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Signal Analysis Techniques: Time, Frequency, and Spatial Algorithms
| Presenter: | Dr. Larry Marple, Oregon State University, Corvallis, Oregon, USA |
| Dates: | 8–9 September, 2008 |
| Venue: | Mawson Lakes, SA |
About Signal Analysis Techniques
Conventional tools for analyzing and mining the feature content of signals are filters (time content) and Fourier transforms (frequency content). This course explores alternative tools to these conventional techniques, which can often produce signal analysis results with finer temporal detail and higher spectral resolution. It will also be shown how to extend these tools to process spatial sensor arrays with a similar increase in spatial detail (higher resolution direction finding).
The course uses a systematic approach based on a signal modelling theme, while focusing on fast computational procedures that make the alternative higher performance analysis techniques feasible to implement in practical applications. An extensive software toolbox (MATLAB and C) is provided students for all the algorithms and computational techniques introduced in the course.
Brief Course Outline
Introduction to Signal Analysis Tools and Classical Spectral Analysis- Complex Signal Representations
- Concepts from Fourier Transform Theory
- Tutorial Review: Random Signal and Matrix Algebra Concepts
- Autocorrelation and Cross Correlation
- Power Spectral Density
- Correlogram Method,
- Periodogram Method
- Blackman-Turkey Method
- Parametric Time Series Models: Autoregressive (AR), Moving Average (MA), and Autoregressive Moving Average (ARMA)
- Parameter Relationships among AR, MA, and ARMA Models
- Autocorrelation Relationships among Parametric Models
- AR, Linear Prediction, and Lattice Filters
- AR Spectral Estimation: Yule-Walker algorithm, Burg algorithm, least squares linear prediction algorithms
- Prony's Method
- Damped Exponential Parameter Estimation
- Relationship to AR Methods
- Least Squares Prony Algorithms
- Noise Excision by Eigenanalysis / Principal Components Analysis
- Minimum Variance Estimation: Derivation and Relationship to AR spectral Estimation:
- Signal and Noise Subspace Concepts
- Pisarenko's Technique
- MUSIC Algorithm
- ESPRIT Algorithm.
- Short Time Fourier Transform
- Linear and Quadratic Time-Frequency Representations
- Short Time Fourier Transform (STFT)
- Wigner-Ville Distribution
- 2-D Methods of TFA
- Scaling (time compression/time expansion) Properties of Analyzing Wavelets for Time-Frequency Analysis
- Scalograms vs Time-Frequency Grams
- Relationship of Temporal and Spatial Spectral Analysis Techniques
- High Resolution Direction Finding and Beamformation.
About the Presenter
Larry Marple received the BS and MEE degrees (Rice University, 1969,1970) and EngrD (Stanford University, 1976) in electrical engineering. He spent 33 years in defense and commercial industry working sensor signal processing for radar (ELINT and SAR), sonar (active and passive), geoseismic, ultrasonic medical imaging, and audio applications, before joining the academic staff at Oregon State University in 2002. He was elected a Fellow of the IEEE in 1989 for work in advanced spectral estimation. He has given short courses since 1977 in the area of signal analysis techniques, and has a 1989 text and two new texts for release in 2008 on the subject.
How to Register
To register for this course, please fill out the registration form and return it to NICTA Industry Education Manager (fax it to +61-8-8302-3115 or scan and email it industryeducation@nicta.com.au).
Cancellation Policy
At least four weeks notice is required for cancellation of a place in a short course for full reimbursement. If cancellation is later than 4 weeks then the place can either be given to another person or the registrant can be provided with a credit towards other NICTA training.