1 edition of **An adaptive lattice algorithm for spectral line estimation** found in the catalog.

An adaptive lattice algorithm for spectral line estimation

Ill Koo Park

- 92 Want to read
- 33 Currently reading

Published
**1987** .

Written in English

- Electrical and computer engineering

The Physical Object | |
---|---|

Pagination | 102 p. |

Number of Pages | 102 |

ID Numbers | |

Open Library | OL25484249M |

Adaptive Algorithm for Estimation of Two-Dimensional Autoregressive Fields from Noisy Observations AlimoradMahmoudi Electrical Engineering Department, Shahid Chamran University, Ahvaz, Iran spectral estimation [ ]. It also can be applied to fading channel estimationin communications[ ]. T. T. CAI AND M. G. LOW Lower bound on the cost of adaptation. Let the ordered modulus of con-tinuity ω(ε,F1,F2) be deﬁned as in (4) and the between-class modulus be given as in (5). Note that ω(ε,F1,F2)does not necessarily equal ω(ε,F2,F1).Itishow- ever clear that the modulus ω(ε,F1,F2) is an increasing function of er, if F1 and F2 are convex with F1 . Adaptive filters are found in a wide range of applications and come in a wide variety of configurations, each of which having distinctive properties. A particular configuration chCited by: 3.

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A multichannel characterization for autoregressive moving average (ARMA) spectrum estimation in subbands is considered in this article. The fullband ARMA spectrum estimation can be realized in two-channels as a special form of this characterization.

A complete orthogonalization of input multichannel data is accomplished using a modified form of Cited by: 6. COURSE SYLLABUS: EE - ADAPTIVE SIGNAL PROCESSING. Instructor: Dr. Edgar Satorius.

Introduction. An adaptive lattice algorithm for spectral line estimation book This class meets PM - PM every Monday evening beginning Janu and ending on Ap The final exam for this course is on An adaptive lattice algorithm for spectral line estimation book May 9, from PM. Our class room will be RTH The grader is Mr.

An indirect adaptive control algorithm for a MIMO plant is studied. It is shown that Polyak - Ruppert estimation algorithm along with the simple dead-beat control law constitutes an adaptive control strategy that achieves the highest possible rate of convergence for the quadratic criterion.

This An adaptive lattice algorithm for spectral line estimation book deals with the problem of two-dimensional autoregressive (AR) estimation from noisy observations. The Yule-Walker equations are solved using adaptive steepest descent (SD) algorithm.

Performance comparisons are made with other existing methods to demonstrate merits of the proposed by: 1. Linear prediction based adaptive algorithm for a complex sinusoidal frequency estimation Article in AEU - International Journal of Electronics and Communications 67(6)– June with.

"Adaptive Filters" by C.F.N. COWAN and P.M. GRANT. filter realization is devoted to full digital as well as to CCD realizations of adaptive FIR filters based on the LMS algorithm. The application of adaptive FIR filters to telecommunications (echo cancelling, equalization) and to further areas such as spectral estimation and adaptive array.

The adaptive lattice other can areas be useful in adaptive linear of Wiener filtering where prediction, or in transversal or FIR filters are used in an adaptive manner; spectral estimation, line tracking, for example, adaptive noise cancelling [11], equalizers [6,14], beamformers, etc.

to be Statistical convergence properties are just beginning Cited by: An adaptive lattice algorithm for spectral line estimation book Algorithms for Adaptive Capon and APES Spectral Estimation Article in IEEE Transactions on Signal Processing 58(1) - 96 February with. In passive sonar, adaptive algorithms can be used to cancel strong sinusoidal self-interferences.

In order to correctly recover low-power target signals during the early stages of processing, these adaptive algorithms must provide fast convergence and, at the same time, narrow notches at the frequencies of the sinusoids. In this respect, the gradient adaptive lattice Cited by: 3.

The second adaptive estimation algorithm resulted from considering the best adaptive estimate to be the mode of the a posteriori probability density of the state trajectory and unknown covariance matrices, conditioned on all the avail able measurements.

The primary contribution is the algorithm used to solve for the conditional mode. Abstract. A novel adaptive algorithm of IIR lattice notch filter realized by all-pass filter is presented. The time-averaged estimation of cross correlation of the present instantaneous input signal and the past output signal is used to update the step-size, leading to a considerably improved convergence rate in a low SNR situation and reduced steady-state bias and by: 2.

Spectral Estimation 8 Signal Modeling 11 Rational or Pole-Zero Models / Fractional Pole-Zero Models and Fractal Models Adaptive Filtering 16 Applications of Adaptive Filters / Features of Adaptive Filters Array Processing 25 Spatial Filtering or Beamforming / Adaptive Interference Mitigation in.

Application: Least-Mean-Square (LMS) Algorithm. Gradient-Adaptive Lattice Filtering Algorithm. Other Applications of Stochastic Gradient Descent. Summary and Discussion. Problems.

Bibliography. Chapter 6 The Least-Mean-Square (LMS) Algorithm. for the narrowband spectra. The proposed algorithm performs similarly to supervised NMF using pre-trained piano spectra but improves pitch estimation performance by 6% to 10% compared to alternative unsupervised NMF algorithms.

Index Terms—Multiple pitch estimation, adaptive represen-tation, nonnegative matrix factorization, harmonicity, spectralCited by: Algorithms for the adjustment of adaptive lattice filters according to a given root of the estimating noise-correlation matrix (CM) are considered.

A basic algorithm is synthesized from which can be derived adjustment algorithms that take into account a priori information on the CM structure. Methods for simplification of the algorithm and increasing its efficiency are by: 8. COVID Resources.

Reliable information about the coronavirus (COVID) is available from the World Health Organization (current situation, international travel).Numerous and frequently-updated resource results are available from this ’s WebJunction has pulled together information and resources to assist library staff as they consider how to handle.

to implement any given adaptive IIR-ﬁlter algorithm using lattice structures. The correspondence is organized as follows. In the next section, the direct-form EE algorithm is given following a general framework for the description of adaptation algorithms.

Later, the two-multiplier lattice structure is presented along with a new technique for. current frequency estimation algorithm [6]-[7].

But the algorithm is more sensitive to the initial parameter value, and it is difficult to balance the convergence rate and the long tracking precision. To resolve the above problems, a novel adaptive frequency estimation algorithm based on interpolation FFT and improved ANF is proposed. AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series OriRosen of the log spectral density.

For example, Wahba () used a frequentist approach for estimating g() via cubic smoothing splines. Carter and Kohn () achieved the same 3 Spectral Estimation for Nonstationary Time Series.

theory of vector linear prediction is explained in considerable detail and so is the theory of line spectral focus and its small size make the book differentfrom many excellent texts which cover the topic, including a few that are actually dedicated to linear Size: 2MB.

Abstract: Line spectral estimation is the problem of recovering the frequencies and amplitudes of a mixture of a few sinusoids from equispaced samples. However, in a variety of signal processing problems arising in imaging, radar, and localization, we do not have access directly to such equispaced by: Shareable Link.

Use the link below to share a full-text version of this article with your friends and colleagues. Learn more. estimation. There is only one parameter which is self-determined and adaptive to the image contents. Simulation results show that the proposed algorithm performs well for different types of images over a large range of noise variances.

Performance comparisons against other approaches are also provided. DATA-ADAPTIVE ESTIMATION OF TIME-VARYING SPECTRAL DENSITIES Anne van Delft and Michael Eichler Maastricht University Septem Abstract. This paper introduces a data-adaptive approach for spectral density estimation of nonstationary processes.

Estimation of time-dependent spectra com-monly proceeds by means of local kernel smoothing. Conditional Adaptive Bayesian Spectrum Analysis (CABS) Algorithm described in "Adaptive Bayesian Spectral Analysis of Nonstationary Biomedical Time Series" by Bruce, Hall, Buysse, and Krafty () - sbruce23/CABS.

Written by leading experts in industry and academia, the book covers the most important aspects of the subject, such as spectral estimation, signal modeling, adaptive filtering, and array processing.

This unique resource provides balanced coverage of implementation issues, applications, and theory, making it a smart choice for professional. Adaptive Line Enhancer, Adaptive Linear Prediction, Adaptive Implementation of Pisarenko’s Method, Gradient Adaptive Lattice Filters, Adaptive Gram-Schmidt Preprocessors, Rank-One Modiﬁcation of Covariance Matrices, RLS Adaptive Filters, Fast RLS Filters, the EM algorithm to provide a more accurate description of the difference-image statistics.

We define this estimation procedure as an adaptive semiparametric approach. On the one hand, the term “adaptive” points out the fact that the proposed method does not assume any a priori model on the data distribution; this. constraints [2], [3]. Frequency estimation and model order selection are two important topics in line spectral estimation.

Given f k’s and K, s k’s can be obtained by a simple least-squares method according to (1). This paper is mainly focused on frequency estimation but we also incorporate existing model order selection tools in our methods.

the spectral norm. In contrast, the commonly used universal thresholding estimators are shown to be suboptimal over the same parameter spaces.

Support recovery is discussed as well. The adaptive thresholding estimators are easy to implement. The numerical performance of the estimators is studied using both simulated and real Size: KB.

Any feedback from readers is welcome. This book is an updated and much enlarged edition of Optimum Signal Processing, which was published in as a republication of the second edition published by McGraw-Hill Publishing Company, New York, NY, in (ISBN ), and also published earlier by Macmillan, Inc.,New York, NY, (ISBN.

Adaptive measurements have shown more powerful capability than nonadaptive measurements in quantum phase estimation, 26,27,28 phase tracking, 29 quantum state discrimination, 30, 31 and Cited by: Stanford Libraries' official online search tool for books, media, journals, databases, government documents and more.

LMS Algorithm for Complex-Valued Signals Beamforming (Revisited) Linearly Constrained LMS Algorithm Statement of the Problem and Its Optimal Solution Update Equations Extension to the Complex-Valued Case Problems Appendix 6A: Derivation of Eq.

() 7 Transform Domain Adaptive. For example, the theory of vector linear prediction is explained in considerable detail and so is the theory of line spectral processes.

This focus and its small size make the book different from many excellent texts which cover the topic, including a few that Cited by: Rosen, Wood, and Stoffer: AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series where W is a Wiener process, or, equivalently, h ∼ N(0,τ2), whereτ2 isasmoothingparameterand() ij = min(ν i,ν j).The parameters α 0 and α 1 are the values of g(ν) and its ﬁrst deriva- tive at ν = 0, respectively.

The symmetry and periodicity of the. A Spectral Envelope Estimation Method Based on F0-Adaptive Multi-Frame Integration Analysis Tomoyasu Nakano and Masataka Goto National Institute of Advanced Industrial Science and Technology (AIST), Japan[at] Abstract This paper presents a novel method of spectral envelope esti-mation and representation.

estimation of pointwise variability bands. The new command is compatible with both Stata 7 and Stata 8, using the appropriate graphics engine under both versions. 2 Adaptive kernel density estimation and variability bands Usefulness of varying (or local) bandwidths is widely acknowledged to estimate long-tailed or.

An adapative algorithm is used to estimate a time varying signal. There are many adaptive algorithms such as Recursive Least Square (RLS) and Kalman filters, but the most commonly used is the Least Mean Square (LMS) algorithm.

It is a simple but powerful algorithm that can be implemented to take advantage of Lattice FPGA architectures. Adaptive Filters 2 Adaptive Filter Structures 3 Adaptive Line Enhancement Beamforming Simpliﬁed LMS Algorithms Normalized LMS Algorithm Least-Squares Lattice RLSL Algorithm Notations and Preliminaries.

channel scanners) sensor at a pixel can be given as18 n pdf 5 E lL lH f k~l!d~l!r~l!l s~l!dl 1 pdf k 5 E lL lH m k~l!r~l!dl 1 n k, (1) where f k(l) is the spectral transmittance of the kth color ﬁlter, d(l) is the spectral sensitivity of the detector in the measurement, r(l) is the spectral reﬂectance of the object being scanned, l s(l) is the spectral radiance of the scan- ner illuminant, and n.b.

Linear mean squared estimation and linear prediction c. Backward Prediction and FIR Lattice Filters d. Levinson Durbin and Levinson Algorithm. 2. Non-Stationary Processes a. Kalman Filtering (pdf of book Optimal Filtering by Anderson and Moore is available on line) b.

Adaptive Filters 1. LMS Algorithm 2.The book also includes several results ebook appear in print for the first time. FEATURES/BENEFITS. Takes a geometric point of view.

Emphasis on the numerically favored array forms of many algorithms. Emphasis on equivalence and duality concepts for the solution of several related problems in adaptive filtering, estimation, and control/5(6).