Advanced Signal Processing A Concise Guide
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Advanced Signal Processing: A Concise Guide
Author: Amir-Homayoon Najmi
language: en
Publisher: McGraw Hill Professional
Release Date: 2020-08-28
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. A comprehensive introduction to the mathematical principles and algorithms in statistical signal processing and modern neural networks. This text is an expanded version of a graduate course on advanced signal processing at the Johns Hopkins University Whiting school program for professionals with students from electrical engineering, physics, computer and data science, and mathematics backgrounds. It covers the theory underlying applications in statistical signal processing including spectral estimation, linear prediction, adaptive filters, and optimal processing of uniform spatial arrays. Unique among books on the subject, it also includes a comprehensive introduction to modern neural networks with examples in time series and image classification. Coverage includes: Mathematical structures of signal spaces and matrix factorizations linear time-invariant systems and transforms Least squares filters Random variables, estimation theory, and random processes Spectral estimation and autoregressive signal models linear prediction and adaptive filters Optimal processing of linear arrays Neural networks
Advanced Signal Processing Handbook
Advances in digital signal processing algorithms and computer technology have combined to produce real-time systems with capabilities far beyond those of just few years ago. Nonlinear, adaptive methods for signal processing have emerged to provide better array gain performance, however, they lack the robustness of conventional algorithms. The challenge remains to develop a concept that exploits the advantages of both-a scheme that integrates these methods in practical, real-time systems. The Advanced Signal Processing Handbook helps you meet that challenge. Beyond offering an outstanding introduction to the principles and applications of advanced signal processing, it develops a generic processing structure that takes advantage of the similarities that exist among radar, sonar, and medical imaging systems and integrates conventional and nonlinear processing schemes.
Introduction to Synthetic Aperture Radar: Concepts and Practice
Author: E. David Jansing
language: en
Publisher: McGraw Hill Professional
Release Date: 2021-04-23
Explore the principles and applications of synthetic aperture radar This comprehensive guide offers a solid grounding in synthetic aperture radar (SAR) fundamentals and techniques. Written by a remote sensing and signal processing expert, Introduction to Synthetic Aperture Radar: Concepts and Practice clearly explains data collection, image formation, error correction, and image quality. You will get concise descriptions of commonly used image formation algorithms, including the Range-Doppler Algorithm (RDA) and the Polar Formatting Algorithm (PFA). Continuous wave LFM systems, interferometry, polarimetry, and moving objects are discussed in detail. Coverage includes: Origins of synthetic aperture radar Ranging and imaging Image formation and image processing tools Linear frequency-modulated chirp Image formation algorithms for quadrature demodulated data Image formation algorithms for dechirped data Autofocus Image quality and speckle reduction Linear frequency-modulated continuous wave systems Remote sensing Interferometry Moving objects in SAR