Signal Processing: A Mathematical Approach

This book is designed to show how many of the mathematical tools the reader knows can be used to understand and employ signal processing techniques in an applied environment. Assuming an advanced undergraduate or graduate-level understanding of mathematics-including familiarity with Fourier series, matrices, probability, and statistics.

Contains new chapters on convolution and the vector DFT, plane-wave propagation, and the BLUE and Kalman filtersExpands the material on Fourier analysis to three new chapters to provide additional background informationPresents real-world examples of applications that demonstrate how mathematics is used in remote sensing

It covers topics such as Fourier series and transforms in one and several variables; applications to acoustic and electro-magnetic propagation models, transmission and emission tomography, and image reconstruction; sampling and the limited data problem; matrix methods, singular value decomposition, and data compression; optimization techniques in signal and image reconstruction from projections; autocorrelations and power spectra; high-resolution methods; detection and optimal filtering; and eigenvector-based methods for array processing and statistical filtering, time-frequency analysis, and wavelets.