Frequency domain sensing system using random modulation pre‐integrator

Zhao, Yijiu; Wang, Houjun; Zhuang, Xinhua; Dai, Zhijian; Wu, Hao

doi:10.1049/iet-smt.2012.0134

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…An analog-to-information converter (AIC) is a vital under-sampling technique based on CS theory and samples the analog signals at the sub-Nyquist rate [16]. Recently, many AIC architectures have been designed in the literature, such as random demodulation AIC (RD-AIC) [17], modulated wideband converters (MWC) [18], and random modulation preintegrator (RMPI) [19]. Since RD-AIC has a broad application prospect with simple structure and low cost, it is usually used in practice to compress analog signals:…”

Section: Introductionmentioning

confidence: 99%

Incoherence Analysis of RD-AIC-Based Observation Matrix and Its Application in Power Quality Disturbance Signal

Liu

Tang

2021

Mathematical Problems in Engineering

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In the theory of compressed sensing, restricted isometry property (RIP) decides the universality and reconstruction robustness of an observation matrix. At present, an observation matrix based on RD-AIC (RD-AIC-based observation matrix) can compress sparse continuous signals with a simple structure, but RIP analysis of this matrix is lack and challenging to prove. In this paper, this problem is relaxed. Instead, we demonstrate the incoherence analysis process, derive the orthogonality and nonsingularity of the matrix, propose the generalized relevance calculation steps of the matrix, and propose the hardware parameter design principles to improve the incoherence of the matrix. Moreover, compression and reconstruction experiments used in power quality disturbance signals are developed for testing the incoherence. The results show that the RD-AIC-based observation matrix has substantial incoherence under suitable hardware parameters, equivalent to the Gaussian random matrix and the Bernoulli random matrix.

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Section: Introductionmentioning

confidence: 99%

Incoherence Analysis of RD-AIC-Based Observation Matrix and Its Application in Power Quality Disturbance Signal

Liu

Tang

2021

Mathematical Problems in Engineering

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“…Recently, compressive sensing (CS) theory [1][2][3][4][5] was proposed, in which sparse signals can be sampled at an extremely low frequency and recovered accurately. Researchers have applied CS theory to actual analogue signal acquisition [6][7][8][9][10][11] such as in the random demodulator (RD) [6,9], multi-coset sampler [8,12], random modulation preintegration [13], and modulated wideband converter (MWC) [14][15][16][17]. Mishali and Eldar developed the MWC [14][15][16][17], which is based on the CS technique, and which can be used to sample analogue multi-band signals over a wide spectral range.…”

Section: Introductionmentioning

confidence: 99%

Successive‐phase correction calibration method for modulated wideband converter system

Jiang

Deng

et al. 2019

IET signal process.

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The modulated wideband converter (MWC) is a sub-Nyquist sampling system recently proposed for sampling sparse multi-band signals. However, due to non-ideal analogue components, transfer-matrix calibration is required for the successful reconstruction. In practice, it is difficult to control the phases of the consecutive sinusoidal inputs precisely which are used to calibrate the transfer-matrix. Here, the authors propose a method of transfer-matrix calibration for the MWC with digital expanded channels when the phases of the sinusoidal inputs are unknown. As the oblique elements of the MWC transfer-matrix are equal, the authors can estimate the unknown phases first. The authors then obtain the actual transfer-matrix by phase correction. Finally, the validity of this method is verified by simulation and hardware experiment. The system performance is measured by computing the signal-to-noise ratio and the mean-square error between the original and reconstructed signals.

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“…However, it employs a bank of integrators which have to be reset in each sampling period. The reset process is non-trivial in practice, and it may limit the application of AIC [32]. In this paper, we employ the low pass filters instead of integrators to propose an AIC-based system to estimate wideband power spectrum by low-rate ADCs.…”

Section: Introductionmentioning

confidence: 99%

Wideband Power Spectrum Estimation Based on Sub-Nyquist Sampling in Cognitive Radio Networks

Zhao

Zheng

et al. 2019

IEEE Access

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The wideband spectrum estimation is an essential step in the wireless network. In order to avoid employing power-hungry high-rate analog-to-digital converters (ADCs), the CS-based sub-Nyquist sampling approaches are used to estimate the wideband spectrum. In this paper, we propose a sub-Nyquist sampling system based on the analog to information converter (AIC), and the proposed system is constructed by multiple parallel channels with a banks of low pass filters. The system model is constructed in the time domain. To estimate the power spectrum, we define a new power spectrum of samples with a finite length, called the circular power spectrum (CPS), served as the aim we strive to estimate. The defined CPS can clearly reflect the power of the signal varying with frequency and is also with the same length as the equivalent digital samples. The experimental results indicate that the defined CPS can be successfully estimated from samples captured by the proposed sub-Nyquist sampling system whose overall sampling rate is much lower than the Nyquist rate.

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Frequency domain sensing system using random modulation pre‐integrator

Cited by 6 publications

References 13 publications

Incoherence Analysis of RD-AIC-Based Observation Matrix and Its Application in Power Quality Disturbance Signal

Incoherence Analysis of RD-AIC-Based Observation Matrix and Its Application in Power Quality Disturbance Signal

Successive‐phase correction calibration method for modulated wideband converter system

Wideband Power Spectrum Estimation Based on Sub-Nyquist Sampling in Cognitive Radio Networks

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