SIGNAL ANALYSIS USING RAISED COSINE EMPIRICAL MODE DECOMPOSITION
Open Access
- Author:
- Roy, Arnab
- Graduate Program:
- Electrical Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- July 05, 2011
- Committee Members:
- Prof John F Doherty, Dissertation Advisor/Co-Advisor
John F Doherty, Committee Chair/Co-Chair
John David Mathews, Committee Member
Ram Mohan Narayanan, Committee Member
Karl Martin Reichard, Committee Member - Keywords:
- Hilbert spectrum
time-frequency analysis
signal overlay
raised cosine interpolation - Abstract:
- The inherent nonstationarity of signals in nature imparts their usefulness. This suggests the use of time-frequency methods to study these signals. The empirical mode decomposition (EMD) and the Hilbert-Huang transform (HHT) provide an adaptive and efficient method to analyze such signals. The EMD technique, being based on the local characteristic time scale of the signal, also works as a time-frequency filter to isolate nonstationary signal components. The rapidly growing list of applications points to its capability. This dissertation’s approach towards the EMD technique revolves around enhancing its performance while simultaneously leveraging its unique capabilities in practical applications. The original contributions of this dissertation are two-fold: firstly, a new signal-analysis technique based on EMD is developed. This new technique, called raised cosine empirical mode decomposition (RCEMD), possesses several desirable qualities: enhanced frequency resolution, computational efficiency and lower sampling rate requirement. A theoretical framework is developed to compare the performances of the original and proposed techniques. A pre-emphasis and de-emphasis based technique to improve the frequency resolution of the EMD family of algorithms is also developed. The second substantial contribution of this dissertation concerns novel applications of signal analysis techniques including RCEMD. An overlay communication technique that utilizes the unique instantaneous frequency based signal decomposition property of RCEMD is developed. A modification of this technique that is suitable for interference rejection in broadband communications is also described. Finally, two applications of signal analysis techniques concerning atmospheric remote sensing are explored. First, an RCEMD-based technique to isolate both persistent and sporadic signal features in atmospheric pressure measurements is developed. Secondly, a genetic algorithm method to resolve and estimate the parameters of fragmenting meteoroids observed using radar measurements is presented.