This paper investigates on the detection and localization of ionospheric irregularities using GNSS Radio Occultation (GNSS-RO). We propose a new segmented phase screen (PS) approach to improve vertical and horizontal localization and remove the presence of outliers. The study focused on the May 2024 geomagnetic solar storm is presented, consisting of a comparison of the GNSS-RO back propagation (BP) irregularity positioning against the data of NASA’s Globalscale Observations of the Limb and Disk (GOLD) mission. This study is performed for validation purposes and examines the presence of equatorial plasma bubbles (EPBs) at predicted locations. Experimental RO data from EUMETSAT’s MetOp satellites is used to demonstrate the method’s capability to characterize the distribution of ionospheric irregularities. Results validate the segmented approach's capabilities of detecting irregularity structures and identifying their centroids with improved performance compared with the previous version of the algorithm. 

Change detection methods are, usually, restricted to SAR image pairs or stacks from identical flight geometries. This selection is mainly explained by the fact that the specular reflection of each object on the ground, such as vehicles, power lines, and buildings, depends on the incident angle and, therefore the heading angle of the platform in a pass. Thus, non-identical acquisitions can significantly increase the number of false alarms for change detection methods. This paper assesses non-identical flight passes on change detection based on tensor robust principle component analysis (TRPCA). We have considered the wavelength-resolution SAR images from the CARABAS-II data set for this assessment. These SAR images are well known for their stability and for being unaffected by speckle noise, thanks to the system resolution in the order of the radar signal wavelength. The experiments in four cases provided in the paper, two cases using images with identical passes and two cases using non-identical passes, have shown that TRPCA can perform very well change detection even in the scenarios where a surveillance image was acquired with a flight pass different from the ones used for the reference images. In addition, the results have shown that the most significant contribution of false alarms comes from elongated structures that can be sensitive to a flight pass but insensitive to others. Such elongated structures can be removed through post-processing techniques. 

The short-range applications of Terahertz (THz) synthetic aperture radar (SAR) can be found in material characterization, hyper-accuracy localization at sub-mm or even better level, scattering analysis of rough surfaces. THz SAR can also be used for material defect detection and it is an active research area. Recent experiments have shown that defects inside material can be observed by a synthetic aperture radar (SAR) operating at THz frequencies. In this paper, the experimental results of material defect detection under various circumstances are presented, for example, defects are inside a standalone material, at the back-end surface, the material with defects is conterminous to different materials. The materials considered in the experiments include electrical insulator, semiconductor, and perfect electric conductor. The electrical insulator is damaged, causing defects in the order of millimeter and submillimeter. An SAR testbed based on a vector network analyzer operating in the frequency range 220-330 GHz is used to measure the electrical insulator with damages in the form of SAR with a two-dimensional (2D) aperture. The experimental results show that damage inside the electrical insulator can be observed under various circumstances. This supports material defect detection, which enhances the monitoring of industrial production processes and the control of product quality. 

The use of THz frequencies has enabled the opportunity to perform synthetic-aperture radar (SAR) imaging at the sub-mm level. It is of great interest for applications where high-resolution remote sensing in short range is required. However, with the increase of the operating frequencies from microwave to THz, the SAR image formation algorithms work with a larger amount of data and become more sensitive to phase errors that can be caused by insufficient signal sampling rate or physical factors that cause platform deviations. This motivates the use of fast image formation capable to handle phase errors. In this paper, we present the experimental results on the performance of the local backprojection (LBP) algorithm for processing THz SAR signals. The LBP algorithm has been tested with the real data in the frequency range 0.325-0.5 THz. The results demonstrate the efficiency of the LBP algorithm for the SAR scene reconstruction and highlight the necessity of the use of two times higher signal upsampling to achieve reconstruction accuracy similar to the global backprojection algorithm. 

The paper presents a changed method based on likelihood ratio test in combination with Bayes' theory developed for foliage penetration (FOPEN) ultra-wideband (UWB) lowfrequency SAR systems. The method is tested on new experimental data collected by the CARABAS II/LORA-VHF system in 2021 in dense forests. The detectability is 87.5% of the nine targets deployed and the false alarm rate is 0.44 false per square kilometer. The probabilities of the detected targets are between 95% and 100%. Traditionally, for SAR change detection, we can calculate a likelihood ratio, giving the range of values of a ratio of two probabilities from 0 to infinity. In this study, we transform the likelihood ratio test to a conditional probability based on Bayes' theorem. With such a transform, the change detection results will be represented by the detected changes, and the detected changes will be associated probabilities that these changes are true. This representation is different from the conventional change detection result representation, i.e., a receiver operating characteristic (ROC) curve showing the relationship between the average detection probability and the false alarm rate. 

Automotive functions such as advanced driver assistance (ADAS) and autonomous driving (AD) lead to the requirement of deploying radars in vehicles. These automotive radars interfere with each other when vehicles are in traffic, called mutual interference. Interference causes a decrease in target detection probability and an increase in false alarm rate simultaneously. This can make automotive functions dependent on automotive radars unreliable. Mitigating interference is therefore important for efficient deployment of automotive radars. In this paper, we introduce a novel method for interference mitigation based on principal component analysis (PCA) using the Alternating Least Square (ALS) algorithm. The method is tested successfully with a set of signals obtained from the radar measurement in the server interference environment. 

The SAR change detection processing sequence includes SAR image stack formation (reference and surveillance), detection, and classification. The currently used approaches for classification in FOPEN SAR change detection are based on morphological operations such as erosion and dilation. In this paper, logistic regression, a supervised machine learning algorithm, is proposed for classification in FOPEN SAR change detection. The proposal is tested on the experimental data collected by the CARABAS system in 2002 over a dense forest. The test results indicate that logistic regression enhances the FOPEN SAR change detection performance. Specifically, the detection probability is 95% and the false alarm rate is 0.51 per square kilometer. 

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. 

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.

The emerge of Terahertz (THz) radar systems allows the short-range applications such as material characterization, hyper accuracy localization at cm or even better level, scattering analysis of rough surfaces, and material defect detection. The paper presents the experimental results about material defect detection based on synthetic aperture radar (SAR) operating at THz frequencies. For the experiments, an electrical insulator is damaged causing the defects in the orders of mm and sub-mm. A SAR testbed at THz frequencies built with a vector network analyzer and a frequency extender in the frequency range 325-500 GHz is used to measure the electrical insulator in the form of SAR with a two-dimensional (2D) aperture. The damaged parts inside the electrical insulator can be observed clearly in the three-dimensional (3D) SAR image. This supports the material defect detection that enhances monitoring industrial production processes and controlling product quality.