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Recursive parameter estimation algorithms

Coelho, J.P.; Cunha, José Boaventura; Oliveira, Paulo
Fonte: Instituto Politécnico de Bragança Publicador: Instituto Politécnico de Bragança
Tipo: Conferência ou Objeto de Conferência
ENG
Relevância na Pesquisa
36.47%
Main adaptive control design approaches assume that a suitable dynamic model of the controlled process can be computed. In this way, recursive parameter estimation algorithms play can important role in tracking the time variant parameters of the process dynamic model. Thois paper describes the major algorithms used to compute the ttransfer function parameters of time varying ssssystems. The advantages and limitations of these techniques are illustrated by computing the parameters of a time varying discrete system, with known structure, under the presence of persistent and non-persistent information.

Recursive Estimation of the Stein Center of SPD Matrices & its Applications*

Salehian, Hesamoddin; Cheng, Guang; Vemuri, Baba C.; Ho, Jeffrey
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /12/2013 EN
Relevância na Pesquisa
36.3%
Symmetric positive-definite (SPD) matrices are ubiquitous in Computer Vision, Machine Learning and Medical Image Analysis. Finding the center/average of a population of such matrices is a common theme in many algorithms such as clustering, segmentation, principal geodesic analysis, etc. The center of a population of such matrices can be defined using a variety of distance/divergence measures as the minimizer of the sum of squared distances/divergences from the unknown center to the members of the population. It is well known that the computation of the Karcher mean for the space of SPD matrices which is a negatively-curved Riemannian manifold is computationally expensive. Recently, the LogDet divergence-based center was shown to be a computationally attractive alternative. However, the LogDet-based mean of more than two matrices can not be computed in closed form, which makes it computationally less attractive for large populations. In this paper we present a novel recursive estimator for center based on the Stein distance – which is the square root of the LogDet divergence – that is significantly faster than the batch mode computation of this center. The key theoretical contribution is a closed-form solution for the weighted Stein center of two SPD matrices...

Optimum-Weighted RLS Channel Estimation for Rapid Fading MIMO Channels

Koike-Akino, Toshiaki
Fonte: Institute of Electrical and Electronics Engineers Publicador: Institute of Electrical and Electronics Engineers
EN_US
Relevância na Pesquisa
36.5%
This paper investigates on an accurate channel estimation scheme for fast fading channels in multiple-input multiple-output (MIMO) mobile communications. A high-order exponential-weighted recursive least-squares (EW-RLS) method has been known as a good channel estimation scheme in rapid fading. however, there exists a drawback that we need to properly adjust the estimation order according to the channel environment. In this paper, we theoretically derive an optimum-weighted LS (OW-LS) channel estimation based on the statistical knowledge of the spatio-temporal channel correlation. Through the analysis, we reveal that the zero-th order polynomial becomes optimal when the optimum-weighting is employed. Furthermore, we propose an efficient recursive algorithm for channel tracking in oder to reduce the computational complexity. Since the proposed scheme automatically adapts the weighting coefficients to the channel condition, it has a significant advantage in mean-square error (MSE) performance compared to EW-RLS scheme.; Engineering and Applied Sciences

Direct Estimation of Structure and Motion from Multiple Frames

Heel, Joachim
Fonte: MIT - Massachusetts Institute of Technology Publicador: MIT - Massachusetts Institute of Technology
Formato: 70 p.; 5476119 bytes; 3861012 bytes; application/postscript; application/pdf
EN_US
Relevância na Pesquisa
46.18%
This paper presents a method for the estimation of scene structure and camera motion from a sequence of images. This approach is fundamentally new. No computation of optical flow or feature correspondences is required. The method processes image sequences of arbitrary length and exploits the redundancy for a significant reduction in error over time. No assumptions are made about camera motion or surface structure. Both quantities are fully recovered. Our method combines the "direct' motion vision approach with the theory of recursive estimation. Each step is illustrated and evaluated with results from real images.

Learning the dynamics of deformable objects and recursive boundary estimation using curve evolution techniques

Sun, Walter
Fonte: Massachusetts Institute of Technology Publicador: Massachusetts Institute of Technology
Tipo: Tese de Doutorado Formato: 176 p.; 13312623 bytes; 17797080 bytes; application/pdf; application/pdf
ENG
Relevância na Pesquisa
36.4%
The primary objective of this thesis is to develop robust algorithms for the incorporation of statistical information in the problem of estimating object boundaries in image data. We propose two primary algorithms, one which jointly estimates the underlying field and boundary in a static image and another which performs image segmentation across a temporal sequence. Some motivating applications come from the earth sciences and medical imaging. In particular, we examine the problems of oceanic front and sea surface temperature estimation in oceanography, soil boundary and moisture estimation in hydrology, and left ventricle boundary estimation across a cardiac cycle in medical imaging. To accomplish joint estimation in a static image, we introduce a variational technique that incorporates the spatial statistics of the underlying field to segment the boundary and estimate the field on either side of the boundary. For image segmentation across a sequence of frames, we propose a method for learning the dynamics of a deformable boundary that uses these learned dynamics to recursively estimate the boundary in each frame over time. In the recursive estimation algorithm, we extend the traditional particle filtering approach by applying sample-based methods to a complex shape space.; (cont.) We find a low-dimensional representation for this shape-shape to make the learning of the dynamics tractable and then incorporate curve evolution into the state estimates to recursively estimate the boundaries. Experimental results are obtained on cardiac magnetic resonance images...

The detection of abrupt changes using recursive identification for power system fault analysis

Ukil, A.; Zivanovic, R.
Fonte: Elsevier Science SA Publicador: Elsevier Science SA
Tipo: Artigo de Revista Científica
Publicado em //2007 EN
Relevância na Pesquisa
36.3%
This paper describes the application of the recursive parameter estimation technique used to detect the abrupt changes in the signals recorded during disturbances in the power network of South Africa. The recursive identification technique uses M parallel Kalman filters. Main focus has been to estimate the time-instants of the changes in the signal model parameters during the pre-fault condition and following the events like initiation of fault, circuit-breaker opening, auto-reclosure of the circuit-breakers and the like. After segmenting the fault signal precisely into these event-specific sections, further signal processing and analysis can be performed on these segments, leading to automated fault recognition and analysis. In the scope of this paper, we focus on the first task, that is, segmenting the fault signal into event-specific sections using the recursive identification technique.; http://www.elsevier.com/wps/find/journaldescription.cws_home/504085/description#description; Abhisek Ukil and Rastko Živanović; Copyright © 2008 Elsevier B.V.

A Recursive Park Transformation to Improve the Performance of Synchronous Reference Frame Controllers in Shunt Active Power Filters

Pigazo López, Alberto; Moreno Sáiz, Víctor M.; Estébanez Amigo, Emilio
Fonte: IEEE Publicador: IEEE
Tipo: info:eu-repo/semantics/article; acceptedVersion
ENG
Relevância na Pesquisa
46.19%
Load harmonic currents and load unbalances reduce power quality (PQ) supplied by electrical networks. Shunt active power filters (SAPFs) are a well-known solution that can be employed to enhance electrical PQ by injecting a compensation current at the point of common coupling (PCC) of the SAPF, the load, and the electrical grid. Hence, SAPF controllers must determine the instantaneous values of the compensation reference current, including nondesirable components of the load current. A family of SAPF controllers, which evaluates the compensation reference current using synchronous rotating frames (SRFs), employs a structure based on Park transformations: direct transform, low- pass filtering (LPF), and inverse transform. The cutoff frequency and the filter order of the LPF stage must be designed properly in order to obtain an accurate reference current and a fast dynamic response of these SAPF controllers. This paper proposes a recursive implementation of the direct Park transformation that avoids the filtering stage and allows accurate SRF controllers to be designed. Moreover, the proposed implementation is not dependent on PCC conditions. The proposed implementation is evaluated using a three-phase, three-wire SAPF and compared with LPF-based controllers by simulation and experiment.

Graphical identification of TAR models

Bermejo, Miguel Ángel; Peña, Daniel; Sánchez, Ismael
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /12/2009 ENG
Relevância na Pesquisa
46.23%
This paper proposes an automatic procedure to identify Threshold Autoregressive models and specify the threshold values. The proposed procedure is based on recursive estimation of arranged autoregression. The main advantage of the proposed procedure over its competitors is that the threshold values are automatically detected. The performance of the proposed procedure is evaluated using simulations and real data.

Recursive estimation o dynamic models using cook's distance,with application to wind energy orecast

Sánchez, Ismael
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /11/2002 ENG
Relevância na Pesquisa
46.23%
This article proposes an adaptive forgetting factor for the recursive estimation of time varying models.The proposed procedure is based on the Cook's distance of the new observation.It is proven that the proposed procedure encompasses the adaptive features of classic adaptive forgetting factors and,therefore,has a larger adaptability than its competitors.The proposed forgetting factor is applied to wind energy forecast,showing advantages with respect to alternative procedures.

A novel correlation adaptive receiver structure for high speed transmissions in ultra wide band systems with realistic channel estimation

Agudelo, L.B.; Cadavid, A.N.
Fonte: Universidade Icesi Publicador: Universidade Icesi
Relevância na Pesquisa
36.35%
Impulse radio ultra wide band (UWB) communications require robust receivers; typically Rake receivers are required to capture a large number of resolvable paths, (even hundred of paths), so large number of correlators are needed; otherwise, adaptive receivers use complex filters and channel estimation algorithms. Therefore, traditional Impulse Radio receivers demand non-practical implementation structures. In this paper we propose a novel correlation-adaptive receiver structure with low complexity for indoor high speed ultra wide band systems. This novel structure combines correlation characteristics from rake receivers with recursive filters from adaptive receivers. The receiver includes a low complexity recursive channel estimation filter capable of estimating hundreds of channel impulse responses, and a single filter-correlation filter used for coherent bit demodulation. Furthermore, we derive by simulations the bit error rate for high density multipath environments for several Impulse Radio modulations like TH-PPM, DS-BPSK and TH-BPSK and we compare the performance of the proposed structure with typical Rake receivers. © 2009 IEEE.

Recursive estimation of the conditional geometric median in Hilbert spaces

Cardot, Hervé; Cénac, Peggy; Zitt, Pierre-André
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 14/04/2012
Relevância na Pesquisa
36.4%
A recursive estimator of the conditional geometric median in Hilbert spaces is studied. It is based on a stochastic gradient algorithm whose aim is to minimize a weighted L1 criterion and is consequently well adapted for robust online estimation. The weights are controlled by a kernel function and an associated bandwidth. Almost sure convergence and L2 rates of convergence are proved under general conditions on the conditional distribution as well as the sequence of descent steps of the algorithm and the sequence of bandwidths. Asymptotic normality is also proved for the averaged version of the algorithm with an optimal rate of convergence. A simulation study confirms the interest of this new and fast algorithm when the sample sizes are large. Finally, the ability of these recursive algorithms to deal with very high-dimensional data is illustrated on the robust estimation of television audience profiles conditional on the total time spent watching television over a period of 24 hours.

An Online Parallel and Distributed Algorithm for Recursive Estimation of Sparse Signals

Yang, Yang; Zhang, Mengyi; Pesavento, Marius; Palomar, Daniel P.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/03/2015
Relevância na Pesquisa
46.3%
In this paper, we consider a recursive estimation problem for linear regression where the signal to be estimated admits a sparse representation and measurement samples are only sequentially available. We propose a convergent parallel estimation scheme that consists in solving a sequence of $\ell_{1}$-regularized least-square problems approximately. The proposed scheme is novel in three aspects: i) all elements of the unknown vector variable are updated in parallel at each time instance, and convergence speed is much faster than state-of-the-art schemes which update the elements sequentially; ii) both the update direction and stepsize of each element have simple closed-form expressions, so the algorithm is suitable for online (real-time) implementation; and iii) the stepsize is designed to accelerate the convergence but it does not suffer from the common trouble of parameter tuning in literature. Both centralized and distributed implementation schemes are discussed. The attractive features of the proposed algorithm are also numerically consolidated.; Comment: Part of this work has been presented at The Asilomar Conference on Signals, Systems, and Computers, Nov. 2014

Distributed and Recursive Parameter Estimation in Parametrized Linear State-Space Models

Ram, S. Sundhar; Veeravalli, V. V.; Nedic, A.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.4%
We consider a network of sensors deployed to sense a spatio-temporal field and estimate a parameter of interest. We are interested in the case where the temporal process sensed by each sensor can be modeled as a state-space process that is perturbed by random noise and parametrized by an unknown parameter. To estimate the unknown parameter from the measurements that the sensors sequentially collect, we propose a distributed and recursive estimation algorithm, which we refer to as the incremental recursive prediction error algorithm. This algorithm has the distributed property of incremental gradient algorithms and the on-line property of recursive prediction error algorithms. We study the convergence behavior of the algorithm and provide sufficient conditions for its convergence. Our convergence result is rather general and contains as special cases the known convergence results for the incremental versions of the least-mean square algorithm. Finally, we use the algorithm developed in this paper to identify the source of a gas-leak (diffusing source) in a closed warehouse and also report numerical simulations to verify convergence.

An ensemble Kushner-Stratonovich-Poisson filter for recursive estimation in nonlinear dynamical systems

Venugopal, Mamatha; Vasu, Ram Mohan; Roy, Debasish
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 08/07/2014
Relevância na Pesquisa
46.23%
Despite the numerous applications that may be expeditiously modelled by counting processes, stochastic filtering strategies involving Poisson-type observations still remain somewhat poorly developed. In this work, we propose a Monte Carlo stochastic filter for recursive estimation in the context of linear/nonlinear dynamical systems with Poisson-type measurements. A key aspect of the present development is the filter-update scheme, derived from an ensemble approximation of the time-discretized nonlinear filtering equation, modified to account for Poisson-type measurements. Specifically, the additive update through a gain-like correction term, empirically approximated from the innovation integral in the filtering equation, eliminates the problem of particle collapse encountered in many conventional particle filters. Through a few numerical demonstrations, the versatility of the proposed filter is brought forth, first with application to filtering problems with diffusive or Poisson-type measurements and then to an automatic control problem wherein the extremization of the associated cost functional is achieved simply by an appropriate redefinition of the innovation process.; Comment: 12 pages, 16 figures, submitted to a refereed journal

A new graphical tool of outliers detection in regression models based on recursive estimation

Paroissin, Christian
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 02/07/2007
Relevância na Pesquisa
46.23%
We present in this paper a new tool for outliers detection in the context of multiple regression models. This graphical tool is based on recursive estimation of the parameters. Simulations were carried out to illustrate the performance of this graphical procedure. As a conclusion, this tool is applied to real data containing outliers according to the classical available tools.

Recursive Parameter Estimation: Convergence

Sharia, Teo
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/05/2007
Relevância na Pesquisa
36.4%
We consider estimation procedures which are recursive in the sense that each successive estimator is obtained from the previous one by a simple adjustment. We propose a wide class of recursive estimation procedures for the general statistical model and study convergence.; Comment: 25 pages with 1 postscript figure

Rate of Convergence in Recursive Parameter Estimation procedures

Sharia, Teo
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/05/2007
Relevância na Pesquisa
36.4%
We consider estimation procedures which are recursive in the sense that each successive estimator is obtained from the previous one by a simple adjustment. We study rate of convergence of recursive estimation procedures for the general statistical model.; Comment: 21 pages

Telescoping Recursive Representations and Estimation of Gauss-Markov Random Fields

Vats, Divyanshu; Moura, Jose M. F.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.33%
We present \emph{telescoping} recursive representations for both continuous and discrete indexed noncausal Gauss-Markov random fields. Our recursions start at the boundary (a hypersurface in $\R^d$, $d \ge 1$) and telescope inwards. For example, for images, the telescoping representation reduce recursions from $d = 2$ to $d = 1$, i.e., to recursions on a single dimension. Under appropriate conditions, the recursions for the random field are linear stochastic differential/difference equations driven by white noise, for which we derive recursive estimation algorithms, that extend standard algorithms, like the Kalman-Bucy filter and the Rauch-Tung-Striebel smoother, to noncausal Markov random fields.; Comment: To appear in the Transactions on Information Theory

On recursive estimation for time varying autoregressive processes

Moulines, Eric; Priouret, Pierre; Roueff, François
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 02/03/2006
Relevância na Pesquisa
46.45%
This paper focuses on recursive estimation of time varying autoregressive processes in a nonparametric setting. The stability of the model is revisited and uniform results are provided when the time-varying autoregressive parameters belong to appropriate smoothness classes. An adequate normalization for the correction term used in the recursive estimation procedure allows for very mild assumptions on the innovations distributions. The rate of convergence of the pointwise estimates is shown to be minimax in $\beta$-Lipschitz classes for $0<\beta\leq1$. For $1<\beta\leq 2$, this property no longer holds. This can be seen by using an asymptotic expansion of the estimation error. A bias reduction method is then proposed for recovering the minimax rate.; Comment: Published at http://dx.doi.org/10.1214/009053605000000624 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

State space second order filter estimation

Medel,J.J; Zagaceta,M. T.
Fonte: Sociedad Mexicana de Física Publicador: Sociedad Mexicana de Física
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/06/2013 EN
Relevância na Pesquisa
46.1%
The second order stochastic filter is based on difference models with uncorrelated innovation conditions structured in state space having stationary properties through a surface with bounded drift around the mean value. This allows building recursive estimation without generality lost and basic properties over the stochastic state space surface with unknown gains viewed as a black-box scheme. The spatial region generated gave an approximation to real parametres set with a sufficient convergence rate in a probability sense. The results were applied in adaptive identification states with a high convergence rate, observed in the functional error described illustratively in simulations. This technique was developed over the smooth slide surface having advantages over other traditional filters.