An L-band geosynchronous synthetic aperture radar (GEO SAR) is susceptible to the ionospheric scintillation induced by ionospheric irregularities due to its lower carrier frequency. In particular, the drifting characteristic of the ionospheric irregularities makes this issue more complicated because the drifting velocity is comparable with the scanning velocity of ionospheric penetration points (IPPs) of GEO SAR. In this article, a drifting ionospheric scintillation simulator (DISS) is devised for producing L-band GEO SAR raw data affected by the drifting ionospheric scintillation phase and amplitude errors. The DISS is divided into a static scintillation transfer function (STF) generator, an IPP drifting offset indexer and a GEO SAR echo generator. Each azimuth echo of a point target is modulated by the complex STF versus the drifting IPP coordinate. Finally, two simulation experiments of the point-target and extended-target scenes are conducted to validate the effectiveness of the DISS. The results indicate that the drifting ionospheric irregularities present absolutely different artifacts and more serious decorrelations in L-band GEO SAR images, compared with the traditional L-band low-Earth-orbit (LEO) SAR.

The amplitude stripes induced by the ionospheric scintillation have been frequently observed in spaceborne L-band synthetic aperture radar (SAR) images, such as ALOS PALSAR, which may impede SAR applications. From another perspective, the presence of ionospheric stripes implies a reverse guidance for ionospheric sounding. In this paper, a methodology for correcting and measuring ionospheric stripes from SAR images is proposed. Firstly, based on prior information of the stripes’ orientation, the stripe components are automatically detected and extracted using a band-rejection spectral filter. Secondly, the extracted amplitude errors are further applied to measure ionospheric scintillation parameters by fitting the power spectrum. The methodology is tested on several PALSAR acquisitions. The results indicate that the methodology can automatically corrects the stripes and has a great potential of measuring ionospheric scintillation from the SAR images with amplitude stripes.

Effective utilization of airborne synthetic-aperture (Airborne SAR) imagery often requires precise location of each image pixel. Historically, the positioning of airborne SAR imagery either relies on the use of reliable reference points to determine the relative position of the image, or requires the precise motion information of the aircraft and the characteristics of the SAR data collection system as input to determine the absolute position of the image. However, for many applications, the accuracy of traditional positioning methods is not high due to the challenge in obtaining the accurate geographic positions of reliable reference points and the inaccuracy of the recorded aircraft motion information. This study introduces an airborne SAR image planar positioning approach based on the premise that the systematic positioning error of the dual-view airborne SAR images are relatively consistent. The suggested planar positioning method applies the positioning auxiliary parameters of the initial ground-range airborne SAR image to ascertain the transformation relationship between the target’s initial geographic position and pixel position, and it then uses the equivalent equation for the position of the homologue point to assess the systematic positioning error of the SAR image and determine the geographic position of a pixel in a digital SAR image. This approach has advantages over previous techniques in that it requires no precise geographic position information of the ground reference points, and on the basis of using the RD model to accomplish coarse positioning of four corners of SAR image, it no longer needs aircraft trajectory data. Tests were conducted using two airborne SAR images actually captured, and the experimental results indicate that the proposed method can achieve high precision planar positioning of dual-view airborne SAR images. Error sources are analyzed and recommendations are given to improve image positioning accuracy in future airborne SARs.

Over-the-horizon radar (OTHR) utilizes the ionospheric refraction and reflection in high-frequency band for air-sea targets detection. However, non-stationary ionospheric dynamics induce inhomogeneous distortions in sea clutter and targets, significantly degrading detection and localization performance. To address this challenge, we present a comprehensive investigation on OTHR full-link modeling and simulation to systematically analyze the impact of various trans-ionospheric propagation effects on echo signals. In Part I, we develop a unified framework for full-link modeling of sea clutter that incorporate background ionospheric and oceanic conditions, enabling simulation and analysis of sea clutter characteristics. Firstly, we establish the models for radar cross section of sea clutter and ionospheric propagation effects. Key parameters, including the wave propagation range, actual range, group delay, phase disturbance, and propagation loss, are calculated based on the Appleton-Hartree formula and ray tracing technique. Secondly, we construct the echo signal models in the fast- and slow-time domain and derive the corresponding range-Doppler spectrum. The origin mechanism and intrinsic cause of Doppler shifting, broadening, and splitting, as well as range localization errors are theoretically analyzed. Finally, simulation experiments of three scenarios are designed to produce OTHR sea clutter data in sea and air modes, which are validated in comparison with the real data. The typical phenomena of Doppler shifting, broadening, and splitting observed in real data are reproduced, and the results indicate that the multi-mode propagation is the main obstacle to range localization and ionospheric decontamination. The full-link sea clutter model provides critical insights for the subsequent signal processing tasks including ionospheric decontamination, clutter suppression, target detection, and localization.