Source number of single-channel signals intelligent estimation via signal reconstruction

Zhang, Yunwei; Gao, Yong

doi:10.1016/j.dsp.2023.104100

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…Consequently, the received signal consists of linear noise and a limited number of * Authors to whom any correspondence should be addressed. source signals with the same modulation [2]. Accurate detection of the source number is crucial for the performance of direction-of-arrival measurement algorithms and most blind signal separation algorithms, as inaccurate detection can severely degrade their effectiveness [3,4].…”

Section: Introductionmentioning

confidence: 99%

An effective source number detection method for single-channel signals based on signal reconstruction and deep learning at low SNR

Zhang,

Wei,

Gao

2023

Meas. Sci. Technol.

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Determining the number of sources under low signal-to-noise ratio (SNR) and signal interference with the same frequency and modulation presents a significant challenge. To address this challenge, we propose a novel method for detecting the number of signal sources from single-channel that leverages signal reconstruction and deep learning. The method employs subspace projection based on the Hankel matrix to reconstruct the measured single-channel signals, effectively suppressing noise. Furthermore, we incorporate the correlation of information and the integrity of feature in the signal, by fusing the in-phase component, quadrature component, and frequency spectrum feature of the reconstructed complex signal into a one-dimensional feature suitable for convolutional neural network (CNN). To address the source number detection task, we design a one-dimensional CNN based on convolutional block attention module, transforming it into a classification problem. Finally, experimental measurements demonstrate the effectiveness of our proposed method, with an detection accuracy of 94% even at an SNR of −10 dB.

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Section: Introductionmentioning

confidence: 99%

An effective source number detection method for single-channel signals based on signal reconstruction and deep learning at low SNR

Zhang,

Wei,

Gao

2023

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

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Modulation Recognition based on One-Dimensional Complex Convolutional Networks under Low SNR

Zhang,

Gao

2024

2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)

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Recovery of Discrete-Time Signal Based on the Moving Average Model and Estimation of the Samples Correlation in Forward and Reverse Forecasting

Yakimov

2024

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The article discusses the development of mathematical support for the recovery of the values of discrete-time sequence samples obtained as a result of uniform sampling of a continuous signal. The recovery problem of discrete-time sequence samples is solved for a signal that can be considered stationary or stationary at least in a broad sense (quasi-stationary). The development of mathematical support for the recovery of the values of signal samples was carried out on the basis of constructing a moving average model and estimating the correlation of signal samples over time with forward and reverse forecasting. Estimates of the signal correlation function necessary to recover sample sections with lost values are calculated from samples with known values. Correlation function estimates can be calculated regardless of the location of the recovery area when the condition of stationarity of the signal is met. The obtained estimates of the correlation function samples can be used for both forward and reverse forecasting. Moreover, even if it is necessary to recover several problem sections, it is enough to calculate only once the sample of correlation function estimates necessary for their restoration. The resulting mathematical solution to the problem became the basis for the development of algorithmic support. Test tests and functional checks of the algorithmic support were carried out on the basis of simulation using a signal model representing an additive sum of harmonic components with random initial phases. The simulation results showed that the calculation of estimates of the lost sample values is carried out with a fairly low error, both in forward and reverse forecasting, as well as when they are used together. In practice, the choice of a sequence recovery algorithm based on forward or reverse forecasting will be determined based on the actual conditions of its processing. In particular, if previous samples with known values are not enough to carry out forward forecasting, then the reverse forecasting procedure is implemented and vice versa. The developed algorithmic support can be implemented in the form of metrologically significant software for digital signal processing systems.

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Source number of single-channel signals intelligent estimation via signal reconstruction

Cited by 4 publications

References 31 publications

An effective source number detection method for single-channel signals based on signal reconstruction and deep learning at low SNR

An effective source number detection method for single-channel signals based on signal reconstruction and deep learning at low SNR

Modulation Recognition based on One-Dimensional Complex Convolutional Networks under Low SNR

Recovery of Discrete-Time Signal Based on the Moving Average Model and Estimation of the Samples Correlation in Forward and Reverse Forecasting

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