Analysis Window Length Selection For Linear Signal Models

Date

2015-05-19

Author

Yazar, Alper
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

45
views

0
downloads

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.

Subject Keywords

Signal Modeling, Linear Models, Parameter Estimation

URI

https://hdl.handle.net/11511/55016

Conference Name

23nd Signal Processing and Communications Applications Conference (SIU)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Application of F-test method on model order selection and related problems Yazar, Alper; Candan, Çağatay; Department of Electrical and Electronics Engineering (2015) Signal modeling is one of the important topics of signal processing area. The input signal should be modeled with a suitable mathematical model first. In statistics related disciplines, there are information theory based criteria for model order selection topic. In this thesis work, F-test based methods are proposed on model order selection and related problems. F-test is used in statistics related disciplines. However, it is not so widely used in signal processing related problems. Solution approaches for ...
Fine resolution frequency estimation from three DFT samples: Case of windowed data Candan, Çağatay (2015-09-01) An efficient and low complexity frequency estimation method based on the discrete Fourier transform (DFT) samples is described. The suggested method can operate with an arbitrary window function in the absence or presence of zero-padding. The frequency estimation performance of the suggested method is shown to follow the Cramer-Rao bound closely without any error floor due to estimator bias, even at exceptionally high signal-to-noise-ratio (SNR) values.
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...
On the Cramér-Rao lower bound under model mismatch Fritsche, Carsten; Orguner, Umut; Özkan, Emre; Gustafsson, Fredrik (2014-04-24) Cram´er-Rao lower bounds (CRLBs) are proposed for deterministic parameter estimation under model mismatch conditions where the assumed data model used in the design of the estimators differs from the true data model. The proposed CRLBs are defined for the family of estimators that may have a specified bias (gradient) with respect to the assumed model. The resulting CRLBs are calculated for a linear Gaussian measurement model and compared to the performance of the maximum likelihood estimator for the corresp...
Dimension reduced robust beamforming for towed arrays Topçu, Emre; Candan, Çağatay; Department of Electrical and Electronics Engineering (2015) Adaptive beamforming methods are used to obtain higher signal to interference plus noise ratio at the array output. However, these methods are very sensitive to steering vector and covariance matrix estimation errors. To overcome this issue, robust methods are usually employed. On the other hand, implementation of these robust methods can be computationally expensive for arrays with large number of sensors. Reduced dimension techniques aim to lower the computational load of adaptive beamforming algorithms w...

Citation Formats

A. Yazar and Ç. Candan, “Analysis Window Length Selection For Linear Signal Models,” presented at the 23nd Signal Processing and Communications Applications Conference (SIU), Malatya, Turkey, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55016.