Generalized Gauss Distribution noise model for respiratory parameter estimation

2009 ◽  
Author(s):  
Esra Saatci ◽  
Aydin Akan
Keyword(s):  
Parameter Estimation ◽  
Noise Model ◽  
Respiratory Parameter ◽  
Gauss Distribution

Parameter Estimation of the Mechanical Behavior of the Artery Using a Nonlinear Least Square Method With a Noise Model

2008 ◽  
Author(s):  
Shahrokh Zeinali ◽  
Jongeun Choi ◽  
Seungik Baek
Keyword(s):  
Parameter Estimation ◽  
Blood Vessel ◽  
Mechanical Behavior ◽  
Constitutive Relation ◽  
Environmental Changes ◽  
Constitutive Relations ◽  
Estimation Method ◽  
Least Square ◽  
Noise Model ◽  
Bias Error

Although it is well known that blood vessels adapt and remodel in response to various biomechanical stimuli, quantifying changes in constitutive relation corresponding to environmental changes is still challenging. Especially, when the dimension of blood vessel is small, the uncertainties in experimental measurements become significant and make it difficult to precisely estimate parameters of constitutive relations for mechanical behavior of the blood vessel. Hence without considering measurement error in displacement, a conventional nonlinear least square (NLS) method results in a biased parameter estimation. In this paper, we propose a new parameter estimation method to eliminate such bias error and provide more accurate estimated parameters for a constitutive relation using a weighted nonlinear least square (WNLS) method with a noise model. We first applied the proposed technique to a set of synthesized data with computer generated white noises and compared the fitting results to those of the NLS method without the noise model. We also applied our method to experimental data sets from mechanical tests of rabbit basilar and mouse carotid arteries and studied parameter sensitivity of the constitutive model.

Parameter Estimation Based on Fractional Correlation under Alpha-Stable Distribution Noise Environment in Bistatic MIMO Radar System

2014 ◽  
Vol 989-994 ◽  
pp. 3710-3713
Author(s):  
Li Li
Keyword(s):  
Parameter Estimation ◽  
Stable Distribution ◽  
Impulsive Noise ◽  
Mimo Radar ◽  
Radar System ◽  
Noise Model ◽  
Distribution Model ◽  
Music Algorithm ◽  
Bistatic Mimo Radar ◽  
Noise Environment

This paper takes the-stable distribution as the noise model and works on the parameter estimation problem of bistatic Multiple-Input Multiple-Output (MIMO) radar system in the impulsive noise environment.This paper presents a signal model and a novel method for parameter estimation in bistatic MIMO radar system in the impulsive noise environment. Firstly, a signal array model is constructed based on the-stable distribution model. Secondly, Doppler parameters are jointly estimated by searching the optimal rotation angle to meet concentrated-energy of the FLOS-FC. Furthermore, two algorithms are presented for the estimation of DODs and DOAs, including based on FLOS-MUSIC algorithm and FLOS-ESPRIT algorithm. Simulation results are presented to verity the effectiveness of the proposed method.

scholarly journals Generalized signal-dependent noise model and parameter estimation for natural images

2015 ◽  
Vol 114 ◽  
pp. 164-170 ◽  
Author(s):  
Thanh Hai Thai ◽  
Florent Retraint ◽  
Rémi Cogranne
Keyword(s):  
Parameter Estimation ◽  
Natural Images ◽  
Noise Model

Simplified noise model parameter estimation for signal-dependent noise

2014 ◽  
Vol 96 ◽  
pp. 266-273 ◽  
Author(s):  
Bo Gyu Jeong ◽  
Byoung Chul Kim ◽  
Yong Ho Moon ◽  
Il Kyu Eom
Keyword(s):  
Parameter Estimation ◽  
Noise Model ◽  
Model Parameter ◽  
Model Parameter Estimation

scholarly journals A Nested Ensemble Filtering Approach for Parameter Estimation and Uncertainty Quantification of Traffic Noise Models

2019 ◽  
Vol 10 (1) ◽  
pp. 204
Author(s):  
Kai Huang ◽  
Yurui Fan ◽  
Liming Dai
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Uncertainty Quantification ◽  
Ensemble Kalman Filter ◽  
Traffic Noise ◽  
Noise Model ◽  
Accurate Estimation ◽  
Noise Prediction ◽  
Ensemble Filtering ◽  
Update Process

In this study, a nested ensemble filtering (NEF) approach is advanced for uncertainty parameter estimation and uncertainty quantification of a traffic noise model. As an extension of the ensemble Kalman filter (EnKF) and particle filter methods, the proposed NEF method improves upon the ensemble Kalman filter (EnKF) method by incorporating the sample importance resampling (SIR) procedures into the EnKF update process. The NEF method can avoid the overshooting problem (abnormal value (e.g., outside the predefined ranges, complex values) in parameter or state samples) existing in the EnKF update process. The proposed NEF method is applied to the traffic noise prediction on the Trans-Canada Highway in the City of Regina to demonstrate its applicability. The results indicate that: (a) when determining parameters in the traffic noise prediction model, the NEF method provides accurate estimation; (b) the model parameters can be recursively corrected with the NEF method whenever a new measurement becomes available; (c) the uncertainty in the traffic noise model (should be the noise itself) can be well reduced and quantified through the proposed NEF approach.

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