3 credits
Fall 2025 Distance Learning Lecture Upper Division
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.
Learning Outcomes1Understand the autocorrelation and covariance methods of estimating the correlation matrix.
2Know the Discrete Time Fourier Transform (DTFT) and its relationship to the Discrete Fourier Transform (DFT).
3Comprehend of parametric methods of spectrum estimation, including autoregressive modeling, minimum variance, linear prediction, and eigendecomposition-based methods.
4Understand nonparametric methods of spectrum estimation, including the periodogram and the correlogram.
5Understand of linear filters, including the Wiener filter, as applied stochastic to signals.
6Introduce to adaptive filters, including the method of steepest descent and the least-mean-square (LMS) algorithms.
Prerequisites
Restrictions
made by Purdue students.