Digital Signal Processing I

Catalog description

Theory and algorithms for processing of deterministic and stochastic signals. Topics include discrete signals, systems, and transforms, linear filtering, fast Fourier transform, nonlinear filtering, spectrum estimation, linear prediction, adaptive filtering, and array signal processing. Typically offered Fall. Course Learning Outcomes 1. An understanding of the autocorrelation and covariance methods of estimating the correlation matrix. 2. Knowledge of the Discrete Time Fourier Transform (DTFT) and its relationship to the Discrete Fourier Transform (DFT). 3. Comprehension of parametric methods of spectrum estimation, including autoregressive modeling, minimum variance, linear prediction, and eigendecomposition-based methods. 4. An understanding of nonparametric methods of spectrum estimation, including the periodogram and the correlogram. 5. An understanding of linear filters, including the Wiener filter, as applied stochastic to signals. 6. An introduction to adaptive filters, including the method of steepest descent and the least-mean-square (LMS) algorithms. View Class Schedule