Digital Signal Processing System Design- E133
- This four-day course is intended for engineers and scientists concerned with the design and performance analysis of signal processing applications. The course will provide the fundamentals required to develop optimum signal processing flows based upon processor throughput resource requirements analysis. Emphasis will be placed upon practical approaches based on lessons learned that are thoroughly developed using procedures with computer tools that show each step required in the design and analysis. MATLAB code will be used to demonstrate concepts and show actual tools available for performing the design and analysis.
What you will learn:
- What are the key DSP concepts and how do they relate to real applications?
- How is the optimum real-time signal processing flow determined?
- What are the methods of time domain and frequency domain implementation?
- How is an optimum DSP system designed?
- What are typical characteristics of real DSP multirate systems?
- How can you use MATLAB to analyze and design DSP systems?From this course you will obtain the knowledge and ability to perform basic DSP systems engineering calculations, identify tradeoffs, interact meaningfully with colleagues, evaluate systems, and understand the literature. Students will receive a suite of MATLAB m-files for direct use or modification by the user. These codes are useful to both MATLAB users and users of other programming languages as working examples of practical signal processing algorithm implementations.
- Discrete Time Linear Systems. A review of the fundamentals of sampling, discrete time signals, and sequences. Develop fundamental representation of discrete linear time-invariant system output as the convolution of the input signal with the system impulse response or in the frequency domain as the product of the input frequency response and the system frequency response. Define general difference equation representations, and frequency response of the system. Show a typical detection system for detecting discrete frequency components in noise.
- System Realizations & Analysis. Demonstrate the use of z-transforms and inverse z-transforms in the analysis of discrete time systems. Show examples of the use of z-transform domain to represent difference equations and manipulate DSP realizations. Present network diagrams for direct form, cascade, and parallel implementations.
- Digital Filters. Develop the fundamentals of digital filter design techniques for Infinite Impulse Response (IIR) and Develop Finite Impulse Response filter (FIR) types. MATLAB design examples will be presented. Comparisons between FIR and IIR filters will be presented.
- Discrete Fourier Transforms (DFT). The fundamental properties of the DFT will be presented: linearity, circular shift, frequency response, scallo ping loss, and effective noise bandwidth. The use of weighting and redundancy processing to obtain desired performance improvements will be presented. The use of MATLAB to calculate performance gains for various weighting functions and redundancies will be demonstrated.
- Fast Fourier Transform (FFT). The FFT radix 2 and radix 4 algorithms will be developed. The use of FFTs to perform filtering in the frequency domain will be developed using the overlap-save and overlap-add techniques. Performance calculations will be demonstrated using MATLAB. Processing throughput requirements for implementing the FFT will be presented.
- Multirate Digital Signal Processing. Multirate processing fundamentals of decimation and interpolation will be developed. Methods for optimizing processing throughput requirements via multirate designs will be developed. Multirate techniques in filter banks and spectrum analyzers and synthesizers will be developed. Structures and Network theory for multirate digital systems will be discussed.
- Detection of Signals In Noise. Develop Receiver Operating Charactieristic (ROC) data for detection of narrowband signals in noise. Discuss linear system responses to discrete random processes. Discuss power spectrum estimation. Use realistic SONAR problem. MATLAB to calculate performance of detection system.
- Finite Arithmetic Error Analysis. Analog-to-Digital conversion errors will be studied. Quantization effects of finite arithmetic for common digital signal processing algorithms including digital filters and FFTs will be presented. Methods of calculating the noise at the digital system output due to arithmetic effects will be developed.
- System Design. Digital Processing system design techniques will be developed. Methodologies for signal analysis, system design including algorithm selection, architecture selection, configuration analysis, and performance analysis will be developed. Typical state-of-the-art COTS signal processing devices will be discussed.
This course is not on the current schedule of open enrollment courses. If you are interested in attending this or another course as open enrollment, please contact us at (410) 956-8805 or at firstname.lastname@example.org indicate the course name and number of students who wish to participate. ATI typically schedules open enrollment courses with a lead time of 3-5 months. Group courses can be presented at your facility at any time. For on-site pricing, request an on-site quote. You may also call us at (410) 956-8805 or email us at email@example.com.
Dr Joseph G. Lucas
has over 35 years of experience in DSP techniques and applications including EW, sonar and radar applications, performance analysis, digital filtering, spectral analysis, beamforming, detection and tracking techniques, finite word length effects, and adaptive processing. He has industry experience at IBM and DSR with radar, sonar and EW applications and has taught classes in DSP theory and applications. He is author of the textbook:
Digital Signal Processing: A System Design Approach
Contact this instructor (please mention course name in the subject line)