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Adaptive Radar Detection: Model-Based, Data-Driven and Hybrid Approaches

Name: Adaptive Radar Detection: Model-Based, Data-Driven and Hybrid Approaches
Brand: ARTECH HOUSE INC
SKU: 9781630819002
Price: 149.55 EUR
Availability: InStock

Автор: Angelo Coluccia

Език:

Корици: С твърди корици

Издател: ARTECH HOUSE INC

Наличност: 50% вероятност

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This book shows you how to adopt data-driven techniques for the problem of radar detection, both per...

Информация за книгата

Автор

Angelo Coluccia

Език

Английски език

Корици

Книга - С твърди корици

Издадена

2022

страници

300

EAN

9781630819002

ISBN

163081900X

Enbook ID

42524684

Издател

ARTECH HOUSE INC

Теглоt

499

Размери

156 x 234 x 18

Пълно описание

This book shows you how to adopt data-driven techniques for the problem of radar detection, both per se and in combination with model-based approaches. In particular, the focus is on space-time adaptive target detection against a background of interference consisting of clutter, possible jammers, and noise. It is a handy, concise reference for many classic (model-based) adaptive radar detection schemes as well as the most popular machine learning techniques (including deep neural networks) and helps you identify suitable data-driven approaches for radar detection and the main related issues. You’ll learn how data-driven tools relate to, and can be coupled or hybridized with, traditional adaptive detection statistics; understand fundamental concepts, schemes, and algorithms from statistical learning, classification, and neural networks domains. The book also walks you through how these concepts and schemes have been adapted for the problem of radar detection in the literature and provides you with a methodological guide for the design, illustrating different possible strategies. You’ll be equipped to develop a unified view, under which you can exploit the new possibilities of the data-driven approach even using simulated data. This book is an excellent resource for Radar professionals and industrial researchers, postgraduate students in electrical engineering and the academic community.