Application of F-test method on model order selection and related problems

Download

index.pdf

Date

2015

Author

Yazar, Alper

Metadata

Show full item record

Item Usage Stats

396
views

221
downloads

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 signal processing related problems based on known F-test are contributions of this thesis work. This work is focused on signals in linear spaces. Fundamentally, F-test is a test of significance. It is used to test whether a signal model is sufficient to model the signal of interest or higher order models are needed. This test is made by using two nested models with different orders. RSS (Residual Sum of Squares) values are calculated for each model and they are compared using F-test. According to the test result, it is determined that whether the lower order model is almost good as the higher order model or the higher order model improves the accuracy significantly. The proposed method is basically an iterative application of F-test. It selects the suitable model order by applying F-test many times. In this work, some problems related with model order selection topic are solved using F-test based approaches. An analysis window length selection method for zero-crossing point estimation problem using line fit is proposed as the first example. Secondly, a method is proposed for the segmentation of multi tone signals. Similar approach is given as the third example for segmentation of FM signals. As the fourth example, a number of pole selection algorithm is proposed for all-pole signal modeling using Prony's method. Lastly, a segmentation method for damped sinusoidal signals with Prony's method is proposed. Simulation results are provided for each five problems.

Subject Keywords

Signal processing., Parameter estimation., Signal theory (Telecommunication).

URI

http://etd.lib.metu.edu.tr/upload/12619253/index.pdf
https://hdl.handle.net/11511/24893

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

A Comparison of sparse signal recovery and approximate bayesian inference methods for sparse channel estimation Uçar, Ayla; Candan, Çağatay; Department of Electrical and Electronics Engineering (2015) The concept of sparse representation is one of the central methodologies of modern signal processing and it has had significant impact on numerous application fields such as communications and imaging. Sparsity expresses the idea that the information rate of a continuous time signal may be much smaller than suggested by its bandwidth, or that a discrete time signal depends on a number of degrees of freedom which is comparably much smaller than its (finite) length. With recent advances in sparse signal estim...
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.
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...
Indoor localization and tracking based on RSSI and accelerometer measurements Doğan, Melih; Yılmaz, Ali Özgür; Orguner, Umut; Department of Electrical and Electronics Engineering (2015) In this study, first, received signal strength (RSS) based indoor localization and tracking techniques including maximum likelihood estimation (MLE), Kalman Filter (KF), serial and parallel extended Kalman Filter (EKF) are investigated and their performances compared to each other via a simulation study. Later, sensor fusion with RSS and inertial measurement unit (IMU) for target tracking is discussed to improve accuracy of RSS-based tracking by using KF and EKF as fusion algorithms. Effects of channel para...
Blind channel estimation in OFDM systems Ayas, Mehmet Akif; Diker Yücel, Melek; Department of Electrical and Electronics Engineering (2015) In this thesis, we have studied blind channel estimation methods for single-input-multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) systems in time and frequency domain, in which the cross relation between the channel gains and a single snapshot of the received signal on each subcarrier is utilized. We have performed blind channel estimation for uncorrelated and correlated Rayleigh fading channel pairs using time and frequency methods in OFDM systems with one-transmitting, two-receivi...

Citation Formats

A. Yazar, “Application of F-test method on model order selection and related problems,” M.S. - Master of Science, Middle East Technical University, 2015.