from one-dimensional photon-counting echo data. We further denoised the data by utilizing the Long Short-Term Memory (LSTM) neural network in extracting features from extended time-series data. The proposed method displayed notable improvement in the signal-to-noise ratio (SNR), from 7.227 dB to 31.35 dB, following an analysis of experimental data collected under cloud and fog conditions. Furthermore, processing positively affected the quality of the distance image with an increase in the structural similarity (SSIM) index from 0.7883 to 0.9070. Additionally, the point-cloud images were successfully restored. These findings suggest that the integration of matched filtering and the LSTM algorithm effectively enhances beam imaging quality in the presence of cloud and fog scattering. This method has potential application in various fields, including navigation, remote sensing, and other areas susceptible to complex environmental conditions.

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Section: Mf-lstm Algorithmmentioning

confidence: 99%

Enhancing signal-to-noise ratio in active laser imaging under cloud and fog conditions through combined matched filtering and neural network

Cui,

Zhang,

Wang

et al. 2024

Front. Phys.

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show abstract

“…In addition, in order to further improve the recognition accuracy of communication radiation sources, part of the research works [ 6 ], Refs. [ 20 , 21 ] combined the manually extracted expert features with deep learning to obtain fusion features. However, the essence of the above works is still only to extract inter-class separable features.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, data-driven strategy based on massive data and deep learning has become popular. A lot of studies [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ] have confirmed that deep learning has a strong feature extraction ability, which makes it superior to traditional methods in many recognition tasks. However, these works are mainly based on improved or new proposed network models.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Recognizing Space Radiation Sources Based on Multi-Scale Residual Prototype Learning Network

Liu

Guo

Zhao

et al. 2023

Sensors

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As a basic task and key link of space situational awareness, space target recognition has become crucial in threat analysis, communication reconnaissance and electronic countermeasures. Using the fingerprint features carried by the electromagnetic signal to recognize is an effective method. Because traditional radiation source recognition technologies are difficult to obtain satisfactory expert features, automatic feature extraction methods based on deep learning have become popular. Although many deep learning schemes have been proposed, most of them are only used to solve the inter-class separable problem and ignore the intra-class compactness. In addition, the openness of the real space may invalidate the existing closed-set recognition methods. In order to solve the above problems, inspired by the application of prototype learning in image recognition, we propose a novel method for recognizing space radiation sources based on a multi-scale residual prototype learning network (MSRPLNet). The method can be used for both the closed- and open-set recognition of space radiation sources. Furthermore, we also design a joint decision algorithm for an open-set recognition task to identify unknown radiation sources. To verify the effectiveness and reliability of the proposed method, we built a set of satellite signal observation and receiving systems in a real external environment and collected eight iridium signals. The experimental results show that the accuracy of our proposed method can reach 98.34% and 91.04% for the closed- and open-set recognition of eight iridium targets, respectively. Compared to similar research works, our method has obvious advantages.

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“…This method is based on the combination of LSTM optimization model and regularized K-SVD. The LSTM sequence prediction network demonstrates excellent proficiency in handling time series data, capturing both longterm and short-term dependencies [23] [24]. On the other hand, the regularization K-SVD algorithm excels in handling sparse representations of signals, capable of filtering out noise and extracting key signal features [25] [26].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Method Based on LSTM and Regularized K-SVD for Source Number Estimation

Pan,

Jiang,

Tian

et al. 2023

Preprint

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In complex environments with limited snapshots, most source number estimation algorithms encounter significant challenges. To address this issue, an enhancement method is proposed involving the integration of an optimized LSTM model with the regularized K-SVD algorithm.Initially, the optimization model is proposed based on the predictability of the LSTM sequence network. This model utilizes limited data to predict additional snapshots, thereby enhancing the covariance matrix of the signal. Subsequently, a sparse reconstruction algorithm based on the regularized K-SVD method is applied to effectively filter out noise, improving signal quality. The combination of the enhancement method with source number estimation techniques further boosts the performance of traditional algorithms. Experimental analysis confirms the superiority of the proposed enhancement method in colored noise environments. Simultaneously, in experiments with a limited number of snapshots, the proposed method significantly enhances estimation performance.

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A Hybrid LSTM-ResNet Deep Neural Network for Noise Reduction and Classification of V-Band Receiver Signals

Cited by 9 publications

References 31 publications

Enhancing signal-to-noise ratio in active laser imaging under cloud and fog conditions through combined matched filtering and neural network

Enhancing signal-to-noise ratio in active laser imaging under cloud and fog conditions through combined matched filtering and neural network

A Novel Method for Recognizing Space Radiation Sources Based on Multi-Scale Residual Prototype Learning Network

An Enhanced Method Based on LSTM and Regularized K-SVD for Source Number Estimation

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