Application of a Fully Automatic Autofocusing Algorithm for Post-Processing of Synthetic Aperture Radar Images based on Image Entropy Minimization

Abstract

In this paper an application of an autofocusing algorithm, previously developed by the authors for the case of Inverse Synthetic Aperture Radar (ISAR) [i.e. airborne radar targets], is presented here for the case of Synthetic Aperture Radar (SAR) geometry [i.e. ground radar targets]. Here, a new theory and methodology for generating SAR synthetic backscattered data is also developed. This algorithm is named 'CPI-split-algorithm', where CPI stands for 'Coherent Processing Interval'. Moreover, two simulation scenarios are presented for a ship target, which is located on the sea surface. In the first simulation scenario, the ship is considered at first to be stationary, and subsequently an oscillatory movement is induced to its position along the vertical axis, due to sea surface motion. In the second simulation scenario, a partial loss of data is examined, caused by temporary accidental malfunctioning of radar transmitter or receiver, assuming that the target (ship) is stationary. Numerical results presented in this paper show the effectiveness of the proposed autofocusing algorithm for SAR image enhancement.

Characterization of Rough Fractal Surfaces from Backscattered Radar Data

“…In this paper we concentrate on the backscattering of EM waves from rough fractal surfaces (e.g. monostatic SAR radar [9], [10]), i.e. θs = -θi at Fig.…”

“…and M is the number of frequencies, fo is the carrier frequency, f = B/M is the frequency step and B is the bandwidth of the radar, i.e. 'stepped -frequency' transmitted radar waveform [9], [10]. Once again, in all our simulations it is set θs = -θi, i.e.…”

“…In this paper, a novel method is presented for the characterization of rough fractal surfaces from backscattered SAR radar data of sufficient bandwidth [9], [10]. As resulted from the plots of the backscattered signal magnitude as a function of the wavenumber (frequency) of the incident EM wave, as the roughness of the fractal surface increases (i.e.…”

“…θs= -θi, from (8) it follows that in this case z  ≈ 0, and from (10) we see that our method is not applicable in this special case (i.e. radar not airborne or spaceborne, any more [9], [10]), since in this special case the scattering coefficient |γ(k)| cannot be computed. Summarizing, our proposed method for characterizing a rough fractal surface provides reliable results for appropriate values of radar bandwidth, surface 'patch size' and angle of incidence.…”

Characterization of 2D and 3D Rough Fractal Surfaces from Backscattered Radar Data

“…In this paper, a novel method is presented for the characterization of rough fractal surfaces from backscattered radar data of sufficient bandwidth [9], [10]. As resulted from the plots of the backscattered signal magnitude as a function of the wavenumber (therefore a 'spectral method') of the incident EM wave, as the roughness of the fractal surface increases, then the observed slope between the main lobe and the side lobes also increases.…”