Viet Thuy Vu

The resolution of a synthetic aperture radar (SAR) system depends not only on the operating frequency range of a radar but also on the geometry. A radar system operating at MHz frequencies facilitates SAR resolution to the meter (m) level with a linear aperture. With the same radar system, the realization of the circular aperture can further enhance the SAR resolutions to the submeter level. For GHz SAR systems, the realization of circular apertures helps to reduce the resolution from the centimeter (cm) level to the millimeter (mm) level. In this paper, a resolution equation for SAR systems synthesizing circular aperture (CSAR) is introduced. The equation is derived from the backprojection integral for CSAR, its Fourier transform, and the method of stationary phase. The accuracy of the derived equation is enhanced with a correcting factor that is numerically calculated with a cubic interpolation. Therefore, the equation can provide a more accurate, analytical, and practical estimate of the spatial resolution that can be reached with a CSAR system than the ones available in the literature. The resolution equation introduced in this paper is verified with simulations of a MHz SAR system and a GHz SAR system. The equation is further verified by an experiment with a THz inverse SAR (ISAR) system. The radar utilized in the ISAR experiment is a D-band radar system mounted on an antenna positioner that supports circular movement.

One of the main challenges in Synthetic Aperture Radar (SAR) change detection involves using SAR images from different flight passes. Depending on the flight pass, objects have different specular reflections since the radar cross-sections of these objects can be totally different between passes. Then, it is common knowledge that the flight passes must be close to identical for conventional SAR change detection. Wavelength-resolution SAR refers to a SAR system with a spatial resolution approximately equal to the wavelength. This high relative resolution helps to stabilize the ground clutter in the SAR images. Consequently, the restricted requirement about identical flight passes for SAR change detection can be relaxed, and SAR change detection becomes possible with nonidentical passes. This paper shows that robust principal component analysis (RPCA) is efficient for change detection even using wavelength-resolution SAR images acquired with very different flight passes. It presents several SAR change detection experimental results using flight pass differences up to 95°. For slightly different passes, e.g., 5°, our method reached a false alarm rate (FAR) of approximately one false alarm per square kilometer for a probability of detection (PD) above 90%. In a particular setting, it achieves a PD of 97.5% for a FAR of 0.917 false alarms per square kilometer, even using SAR images acquired with nonidentical passes.

This letter introduces a novel regression model structure for the inflated Rayleigh distribution, which effectively models high dynamic amplitude pixel values in synthetic aperture radar (SAR) images. The proposed model estimates the mean of inflated Rayleigh distribution signals by a structure that includes a set of regressors and a link function. The inflated Rayleigh distribution combines the Rayleigh and a degenerate distribution, assigning non-null probability specifically for observed values equal to zero. Null pixel values in amplitude SAR images can be randomly distributed within the image, especially in low-intensity areas; a model capable of incorporating these values is essential to avoid changes in image statistics. Extensive evaluations are conducted using simulated and real SAR images to validate the proposed model, specifically focusing on ground-type detection in high dynamic amplitude pixel values scenarios. The performance of the proposed inflated Rayleigh regression model is compared with traditional Gaussian-based regression models, excelling in terms of ground-type detection in a SAR image obtained from the ICEYE radar.

Wavelength-resolution (WR) synthetic aperture radar (SAR) change detection (CD) has been used to detect concealed targets in forestry areas. However, most proposed methods are generally based on matrix or vector analyses and, therefore, do not exploit information embedded in multidimensional data. In this letter, a CD method for WR SAR image stacks based on tensor robust principal component analysis (TRPCA) is proposed. The proposed CD method used the new tensor nuclear norm induced by the definition of the tensor-tensor product to exploit temporal and spatial information contained in the image stack. To assess the performance of the proposed method, we considered SAR images obtained by the very high frequency (VHF) WR CARABAS-II SAR system. Experiments for three different stack sizes show that a significant performance gain can be achieved when large image stacks are considered. The proposed CD method performs better in terms of probability of detection (PD) and false alarm rate (FAR) than the other five CD methods in VHF WR SAR images, including one based on matrix robust principal component analysis (RPCA). In a particular setting, it achieves a PD of 99% and a FAR of 0.028 false alarms per km ² .

The current synthetic-aperture-radar (SAR) image formation algorithms have been developed for the pulse radar systems, and they are desired to be used for frequency modulated continuous wave (FMCW) radar systems. Since there is a difference between the outputs of pulse radar and FMCW radar, it is necessary to adapt these algorithms to the output of the pulse radar. Beside this, the start-stop approximation, which can be used for signal processing of pulse radar systems, should be taken into account for FMCW radar systems due to the fact that the pulse duration of pulse radar is relatively small in comparison to the modulation time of FMCW radar. The study investigates the phase error caused by the start-stop approximation in processing the data measured by a FMCW radar system for synthetic aperture imaging. The important finding is that the start-stop approximation is valid for processing FMCW SAR data in many cases. If the following circumstances occur simultaneously, such as high radar signal frequency, long modulation time, high platform speed, and short propagation range, then the approximation may become invalid. The simulations and the experiments performed with a wideband 154 GHz FMCW radar support this statement.

This paper presents an iterative change detection (CD) method based on Bayes’ theorem for very high-frequency (VHF) ultra-wideband (UWB) SAR images considering commonly used clutter-plus-noise statistical models. The proposed detection technique uses the information of the detected changes to iteratively update the data and distribution information, obtaining more accurate clutter-plus-noise statistics resulting in false alarm reduction. The Bivariate Rayleigh and Bivariate Gaussian distributions are investigated as candidates to model the clutter-plus-noise, and the Anderson-Darling goodness-of-fit test is used to investigate three scenarios of interest. Different aspects related to the distributions are discussed, the observed mismatches are analyzed, and the impact of the distribution chosen for the proposed iterative change detection method is analyzed. Finally, the proposed iterative method performance is assessed in terms of the probability of detection and false alarm rate and compared with other competitive solutions. The experimental evaluation uses data from real measurements obtained using the CARABAS II SAR system. Results show that the proposed iterative CD algorithm performs better than the other methods.

This paper presents a statistical analysis of intensity wavelength-resolution synthetic aperture radar (SAR) difference images. In this analysis, Anderson Darling goodness-of-fit tests are performed, considering two different statistical distributions as candidates for modeling the clutter-plus-noise, i.e., the background statistics. The results show that the Gamma distribution is a good fit for the background of the tested SAR images, especially when compared with the Exponential distribution. Based on the results of this statistical analysis, a change detection application for the detection of concealed targets is presented. The adequate selection of the background distribution allows for the evaluated change detection method to achieve a better performance in terms of probability of detection and false alarm rate, even when compared with competitive performance change detection methods in the literature. For instance, in an experimental evaluation considering a data set obtained by the Coherent All Radio Band Sensing (CARABAS) II UWB SAR system, the evaluated change detection method reached a detection probability of 0.981 for a false alarm rate of 1/km².