scholarly journals A High-Resolution Algorithm for Supraharmonic Analysis Based on Multiple Measurement Vectors and Bayesian Compressive Sensing

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2559 ◽  
Author(s):  
Shuangyong Zhuang ◽  
Wei Zhao ◽  
Qing Wang ◽  
Zhe Wang ◽  
Lei Chen ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Electromagnetic Interference ◽  
Frequency Estimation ◽  
Estimation Error ◽  
Observation Time ◽  
Frequency Resolution ◽  
Inner Product ◽  
Signal Frequency ◽  
Multiple Measurement ◽  
Multiple Measurement Vectors

Supraharmonics emitted by electrical equipment have caused a series of electromagnetic interference in power systems. Conventional supraharmonic analysis algorithms, e.g., discrete Fourier transform (DFT), have a relatively low frequency resolution with a given observation time. Our previous work supplied a significant improvement on the frequency resolution based on multiple measurement vectors and orthogonal matching pursuit (MMV-OMP). In this paper, an improved algorithm for supraharmonic analysis, which employs Bayesian compressive sensing (BCS) for further improving the frequency resolution, is proposed. The performance of the proposed algorithm on the simulation signal and experimental data show that the frequency resolution can be improved by about a magnitude compared to that of the MMV-OMP algorithm, and the signal frequency estimation error is about 20 times better. In order to identify the signals in two adjacent frequency grids with one resolution, a normalized inner product criterion is proposed and verified by simulations. The proposed algorithm shows a potential for high-accuracy supraharmonic analysis.

scholarly journals Sparse GLONASS Signal Acquisition Based on Compressive Sensing and Multiple Measurement Vectors

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Guodong He ◽  
Maozhong Song ◽  
Shanshan Zhang ◽  
Peng Song ◽  
Xinwen Shu
Keyword(s):  
Compressive Sensing ◽  
Satellite System ◽  
New Method ◽  
Signal Acquisition ◽  
Multiple Measurement ◽  
Multiple Measurement Vectors ◽  
Navigation Data ◽  
Long Time ◽  
Global Navigation Satellite ◽  
Sparse Features

A sparse global navigation satellite system (GLONASS) signal acquisition method based on compressive sensing and multiple measurement vectors is proposed. The nonsparse GLONASS signal can be represented sparsely on our proposed dictionary which is designed based on the signal feature. Then, the GLONASS signal is sensed by a normalized orthogonal random matrix and acquired by the improved multiple measurement vectors acquisition algorithm. There are 10 cycles of pseudorandom codes in a navigation message, and these 10 pseudorandom codes have the same row sparse structure. So, the acquisition probability can be raised by row sparse features theoretically. A large number of simulated GLONASS signal experiments show that the acquisition probability increases with the increase in the measurement vector column dimension. Finally, the practical availability of the new method is verified by acquisition experiments with the real record GLONASS signal. The new method can reduce the storage space and energy loss of data transmission. We hope that the new method can be applied to field receivers that need to record and transmit navigation data for a long time.

scholarly journals Downward-Looking Linear Array 3D SAR Imaging Based on Multiple Measurement Vectors Model and Continuous Compressive Sensing

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Qi-yong Liu ◽  
Qun Zhang ◽  
Fu-fei Gu ◽  
Yi-chang Chen ◽  
Le Kang ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
3D Imaging ◽  
Linear Array ◽  
Multiple Measurement ◽  
Memory Space ◽  
Multiple Measurement Vectors ◽  
Huge Data ◽  
Imaging Result ◽  
Sar Imaging ◽  
Cross Track

This paper concerns the problems of huge data and off-grid effect of cross-track direction in downward-looking linear array (DLLA) 3D SAR imaging. Since the 3D imaging needs a great deal of memory space, we consider the methods of downsampling to reduce the data quantity. In the azimuth direction, we proposed a method based on the multiple measurement vectors (MMV) model, which can enhance computational efficiency and elevate the performance of antinoise, to recover the signal. Further, in cross-track direction, since the resolution is restricted by the length of array, as well as platform size, the influence of off-grid effect is more serious than azimuth direction. Continuous compressive sensing (CCS), which can solve the off-grid effect of the classical compressive sensing (CS), is presented to obtain the precise imaging result under the noise scenarios. Finally, we validate our method by extension numerical experiments.

scholarly journals An Algorithm for Estimating the Signal Frequency at the Output of a Channel with a Controlled Information Flow under Phase Noise Conditions

2021 ◽  
Vol 28 (4) ◽  
pp. 452-461
Author(s):  
Leonid Nikolaevich Kazakov ◽  
Evgenii Pavlovich Kubyshkin ◽  
Ilya Victorovich Lukyanov
Keyword(s):  
Phase Noise ◽  
Frequency Estimation ◽  
Estimation Error ◽  
Harmonic Signal ◽  
Frequency Synthesizers ◽  
Broadband Noise ◽  
Signal Frequency ◽  
High Signal ◽  
Radio Network ◽  
Estimation Algorithms

Research in the field of efficient frequency estimation algorithms is of great interest. The reason for this is the redistribution of the role of additive and phase noise in many modern radio-engineering applications. An example is the area of measuring radio devices, which usually operate at high signal-to-noise ratios (SNR). The estimation error is largely determined not by the broadband noise, but by the frequency and phase noise of the local oscillators of the receiving and transmitting devices. In particular, earlier works \\cite{Nikiforov} proposed an efficient computational algorithm for estimating the frequency of a quasi-harmonic signal based on the iterative calculation of the autocorrelation sequence (ACS). In \\cite{Volkov}, this algorithm was improved and its proximity to the Rao-Cramer boundary was shown (the sources of this noise are master oscillators and frequency synthesizers). Possibilities of frequency estimation in radio channels make it possible to significantly expand the functionality of the entire radio network. This can include, for example, the problem of adaptive distribution of information flows of a radio network. This also includes the tasks of synchronization and coherent signal processing. For these reasons, more research is needed on this algorithm, the calculation of theoretical boundaries and their comparison with the simulation results.

scholarly journals Influence of A/D Quantization in an Interpolated DFT Based System of Power Control with A Small Delay

2014 ◽  
Vol 21 (3) ◽  
pp. 423-432 ◽  
Author(s):  
Józef Borkowski ◽  
Dariusz Kania ◽  
Janusz Mroczka
Keyword(s):  
Time Window ◽  
Frequency Estimation ◽  
Estimation Error ◽  
Estimation Method ◽  
Quantization Noise ◽  
Estimation Accuracy ◽  
Signal Frequency ◽  
Practical Applications ◽  
Dominant Component ◽  
Signal Value

Abstract Fast and accurate grid signal frequency estimation is a very important issue in the control of renewable energy systems. Important factors that influence the estimation accuracy include the A/D converter parameters in the inverter control system. This paper presents the influence of the number of A/D converter bits b, the phase shift of the grid signal relative to the time window, the width of the time window relative to the grid signal period (expressed as a cycle in range (CiR) parameter) and the number of N samples obtained in this window with the A/D converter on the developed estimation method results. An increase in the number b by 8 decreases the estimation error by approximately 256 times. The largest estimation error occurs when the signal module maximum is in the time window center (for small values of CiR) or when the signal value is zero in the time window center (for large values of CiR). In practical applications, the dominant component of the frequency estimation error is the error caused by the quantization noise, and its range is from approximately 8×10-10 to 6×10-4.

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