SUBAPERTURE AUTOFOCUS FOR SYNTHETIC-APERTURE RADAR

作     者：CALLOWAY, TM DONOHOE, GW 

作者机构：UNIV NEW MEXICOALBUQUERQUENM 87131 

出 版 物：《IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS》 (IEEE Trans. Aerosp. Electron. Syst.)

年 卷 期：1994年第30卷第2期

页      面：617-621页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0825[工学-航空宇航科学与技术] 

基　　金：U.S. Department of Energy  USDOE  (DE-AC04-76DP0089) 

主　　题：error analysis error statistics parameter estimation radar interference radar theory radiofrequency interference signal detection synthetic aperture radar SAR autofocus algorithm cross-range drift degraded SAR image equal-width subapertures full-scene im Radar theory signal acquisition Parameter estimation Polynomial auto-focus Sinusoidal functions Mean square error Rescue Radiofrequency interference Specific absorption rate 

摘      要：A subaperture autofocus algorithm for synthetic aperture radar (SAR) partitions range-compressed phase-history data collected over a full aperture into equal-width subapertures. Application of a one-dimensional Fourier transform to each range bin converts each subaperture data set into a full-scene image (map). Any linear phase difference, or phase ramp, between a pair of subapertures expresses itself as cross-range drift in their maps. A traditional autofocus algorithm fits a polynomial to inferred equal-width phase ramps. If the true phase error function contains significant high-order components, then polynomial regression generates a poor estimate of the phase error function. Instead of fitting a polynomial, we fit a sinusoidal function through the inferred phase ramps. An example with a degraded SAR image shows how a sinusoidal correction improves image quality. We compare lower bounds on mean squared error (MSE) for polynomial and sinusoidal parameterizations. Sinusoidal parameterization reduces MSE significantly for model orders greater than five.