Publication Type : Conference Paper
Publisher : 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC).
Source : 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (2018)
Url : https://ieeexplore.ieee.org/document/8782387
Keywords : Acoustic signal processing, Active Set, Additive White Gaussian noise, AWGN, AWGN channels, Curve fitting, de-noised cases, echo, echo parameters, Error analysis, estimated signals, Gaussian echo, Gaussian noise, Least squares approximations, Levenberg-Marquardt algorithm, LM, Maximum likelihood estimation, Mean square error, mean square error methods, MLE, MSE, Newton method, noise added signals, noise altered echo, noise reduction, Optimization, Optimization algorithms, Parameter estimation, Quadratic programming, Quasi Newton, Sequential quadratic programming, Signal processing algorithms, Signal to noise ratio, SNR, SNR values viz, square curve fitting, Trust-Region Reflective, ultrasonic echo, ultrasonic materials testing, ultrasonic parameter Estimation, ultrasonic scattering, Wavelet denoising
Campus : Amritapuri
School : School of Engineering
Department : Computer Science, Electronics and Communication
Year : 2018
Abstract : The back scattered ultrasonic echo carries several valuable information in its amplitude, bandwidth, delay in time, phase and time of flight. This information is capable of characterizing properties of the material from which it is reflected. In order to model the reverberations, white Gaussian noise is added to Gaussian echo. The required set of parameters are estimated from the noise altered echo using Maximum Likelihood Estimation. This reduces to least square curve fitting, due to additive white gaussian noise. For optimizing the estimated parameters from the back-scattered signal, different algorithms such as Levenberg-Marquardt, Trust-Region Reflective, Active Set, Quasi Newton and Sequential quadratic programming are used. The proposed work aims at the error analysis of estimated signals using the above-mentioned optimization algorithms for different SNR values viz. 0-20dB. Further the noise added signals are subjected to wavelet denoising, prior to parameter estimation. The experimental result shows that the lowest mean square error is obtained for the echo parameters optimized using Levenberg-Marquardt algorithm, for both noisy and de-noised cases.
Cite this Research Publication : R. N. Aditya, K. Abhijeeth, S., Anuraj K., and Poorna S. S., “Error Analysis of Optimization Algorithms in Ultrasonic Parameter Estimation”, in 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2018.