Maximum likelihood autoregressive model parameter estimation with noise corrupted independent snapshots

Date

2021-09-01

Author

Çayır, Ömer
Candan, Çağatay

Metadata

Show full item record

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

Item Usage Stats

411
views

0
downloads

Maximum likelihood autoregressive (AR) model parameter estimation problem with independent snapshots observed under white Gaussian measurement noise is studied. In addition to the AR model parameters, the measurement noise variance is also included among the unknowns of the problem to develop a general solution covering several special cases such as the case of known noise variance, noise-free snapshots, the single snapshot operation etc. The presented solution is based on the expectation-maximization method which is formulated by assigning the noise-free snapshots as the missing data. An approximate version of the suggested method, at a significantly reduced computational load with virtually no loss of performance, has also been developed. Numerical results indicate that the suggested solution brings major performance improvements in terms of estimation accuracy and does not suffer from unstable AR filter estimates unlike some other methods in the literature. The suggested method can be especially useful for small-dimensional multiple-snapshot noisy AR modeling applications such as the clutter power spectrum modeling application in radar signal processing.

Subject Keywords

Autoregressive process, Autoregressive model parameter estimation, Multiple snapshots, Expectation-maximization, Parametric spectrum estimation, Spectrum estimation

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85105693629&origin=inward
https://hdl.handle.net/11511/90688

Journal

Signal Processing

DOI

https://doi.org/10.1016/j.sigpro.2021.108118

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

Covariance Matrix Estimation of Texture Correlated Compound-Gaussian Vectors for Adaptive Radar Detection Candan, Çağatay; Pascal, Frederic (2022-01-01) Covariance matrix estimation of compound-Gaussian vectors with texture-correlation (spatial correlation for the adaptive radar detectors) is examined. The texture parameters are treated as hidden random parameters whose statistical description is given by a Markov chain. States of the chain represent the value of texture coefficient and the transition probabilities establish the correlation in the texture sequence. An Expectation-Maximization (EM) method based covariance matrix estimation solution is given ...
Analysis Window Length Selection For Linear Signal Models Yazar, Alper; Candan, Çağatay (2015-05-19) A method is presented for the selection of analysis window length, or the number of input samples, for linear signal modeling without compromising the model assumptions. It is assumed that the signal of interest lies in a known linear space and noisy samples of the signal is provided. The goal is to use as many signal samples as possible to mitigate the effect of noise without violating the assumptions on the model. An application example is provided to illustrate the suggested method.
Noise Estimation for Hyperspectral Imagery using Spectral Unmixing and Synthesis DEMİRKESEN, CAN; Leloğlu, Uğur Murat (2014-09-25) Most hyperspectral image (HSI) processing algorithms assume a signal to noise ratio model in their formulation which makes them dependent on accurate noise estimation. Many techniques have been proposed to estimate the noise. A very comprehensive comparative study on the subject is done by Gao et al. [1]. In a nut-shell, most techniques are based on the idea of calculating standard deviation from assumed-to-be homogenous regions in the image. Some of these algorithms work on a regular grid parameterized wit...
Parameter estimation in generalized partial linear models with Tikhanov regularization Kayhan, Belgin; Karasözen, Bülent; Department of Scientific Computing (2010) Regression analysis refers to techniques for modeling and analyzing several variables in statistical learning. There are various types of regression models. In our study, we analyzed Generalized Partial Linear Models (GPLMs), which decomposes input variables into two sets, and additively combines classical linear models with nonlinear model part. By separating linear models from nonlinear ones, an inverse problem method Tikhonov regularization was applied for the nonlinear submodels separately, within the e...
Simplified MAP estimator for OFDM systems under fading Cueruek, Selva Muratoglu; Tanık, Yalçın (2007-04-25) This paper presents a simplified Maximum A Posteriori (MAP) estimator, which yields channel taps in OFDM systems under fading conditions using a parametric correlation model, assuming that the channel is frequency selective, slowly time varying and Gaussian. Expressions for the variance of estimation error are derived to evaluate the performance of the MAP estimator. The relation between the correlation of subchannels taps and error variance and the effect of Signal to Noise Ratio (SNR) are investigated. Th...

Citation Formats

Ö. Çayır and Ç. Candan, “Maximum likelihood autoregressive model parameter estimation with noise corrupted independent snapshots,” Signal Processing, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85105693629&origin=inward.