Iterative MMSE method and recurrent Kalman procedure for ISAR image reconstruction

Author

Lazarov, A.D.

Author_Institution

Military Acad. for Artillery & Air Defence

Volume

37

Issue

4

fYear

2001

fDate

10/1/2001 12:00:00 AM

Firstpage

1432

Lastpage

1441

Abstract

This work presents a novel approximate iterative and recurrent approach for image reconstruction from inverse synthetic aperture radar (ISAR) data. Mathematical models of the quadrature components of the ISAR signal, reflected by an object with a complex geometry, are devised. Approximation matrix functions are used to describe deterministic signals reflected by point scatterers located at nodes of the uniform grid (model) during inverse aperture synthesis. Minimum mean square error (MMSE) equations and Kalman equations are derived. To prove the validity and correctness of the developed iterative MMSE method and recurrent Kalman procedure, numerical experiments were performed. The computational results demonstrate high resolution images, unambiguous and convergent estimates of the point scatterers´ intensities of a target from simulated ISAR data

Keywords

Kalman filters; image restoration; iterative methods; least mean squares methods; radar computing; radar imaging; radar resolution; synthetic aperture radar; ISAR image reconstruction; approximation matrix functions; correlation algorithms; deterministic signals; high resolution images; image restoration; iterative MMSE method; mathematical models; point scatterers; quadrature components; recurrent Kalman procedure; recurrent approach; unambiguous convergent estimates; uniform grid nodes; vector measurement equation; vector state equation; Equations; Geometry; Image reconstruction; Inverse problems; Inverse synthetic aperture radar; Iterative methods; Kalman filters; Mathematical model; Radar scattering; Signal synthesis;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.976978

Filename

976978