HHT based Instrumental Glitch Trigger Generation

Project Description

emd2.png HHT is a recently suggested empirical data transform based on adaptive bases. So it is very useful for analyzing non-linear and/or non-stationary data. The HHT consists of two main parts: 1) empirical mode decomposition (EMD) 2) Hilbert transform. The EMD can decompose the original data into some appropriate data sets that contributes to some frequency bands. Basically the EMD is described by a sifting function defined as https://upload.wikimedia.org/math/e/e/4/ee4471f673afaddfc791f692cf250f6b.png, which shows that the EMD performs the subtraction between the original data (or the previously generated data) and its average of envelopes, repeatedly, called intrinsic mode functions (IMF), expressed by https://upload.wikimedia.org/math/7/e/c/7ec2bae54bcdd79fab725ef8d6c5314c.png and https://upload.wikimedia.org/math/6/5/3/65349fc2c693c1b809562b59f4b5e172.png. The Hilbert transform of the EMDed data consists of the amplitude and phase parts and we see that the original data can be expressed by the summation of the whole IMFs, which is https://upload.wikimedia.org/math/9/d/e/9de1f9c2b24c4c2b3bab29243d825bc3.png.

Goal

Preliminary Analysis of HHT

Trigger Generation Method

Simulation Results

Comparison to Klein-Welle and Other Methods