Synthetic aperture radar autofocus based on projection approximation subspace tracking

Autor:	Meng Shen, Zhaoda Zhu, Rui Jiang, Dalong Zhu
Rok vydání:	2012
Předmět:	Autofocus Synthetic aperture radar Radar tracker Computer science Covariance matrix business.industry law.invention law Radar imaging Computer vision Artificial intelligence Electrical and Electronic Engineering business Subspace topology Eigendecomposition of a matrix Eigenvalues and eigenvectors
Zdroj:	IET Radar, Sonar & Navigation. 6:465-471
ISSN:	1751-8792 1751-8784
DOI:	10.1049/iet-rsn.2011.0312
Popis:	An eigenvector method for maximum-likelihood estimation (MLE) of phase error has better algorithmic performance than phase gradient autofocus (PGA), which is implemented by the simultaneous processing of multiple-pulse vectors of the range-compressed data. However, this method requires eigendecomposition of the sample covariance matrix, which is a computationally expensive task and also limits the real-time application. In order to overcome such difficulty, this study proposes a novel autofocus algorithm using the projection approximation subspace tracking (PAST) approach. With this methodology, the computational cost can be reduced effectively to the level of PGA via avoiding the procedures of covariance matrix estimation and eigendecomposition. Monte Carlo tests and real synthetic aperture radar (SAR) data validate that although undergoing performance loss compare with the original multiple-pulse MLE algorithm, the new approach outperforms the mostly used PGA.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::19a31d22804d776de6c03d0dbb4fdf63 https://doi.org/10.1049/iet-rsn.2011.0312 Zobrazit plný text záznamu