This book demonstrates how many of the reader's familiar mathematical tools can be used to comprehend and apply signal-processing principles in practical settings. assuming knowledge of mathematics at the advanced undergraduate or graduate level, including proficiency with Fourier series, matrices, probability, and statistics

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

It includes information on Fourier series and transforms in one and several variables, applications to electromagnetic and acoustic propagation models, transmission and emission tomography, 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 techniques, detection, and optimal filtering; and eigenvector-based methods for array processing and statistical filtering, time-frequency analysis, and wavelets.