Classification of Radar Targets Using Invariant Features / Nejlevnější knihy
Classification of Radar Targets Using Invariant Features

Kód: 08244431

Classification of Radar Targets Using Invariant Features

Autor Gregory J Meyer

Automatic target recognition (ATR) using radar commonly relies on modeling a target as a collection of point scattering centers. Features extracted from these scattering centers for input to a target classifier may be constructed ... celý popis

1646


Skladem u dodavatele
Odesíláme za 14-18 dnů
Přidat mezi přání

Mohlo by se vám také líbit

Darujte tuto knihu ještě dnes
  1. Objednejte knihu a zvolte Zaslat jako dárek.
  2. Obratem obdržíte darovací poukaz na knihu, který můžete ihned předat obdarovanému.
  3. Knihu zašleme na adresu obdarovaného, o nic se nestaráte.

Více informací

Více informací o knize Classification of Radar Targets Using Invariant Features

Nákupem získáte 165 bodů

Anotace knihy

Automatic target recognition (ATR) using radar commonly relies on modeling a target as a collection of point scattering centers. Features extracted from these scattering centers for input to a target classifier may be constructed that are invariant to translation and rotation, i.e., they are independent of the position and aspect angle of the target in the radar scene. Here an iterative approach for building effective scattering center models is developed, and the shape space of these models is investigated. Experimental results are obtained for three-dimensional scattering centers compressed to nineteen-dimensional feature sets, each consisting of the singular values of the matrix of scattering center locations augmented with the singular values of its second and third order monomial expansions. These feature sets are invariant to translation and rotation and permit the comparison of targets modeled by different numbers of scattering centers. A Mahalanobis distance metric is used that effectively identifies targets under "real world" conditions that include noise and obscuration. In particular, eight targets of military interest are sampled in tenth-degree aspect angle increments to extract scattering centers, and 36 subclasses that encompass ten degrees are specified for each target. Each subclass is compressed to a nineteen-dimensional singular value feature set, and because the spatial distribution of the 100 nineteen-dimensional points in each subclass is approximately Gaussian, a mean and a covariance matrix represent each subclass.

Parametry knihy

Zařazení knihy Knihy v angličtině Society & social sciences Education

1646

Oblíbené z jiného soudku



Osobní odběr Praha, Brno a 12903 dalších

Copyright ©2008-24 nejlevnejsi-knihy.cz Všechna práva vyhrazenaSoukromíCookies


Můj účet: Přihlásit se
Všechny knihy světa na jednom místě. Navíc za skvělé ceny.

Nákupní košík ( prázdný )

Vyzvednutí v Zásilkovně
zdarma nad 1 499 Kč.

Nacházíte se: