COURSE # RTO-425
SIGNAL PROCESSING FUNDAMENTALS and APPLICATIONS
in MODERN COMMUNICATIONS, RADAR and SENSING SYSTEMS
another look at signal processing in noise, building upon physical and intuitive approaches with minimal use of mathematical derivations
Continuous improvements in high data rate communications, and sensors sensitivity and resolution, are bound by fundamental physical constraints that this course is designed to address. The course covers the fundamental concepts and processes involved in the detection and extraction of information from radio frequency (RF) to optical and acoustic signals in the presence of noise. Such operations are essential for the processing of signals for Communications and also for sensing systems such as radar, sonar, medical ultrasonics and laser radar. As much as possible, our unique approach reduces the detailed mathematical derivations to a minimum while focusing on physical and intuitive arguments. The complete, detailed derivations of all the material presented are covered in the course text authored by the instructor of the course.
Applications and benefits:
You will benefit by enhancing your understanding of the :
- Basic signal detection concepts.
- Fundamental operations on signals such as sampling and filtering.
- Fundamental defining features of RF, optical and acoustic systems.
- How advanced signal processing operations are applied for optimal detection and extraction of information.
Who should attend:
The course presents advanced techniques used in modern defense, telecommunications and medical systems to extract maximum information in the ever present noise. The concepts and techniques presented here make this course invaluable to all involved in systems architecture, design and marketing of communications and/or major sensing systems for commercial, military and scientific purposes including systems and signal processing engineers, managers and business development personnel. Although this course has no prerequisites, a prior understanding of probability and Fourier transforms, and an engineering level knowledge of noise and random processes will be helpful.
Course Outline:
- Introduction
- Origins of signal processing as a technical discipline
- Fundamentals of receivers
- Review of Probability
- Review of important probability density functions
- Fundamental statistical concepts the central limit theorem the law of large numbers
- Review of Noise and Random Processes
- Correlation functions and power spectral densities
- Spectral densities of thermal noise (Nyquists theorem) and shot noise
- Noise figure and noise temperature
- Noise power measurements
- Continuous and Discrete-time Signals
- The sampling theorem and over-sampling
- Signal duration and bandwidth
- Filtering of continuous and discrete-time signals
- Detection of Signals in Noise
- Statistical decision theory the likelihood ratio test
- Decision criteria Bayes, maximum likelihood, Neyman Pearson
- Signal detection in Gaussian and shot noise channels
- Bit error rates
- Correlation detection, the matched filter
- Coherent and Non-Coherent Detection and Processing
- Coherent integration
- Non-coherent integration
- Improvement in signal-to-noise ratio by coherent and non-coherent integration
- Performance of coherent and non-coherent processing systems
- Parameter Estimation and Applications
- Estimation of range to a target
- Generalized parameter estimation the Cramer Rao lower bound
- Maximum likelihood estimation
- Application of parameter estimation to target tracking in sensing systems
- Waveform Analysis Range-Doppler Resolution and Ambiguity
- Sensor waveform analysis
- Radar ambiguity functions
- Generalized ambiguity functions
- Large Time-Bandwidth Waveforms
- Chirp waveform and pulse compression
- Doppler-invariant properties of chirp waveforms
- Hyperbolic frequency modulation
- Ambiguity functions of large BT waveforms
- Coded waveforms PN sequences
- Systems Considerations in Sensing Systems
- Beam-patterns and gain of arrays
- The radar and sonar equations
- The search problem
- Specification of false alarm probability
Text: Signal Processing Fundamentals and Applications for Communications and Sensing Systems by J. Minkoff, published by Artech House, 2002.
About the Instructor
John Minkoff received a B.S., M.S. and a Ph.D. in electrical engineering all from Columbia University. As a research associate at the Columbia University Electronics Research Laboratories, and later as a manager at Riverside Research Institute, Dr. Minkoff worked in the fields of electro-optical and acousto-optical systems and signal processing, radar systems, and ionospheric and auroral RF phenomenology. He also participated in the first series of ionospheric modification experiments in conjunction with the US Dept. of Commerce Institute for Telecommunications Sciences (see Special Issue of Radio Science Vol. 9, No. 11, 1974). Subsequently, as a Distinguished Member of the Technical Staff at ATT later Lucent Technologies Bell Laboratories, Dr. Minkoff worked on the analysis of intermodulation noise in nonlinear power amplifiers, as related to satellite communications systems; adaptive systems and filtering, with applications to noise and vibration control; and wireless transmission systems.
Dr. Minkoff is currently a Staff Scientist in the ITT Space Division in Clifton, NJ. He is the author of the textbooks, Signals Noise and Active Sensors (Wiley 1992) and Signal Processing Fundamentals with Applications to Communications and Sensing Systems (Artech House 2002).
Details:
Course: RTO-425 Duration: 3 Days FEE: $1,499 CEUs: 2.16
Please direct any additional inquiries regarding our courses to Zygmond Turski, Program Director, by e-mail, FAX: (636) 273-4955 or TELEPHONE: (636) 273-9608.
Call toll free 1-800-683-7267 from anywhere in the Continental U.S. or CANADA.
Last modified April 6, 2008.
