Factor Graph Based LMMSE Filtering for Colored Gaussian Processes

Download

index.pdf

Date

2014-10-01

Author

Sen, Pinar
Yılmaz, Ali Özgür

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

213
views

68
downloads

We propose a reduced complexity, graph based linear minimum mean square error (LMMSE) filter in which the non-white statistics of a random noise process are taken into account. Our method corresponds to block LMMSE filtering, and has the advantage of complexity linearly increasing with the block length and the ease of incorporating the a priori information of the input signals whenever possible. The proposed method can be used with any random process with a known autocorrelation function by use of an approximation to an autoregressive (AR) process. We show through extensive simulations that our method performs identical with the optimal block LMMSE filtering for Gaussian input signals.

Subject Keywords

Signal Processing, Electrical and Electronic Engineering, Applied Mathematics

URI

https://hdl.handle.net/11511/40283

Journal

IEEE SIGNAL PROCESSING LETTERS

DOI

https://doi.org/10.1109/lsp.2014.2330630

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

Properly Handling Complex Differentiation in Optimization and Approximation Problems Candan, Çağatay (Institute of Electrical and Electronics Engineers (IEEE), 2019-03-01) Functions of complex variables arise frequently in the formulation of signal processing problems. The basic calculus rules on differentiation and integration for functions of complex variables resemble, but are not identical to, the rules of their real variable counterparts. On the contrary, the standard calculus rules on differentiation, integration, series expansion, and so on are the special cases of the complex analysis with the restriction of the complex variable to the real line. The goal of this lect...
Analysis and Further Improvement of Fine Resolution Frequency Estimation Method From Three DFT Samples Candan, Çağatay (Institute of Electrical and Electronics Engineers (IEEE), 2013-09-01) The bias and mean square error (MSE) analysis of the frequency estimator suggested in [1] is given and an improved version of the estimator, with the removal of estimator bias, is suggested. The signal-to-noise ratio (SNR) threshold above which the bias removal is effective is also determined.
Nonlinear oscillation of second-order dynamic equations on time scales Anderson, Douglas R.; Zafer, Ağacık (Elsevier BV, 2009-10-01) Interval oscillation criteria are established for a second-order nonlinear dynamic equation on time scales by utilizing a generalized Riccati technique and the Young inequality. The theory can be applied to second-order dynamic equations regardless of the choice of delta or nabla derivatives.
Maximum likelihood estimation of transition probabilities of jump Markov linear systems Orguner, Umut (Institute of Electrical and Electronics Engineers (IEEE), 2008-10-01) This paper describes an online maximum likelihood estimator for the transition probabilities associated with a jump Markov linear system (JMLS). The maximum likelihood estimator is derived using the reference probability method, which exploits an hypothetical probability measure to find recursions for complex expectations. Expectation maximization (EM) procedure is utilized for maximizing the likelihood function. In order to avoid the exponential increase in the number of statistics of the optimal EM algori...
On higher order approximations for hermite-gaussian functions and discrete fractional Fourier transforms Candan, Çağatay (Institute of Electrical and Electronics Engineers (IEEE), 2007-10-01) Discrete equivalents of Hermite-Gaussian functions play a critical role in the definition of a discrete fractional Fourier transform. The discrete equivalents are typically calculated through the eigendecomposition of a commutator matrix. In this letter, we first characterize the space of DFT-commuting matrices and then construct matrices approximating the Hermite-Gaussian generating differential equation and use the matrices to accurately generate the discrete equivalents of Hermite-Gaussians.

Citation Formats

P. Sen and A. Ö. Yılmaz, “Factor Graph Based LMMSE Filtering for Colored Gaussian Processes,” IEEE SIGNAL PROCESSING LETTERS, pp. 1206–1210, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40283.