Harmonic Frequency Estimation Based on Modified-MUSIC Algorithm in Power System

2015 ◽  
Vol 9 (1) ◽  
pp. 38-42 ◽  
Author(s):  
Xiangwen Sun ◽  
Ligong Sun
Keyword(s):  
Frequency Estimation ◽  
Estimation Method ◽  
Super Resolution ◽  
Small Data ◽  
Music Algorithm ◽  
Harmonic Frequency ◽  
Multiple Signal Classification ◽  
Analysis Techniques ◽  
Resolution Algorithm ◽  
Data Length

This paper presents a new harmonics frequency estimation method. Unlike the conventional harmonic frequency estimation method (fast Fourier transform), the new algorithm is based on spectrum analysis techniques often used to estimate the direction of angle; the most popular is the multiple signal classification (MUSIC) algorithm. The drawbacks of MUSIC algorithm are concluded. Improved-MUSIC approximation algorithm is introduced and compared with FFT based on algorithm for harmonic frequency estimation. Theoretical analysis and simulations show this algorithm is a super- resolution algorithm with small data length.

scholarly journals High-Efficiency Super-Resolution FMCW Radar Algorithm Based on FFT Estimation

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4018
Author(s):  
Bong-seok Kim ◽  
Youngseok Jin ◽  
Jonghun Lee ◽  
Sangdong Kim
Keyword(s):  
High Efficiency ◽  
Continuous Wave ◽  
Super Resolution ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Fmcw Radar ◽  
Similar Performance ◽  
Average Complexity ◽  
Resolution Algorithm ◽  
And Performance

This paper proposes a high-efficiency super-resolution frequency-modulated continuous-wave (FMCW) radar algorithm based on estimation by fast Fourier transform (FFT). In FMCW radar systems, the maximum number of samples is generally determined by the maximum detectable distance. However, targets are often closer than the maximum detectable distance. In this case, even if the number of samples is reduced, the ranges of targets can be estimated without degrading the performance. Based on this property, the proposed algorithm adaptively selects the number of samples used as input to the super-resolution algorithm depends on the coarsely estimated ranges of targets using the FFT. The proposed algorithm employs the reduced samples by the estimated distance by FFT as input to the super resolution algorithm instead of the maximum number of samples set by the maximum detectable distance. By doing so, the proposed algorithm achieves the similar performance of the conventional multiple signal classification algorithm (MUSIC), which is a representative of the super resolution algorithms while the performance does not degrade. Simulation results demonstrate the feasibility and performance improvement provided by the proposed algorithm; that is, the proposed algorithm achieves average complexity reduction of 88% compared to the conventional MUSIC algorithm while achieving its similar performance. Moreover, the improvement provided by the proposed algorithm was verified in practical conditions, as evidenced by our experimental results.

A Signal Detection Method for Harmonic Signal Submerged in Complex Noise Background

2013 ◽  
Vol 397-400 ◽  
pp. 2262-2265 ◽  
Author(s):  
Ai Juan Quan ◽  
Xiao Dong Sun ◽  
Lan Xiang Zhu
Keyword(s):  
Background Noise ◽  
Detection Method ◽  
Wavelet Packet ◽  
Harmonic Signal ◽  
Chaotic Signal ◽  
Music Algorithm ◽  
Harmonic Frequency ◽  
Multiple Signal Classification ◽  
Chaotic Signals ◽  
Correlation Characteristic

This paper presents a method to detect weak harmonic signal embedded in chaotic noise. Using different correlation characteristic of harmonic and chaotic signal ,we can transform the sample signal to a new data sequence which has new harmonic . The new harmonic frequency is m times of the original harmonic and beyond the center bandwidth of noise. Then use wavelet packet decomposition to analysis the energy distribution of harmonic and chaotic signals and extract the component which the harmonic energy concentrated on, In the end, a multiple signal classification (MUSIC) algorithm is employed to estimate harmonic frequencies . The method suit for the complex background noise (strong chaotic noise and gaussian noise).

scholarly journals Harmonic Frequency Estimation Algorithm Based on MSWF in Power System

2015 ◽  
Vol 9 (1) ◽  
pp. 524-529
Author(s):  
Cao Zhe ◽  
Sun Xiangwen ◽  
Niu Xinwen
Keyword(s):  
Computational Complexity ◽  
Power System ◽  
Frequency Estimation ◽  
Recursive Algorithm ◽  
Wiener Filter ◽  
Estimation Algorithm ◽  
Music Algorithm ◽  
Harmonic Frequency ◽  
Multi Stage ◽  
Data Volume

This paper pointed out the disadvantage of harmonic frequency estimation algorithm in current power system, presented the MUSIC (multiple signal classification)-based harmonic frequency estimation algorithm in power system, and analyzed the computational complexity of the MUSIC algorithm. In order to reduce the computational complexity of conventional MUSIC algorithm and to increase the real-time characteristic of harmonic frequency estimation algorithm, we combined the multi-stage wiener filter (MSWF) recursive algorithm and MUSIC algorithm so as to avoid the subspace decomposition process of the conventional MUSIC algorithm, thus realizing the purpose of significantly reducing the computational complexity of the MUSIC algorithm. Through theoretical analysis and simulation experiments, we find that the algorithm proposed in this paper is of excellent resolution characteristic, and less dependent on data volume.

Harmonic Estimation Algorithm for Power System Based on Improved MUISC and Linear Neural Networks

2015 ◽  
Vol 9 (1) ◽  
pp. 445-451 ◽  
Author(s):  
Sun Ligong Ligong ◽  
Sun Xiangwen ◽  
Xiang Fei
Keyword(s):  
Neural Network ◽  
Power System ◽  
Frequency Estimation ◽  
Wiener Filter ◽  
Estimation Algorithm ◽  
Estimation Accuracy ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Harmonic Phase ◽  
Linear Neural Network

The paper proposes harmonic estimation algorithm for power system based on Multiple Signal Classification (MUSIC) and linear neural network because of the insufficiency of harmonic frequency estimation algorithm. The conventional MUSIC algorithm has the advantage of higher estimation accuracy, while the disadvantage is that the computational complexity is high and it cannot estimate the harmonic phase and amplitude. In the paper, a new harmonic estimation algorithm for power system is constructed with combining the MUSIC algorithm, the multistage Wiener filter (MSWF) and linear neural network. Theoretic analysis and simulation experiments show that the requirement to data is relatively low, and has good harmonic estimation accuracy and reliability.

scholarly journals Joint DOA and Polarization Estimation for Unequal Power Sources

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Qingyuan Fang ◽  
Yong Han ◽  
Ming Jin ◽  
Wenyi Dong
Keyword(s):  
Estimation Method ◽  
Estimation Methods ◽  
Invariance Property ◽  
Music Algorithm ◽  
Power Sources ◽  
Multiple Signal Classification ◽  
Power Difference ◽  
Joint Direction ◽  
Polarization Estimation ◽  
Cramer Rao Bound

For most joint direction of arrival (DOA) and polarization estimation methods, the performances of proposed methods in dealing with unequal power sources are not discussed. However, sources with unequal powers apparently exist widely in actual applications. In this study, we propose a joint DOA and polarization estimation method for unequal power sources by utilizing the invariance property of noise subspace (IPNS) to the power of sources. This work extends the IPNS method to the dual polarized antenna array for joint DOA and polarization estimation. Moreover, we theoretically prove that the IPNS remains valid even when the sources are correlated. The computer simulations illustrate that the proposed method can effectively estimate the DOA and polarization parameters as the power difference between sources increases, as opposed to the polarimetric multiple signal classification (MUSIC) algorithm, which suffers from degradation in resolution probability. In addition, the performances of the proposed method are provided, as well the Cramer Rao Bound (CRB), which show approximate performance as the polarimetric MUSIC algorithm.

Implementation of Unitary Music Algorithm Using Xilinx System Generator

2013 ◽  
Vol 748 ◽  
pp. 629-633
Author(s):  
Mer Wan Lounici ◽  
Xiao Ming Luan
Keyword(s):  
Super Resolution ◽  
Eigenvalue Decomposition ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
System Generator ◽  
Resolution Capability ◽  
Coordinate Rotation ◽  
Field Programmable ◽  
Computer Based ◽  
Xilinx System Generator

The MUltiple SIgnal Classification MUSIC algorithm is a kind of DOA (Direction Of Arrival) estimation technique based on eigenvalue decomposition, which is also called subspace-based method [5]. In addition of its super resolution capability, MUSIC is very suitable for integration on logic circuit devices such as FPGAs (Field Programmable Gate Array).this paper proposes an implementation of unitary MUSIC algorithm using Xilinx System Generator (XSG). The design proposed uses CORDIC (COordinate Rotation DIgital Computer) -based Triangular Systolic Array for QR- decomposition to deal with EVD (eigenvalue decomposition). The MUSIC spectrum is computed with spatial DFT (Discrete Fourier Transform) using FFT block offered by Simulink- Xilinx blockset library. The performance of eight elements antenna array system was obtained and discussed.

scholarly journals Super-Resolution Range and Velocity Estimations for SFA-OFDM Radar

2022 ◽  
Vol 14 (2) ◽  
pp. 278
Author(s):  
Zhixing Liu ◽  
Yinghui Quan ◽  
Yaojun Wu ◽  
Mengdao Xing
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Linear Array ◽  
Super Resolution ◽  
Music Algorithm ◽  
Excellent Performance ◽  
Multiple Signal Classification ◽  
Parameter Estimations ◽  
Coherent Integration ◽  
Ofdm Signal ◽  
Frequency Agile

Sparse frequency agile orthogonal frequency division multiplexing (SFA-OFDM) signal brings excellent performance to electronic counter-countermeasures (ECCM) and reduces the complexity of the radar system. However, frequency agility makes coherent processing a much more challenging task for the radar, which leads to the discontinuity of the echo phase in a coherent processing interval (CPI), so the fast Fourier transform (FFT)-based method is no longer a valid way to complete the coherent integration. To overcome this problem, we proposed a novel scheme to estimate both super-resolution range and velocity. The subcarriers of each pulse are firstly synthesized in time domain. Then, the range and velocity estimations for the SFA-OFDM radar are regarded as the parameter estimations of a linear array. Finally, both the super-resolution range and velocity are obtained by exploiting the multiple signal classification (MUSIC) algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed method.

Interference Localization via Direction of Arrival Estimation for Satellite with Planar Array

2014 ◽  
Vol 1049-1050 ◽  
pp. 1788-1791
Author(s):  
Xiao Feng Qiu ◽  
Xiao Fei Zhang
Keyword(s):  
Computational Cost ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Music Algorithm ◽  
Angle Estimation ◽  
Multiple Signal Classification ◽  
Estimation Performance ◽  
Planar Array ◽  
Esprit Algorithm

This paper presents the model of satellite planar array, and interference localization via direction of arrival (DOA) estimation. We derive a dimension reduction DOA estimaton algorithm therein. The proposed algorithm, which only requires a one-dimensional local searching, can avoid the high computational cost within two-dimensional multiple signal classification (2D-MUSIC) algorithm. We illustrate that the proposed algorithm has better angle estimation performance than estimation method of signal parameters via rotational invariance technique (ESPRIT) algorithm, and has very close angle estimation performance to 2D-MUSIC algorithm. Furthermore, our algorithm requires no extra pairing. Simulation results present the usefulness of our algorithm.

A Two Dimension Signal Parameter Estimation Method Based on Super Resolution Technology

2013 ◽  
Vol 712-715 ◽  
pp. 2007-2014
Author(s):  
Ying Xu ◽  
Jun Zhao
Keyword(s):  
Signal Processing ◽  
Parameter Estimation ◽  
Array Signal Processing ◽  
Estimation Method ◽  
Super Resolution ◽  
Parameters Estimation ◽  
Joint Estimation ◽  
Spectrum Estimation ◽  
Processing Parameter ◽  
Music Algorithm

The 2D spectrum estimation based on uniform linear array is studied in this paper. MUSIC method is firstly introduced into array signal processing to realize the frequency and angle joint estimation. And the modified MUSIC method is then applied for two dim array signal processing to fulfill joint parameters estimation of coherent signal sources. Simulation results show the validity of the proposed method. Keywords: Super resolution;Array signal processing;Parameter estimation;MUSIC algorithm

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