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The burg algorithm with Extrapolation for Improving the Frequency Estimation

INFORMATICA 2011年第2期22卷 177-188页

作者： Kazlauskas, Kazys Vilnius Univ Inst Math & Informat LT-08663 Vilnius Lithuania

The paper presents a novel method for improving the estimates of closely-spaced frequencies of a short length signal in additive Gaussian noise based on the burg algorithm with extrapolation. The proposed method is implemented in two consecutive steps. In the first step, the burg algorithm is used to estimate the parameters of the predictive filter, while in the second step the extrapolation technique of the signal is used to improve the frequency estimates. The experimental results demonstrate that the frequency estimates of the short length signal, using the burg algorithm with extrapolation, are more accurate than the frequency estimates using the burg algorithm without extrapolation.

关键词： frequency estimation short length signals burg algorithm extrapolation

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The Simulation of burg algorithm for AR Model Power Spectrum Estimation

The Simulation of Burg Algorithm for AR Model Power Spectrum...

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International Conference on Materials Science and Engineering Science

作者： He, Hong Huang, Xing Liu, Fang Zhang, Da-Jian Hou, Ming-Feng Tianjin Univ Technol Tianjin Key Lab Control Theory & Applicat Complic Tianjin Peoples R China Res Inst Meausre Technol Maanshan Anhui Peoples R China

ISBN: (纸本)9783037850169

Classical spectral estimation, whether the direct method or indirect method, are available to fast Fourier transform method, their concepts are clear and easy to understand, its resolution is proportional to 2 pi/N. This paper describes the commonly used in modern power spectrum estimation based on AR model burg algorithm, MATLAB simulation shows this method to estimate the power spectrum curve, results showed that the burg algorithm in spectral resolution and model in the order of sub-option square than the self-correlation algorithm has obvious advantages.

关键词： burg algorithm spectral estimation AR Model

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FPGA Implementation of Covariance Lattice LPC Method Using burg algorithm 9

FPGA Implementation of Covariance Lattice LPC Method Using B...

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9th International Conference on Advanced Infocomm Technology (ICAIT)

作者： Song, Dongpeng Ren, Shiwei Zhuo, Jin Yang, Hao Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China

ISBN: (纸本)9781538636282

Aiming at the defects of classical lattice algorithm of speech signal linear prediction analysis, an improved algorithm for hardware implementation is proposed in this paper. The recursive form and symmetry characteristic is used to transform the formula of intermediate covariance and three mean square errors, which increase the speed of hardware calculation. The improved algorithm is implementation on Virtex5 XC5VLX110T FPGA, with the maximum frequency of 100.220MHz, while the latency is 9.54us and 17195 LUTs are utilized. The functional test shows that the normalized MSE between MATLAB and FPGA is 1.12% and the reflection

关键词： linear prediction analysis lattice method burg algorithm FPGA

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A harmonic impedance estimation method based on AR model and burg algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2022年 202卷 107568-107568页

作者： Shu, Qin Fan, Yu Xu, Fangwei Wang, Chang He, Jinyan Sichuan Univ Coll Elect Engn Chengdu 610000 Peoples R China

With the development of smart grid, a lot of nonlinear loads and new energy power stations are connected into the power network. There are a large number of harmonic sources in the power grid, harmonic distortion has become one of the main power quality concerns in recent years. Utility side harmonic impedance estimation is important for evaluating harmonic emission levels and determining the harmonic responsibility. In the traditional methods, the harmonic voltage and current at the PCC are considered to satisfy the normal distribution and the timing correlation of them is ignored which contains important information that can be used to estimate the harmonic impedance. This paper proposes a new method for harmonic impedance estimation based on the burg algorithm and AR model. In this paper, harmonic voltage and current are considered to satisfy AR model including timing correlation. The reflection coefficient is calculated by minimizing the average power of the forward and backward errors in the AR model. Then, the utility side harmonic impedance is estimated by minimizing the apparent power. The proposed method is robust, and the error of the estimation result is small by the proposed method. Whats more, the proposed method is not limited by some traditional assumptions, such as the background harmonics are stable and small, the customer harmonic impedance is much larger than the utility, and the harmonic sources of both sides are independent, and so on.

关键词： Smart grid Utility impedance estimation AR model burg algorithm Apparent power

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Generalized Levinson-Durbin and burg algorithms

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JOURNAL OF ECONOMETRICS 2004年第1-2期118卷 129-149页

作者： Brockwell, PJ Dahlhaus, R Heidelberg Univ Inst Angew Math D-69120 Heidelberg Germany Colorado State Univ Dept Stat Ft Collins CO 80523 USA

A basic recursive property of orthogonal projections is presented which leads easily to a variety of different prediction algorithms. Examples are the classical Levinson-Durbin and burg algorithms and a subset Whittle algorithm of Penm and Terrell. In addition, some new algorithms are derived including easily applied algorithms for the recursive calculation of best h-step predictors and a burg algorithm for the best subset predictor. The relation to lattice algorithms is discussed. (C) 2003 Elsevier B.V. All rights reserved.

关键词： recursive autoregression Levinson-Durbin algorithm burg algorithm subset modelling multistep prediction

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Target Detection Through Riemannian Geometric Approach With Application to Drone Detection

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IEEE ACCESS 2021年 9卷 123950-123963页

作者： Chahrour, Hossein Dansereau, Richard M. Rajan, Sreeraman Balaji, Bhashyam Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada Def Res & Dev Canada Radar Sensing & Exploitat Sect Ottawa ON K1A 0Z4 Canada

Radar detection of small drones in presence of noise and clutter is considered from a differential geometry viewpoint. The drone detection problem is challenging due to low radar cross section (RCS) of drones, especially in cluttered environments and when drones fly low and slow in urban areas. This paper proposes two detection techniques, the Riemannian-Brauer matrix (RBM) and the angle-based hybrid-Brauer (ABHB), to improve the probability of drone detection under small sample size and low signal-to-clutter ratio (SCR). These techniques are based on the regularized burg algorithm (RBA), the Brauer disc (BD) theorem, and the Riemannian mean and distance. Both techniques exploit the RBA to obtain a Toeplitz Hermitian positive definite (THPD) covariance matrix from each snapshot and apply the BD theorem to cluster the clutter-plus-noise THPD covariance matrices. The proposed Riemannian-Brauer matrix technique is based on the Riemannian distance between the Riemannian mean of clutter-plus-noise cluster and potential targets. The proposed angle-based hybrid-Brauer technique uses the Euclidean tangent space and the Riemannian geodesical distances between the Riemannian mean, the Riemannian median and the potential target point. The angle at the potential target on the manifold is computed using the law of cosines on the manifold. The proposed detection techniques show advantage over the fast Fourier transform, the Riemannian distance-based matrix and the Kullback-Leibler (KLB) divergence detectors. The validity of both proposed techniques are demonstrated with real data.

关键词： Covariance matrices Drones Detectors Radar Radar detection Manifolds Radar cross-sections Differential geometry burg algorithm Brauer disc theorem Riemannian mean and distance Toeplitz Hermitian positive definite covariance matrices

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APPLICATION OF MAXIMUM-ENTROPY SPECTRAL-ANALYSIS TO ESTIMATION OF PARAMETERS OF ARMA MODELS BY THE ITIF METHOD

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ELECTRONICS LETTERS 1985年第14期21卷 611-613页

作者： MARTIN, RG PEREZ, MCC IBRAHIM, SA OLMEDO, BG Dpto. de Electricidad Facultad de Ciencias Granada Spain

In the letter we show how it is possible to improve the results obtained by the ITIF method, modelling a deterministic, linear, time-invariant system through a rational function in the z-domain, when the input of the system is a white-noise signal. This is achieved by using the maximum entropy method (burg algorithm) during the deconvolution stage instead of the covariance method.

关键词： white-noise signal z-domain ITIF method ARMA models burg algorithm deconvolution stage deterministic linear time-invariant system modelling Signal processing and detection spectral analysis Information theory maximum entropy spectral analysis signal processing parameter estimation maximum entropy method filtering and prediction theory

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Toeplitz Hermitian Positive Definite Matrix Machine Learning Based on Fisher Metric 4th

Toeplitz Hermitian Positive Definite Matrix Machine Learning...

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4th International SEE Conference on Geometric Science of Information (GSI)

作者： Cabanes, Yann Barbaresco, Frederic Arnaudon, Marc Bigot, Jeremie Adv Radar Concepts Thales Surface Radar Limours France Inst Math Bordeaux Bordeaux France

ISBN: (纸本)9783030269807;9783030269791

Here we propose a method to classify radar clutter from radar data using an unsupervised classification algorithm. The data will be represented by Positive Definite Hermitian Toeplitz matrices and clustered using the Fisher metric. Once the clustering algorithm dispose of a large radar database, new radars will be able to use the experience of other radars, which will improve their performances: learning radar clutter can be used to fix some false alarm rate created by strong echoes coming from hail, rain, waves, mountains, cities;it will also improve the detectability of slow moving targets, like drones, which can be hidden in the clutter, flying close to the landform.

关键词： Radar clutter Machine learning Unsupervised classification k-means Autocorrelation matrix burg algorithm Reflection coefficients Kahler metric

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Detecting Moving Targets in Ground Clutter Using RBF Neural Network

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5th International Symposium on Neural Networks

作者： Lao, Jian Ning, Bo Zhang, Xinchun Zhao, Jianye Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

ISBN: (纸本)9783540877332

In this paper, a new structure for moving targets detection and characteristics extraction in ground clutter is proposed. This structure combines Radial Basis Function (RBF) neural network. burg algorithm, and notch filter. After dynamical reconstruction, the RBF network is used to predict the ground clutter. Spectral characteristics of the ground clutter are estimated using the burg algorithm. We apply notch filter to cancel the interference caused by the ground clutter. Moreover, a hardware platform based on FPGA is also realized for this paper to demonstrate this proposed Structure and Sufficient details of the hardware platform are discussed. The results of simulation and hardware implementation show that the presented structure has a good performance in processing target signals mixed with the ground clutter.

关键词： Ground clutter Reconstruction RBF burg algorithm Notch filter FPGA

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Non-Supervised High Resolution Doppler Machine Learning for Pathological Radar Clutter

Non-Supervised High Resolution Doppler Machine Learning for ...

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International Radar Conference (RADAR)

作者： Cabanes, Yann Barbaresco, Frederic Arnaudon, Marc Bigot, Jeremie Thales Surface Radar Adv Radar Concepts Limours France Inst Math Bordeaux Bordeaux France

ISBN: (纸本)9781728126609

In this paper we propose a method to classify radar clutter from radar data using a non-supervised classification algorithm. As a final objective, new radars will therefore be able to use the experience of other radars to improve their performances: learning pathological radar clutter can be used to fix some false alarm rate created by strong echoes coming from hail, rain, waves, mountains, cities;it will also improve the detectability of slow moving targets, like drones, which can be hidden in the clutter, flying close to the landform.

关键词： radar clutter machine learning non-supervised classification k-means autocorrelation matrix burg algorithm reflection coefficients Kahler metric

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