1 Introduction
Terahertz time-domain spectroscopy technology is a new type of non-destructive testing technology that has emerged in recent years. It has shown great potential in many fields, such as biomedicine, non-destructive testing, safety testing, etc. However, to make better use of terahertz spectroscopic data, we need accurate parameter estimates for it. Among them, the maximum likelihood parameter estimation method is a widely used statistical method suitable for such estimation.
In this article, we will delve into how to use MATLAB for maximum likelihood parameter estimation of terahertz time-domain spectra and provide detailed MATLAB programming examples. We will try our best to use simple language and intuitive programming practices to help readers understand the entire process.
2. Introduction to terahertz time domain spectroscopy
Terahertz waves are electromagnetic waves between microwaves and infrared waves, with a frequency range of approximately 0.1 THz to 10 THz. Terahertz waves are widely used in various fields due to their strong penetrability and good sensitivity to non-polar molecules.
In terahertz time-domain spectroscopy measurements, an important task is to extract information about the physical and chemical properties of materials from the collected data. To achieve this goal, we need efficient parameter estimation of spectral data.
3. Introduction to maximum likelihood parameter estimation
Maximum Likelihood Estimation (MLE) is a parameter estimation method. The basic idea is: given a set of observation data, find a set of parameters that make this set of data most likely to appear under this set of parameters.
Using MATLAB for maximum likelihood estimation, we can easily implement numerical optimization and obtain estimated parameter values.
4. Maximum likelihood parameter estimation practice in MATLAB
First, we need to generate or obtain a set of terahertz time-domain spectrum data. To simplify the discussion, we only consider a simple simulated data set here.