A solution for the three-dimensional (3D) hyper-accurate localization in indoor environment for mobile equipment problem can be based on radar systems. Mobile equipment with an integrated radar system is known as a joint radar-communication (JRC) system or a joint communication and sensing (JCAS) system. The paper proposes an approach for 3D hyper-accurate localization in indoor environment without modifications of cellular network infrastructure. The simulations and experiments show the feasibility of the proposal. © 2024 IEEE.

This study presents a new back-propagation (BP) method to determine the vertical location of ionospheric irregularities using GNSS Radio Occultation (GNSS-RO) signals. GNSS-RO employs signals from GNSS satellites intercepted by Low Earth Orbit (LEO) satellites to gather data about different atmospheric layers, e.g., the ionosphere, which are crucial for weather prediction and studying ionospheric dynamics. The BP method involves computing diffractive integrals along the LEO path to identify disturbances such as sporadic E-layer clouds and equatorial plasma bubbles (EPBs). By effectively unwinding diffraction and multipath effects, the method pinpoints regions with minimal amplitude disturbance, indicating the location of ionospheric irregularities along the ray path. Beside estimates along the horizontal axis, case studies demonstrate the new method's capabilities in locating and estimating the vertical extent of these irregularities, showing its potential to enhance ionospheric modelling and forecasting. Results achieved show consistency with previous publications on the topic as well as methodologies used to locate ionospheric irregularities, allowing the presented method a better picture of the ionospheric irregularity.

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 km2. Authors

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. 

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. © 2023 by the authors.

Automatic target recognition (ATR) algorithms have been successfully used for vehicle classification in synthetic aperture radar (SAR) images over the past few decades. For this application, however, the scarcity of labeled data is often a limiting factor for supervised approaches. While the advent of computer-simulated images may result in additional data for ATR, there is still a substantial gap between synthetic and measured images. In this paper, we propose the so-called adaptive target enhancer (ATE), a tool designed to automatically delimit and weight the region of an image that contains or is affected by the presence of a target. Results for the publicly released Synthetic and Measured Paired and Labeled Experiment (SAMPLE) dataset show that, by defining regions of interest and suppressing the background, we can increase the classification accuracy from 68% to 84% while only using artificially generated images for training. © 2023 IEEE.

The expansion of the synthetic aperture radar (SAR) to the emerging THz spectrum has enabled a new era of applications in the areas of automobile, security, non-destructive testing, and material characterization. Thanks to the sub-mm wavelength, extraction of material surface properties is possible and of significant interest for the THz SAR applications. The properties define the surface scattering behavior, which is relational to the applied frequency. This study focuses on surface classification. We evaluate the scattering behavior of a rough surface and a smooth surface at 1.5 THz based on a SAR processing sequence that is introduced in this paper. First, we form the 3D SAR images of the metallic objects and then evaluate the surface properties based on the variation in the energy reflected by the object's surface.

The THz frequency spectrum provides an opportunity to explore high-resolution synthetic-aperture-radar (SAR) short-range imaging that can be used for various applications. However, the performance of THz SAR imaging is sensitive to phase errors that can be caused by an insufficient amount of data samples for image formation and by path deviations that can be practically caused by SAR platform vibrations, changes in speed, changes in direction, and acceleration. To solve the former problem, an improved interpolation procedure for backprojection algorithms has been proposed. However, to make these algorithms efficient in handling the latter problem, an additional autofocusing is necessary. In this paper, we introduce an autofocusing procedure based on compressed sensing that is incorporated into the backprojection algorithm. The reconstruction is based on the following calculated parameters: windowed interpolation sinc kernel, and range distances between SAR platform and image pixels in a defined image plane. The proposed approach is tested on real data, which was acquired by the 2\pi FMCW SAR system through outdoor SAR imaging. © 2023 IEEE.

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. Author

The back propagation (BP) method consists of diffractive integrals computed over a trajectory path, projecting a signal to different planes. It unwinds the diffraction and multipath, resulting in minimum disturbance on the BP amplitude when the auxiliary plane coincides with the region causing the diffraction. The method has been previously applied in GNSS Radio Occultation (RO) measurements showing promising results in the location estimate of ionospheric irregularities but without complementary data to validate the estimation. In this study, we investigate with wave optics propagator (WOP) simulations of an equatorial C/NOFS occultation with scintillation signatures caused by an equatorial plasma bubble (EPB), which was parametrized with aid of collocated data. In addition, a few more test cases were designed to assess the BP method regarding size, intensity and placement of single and multiple irregularity regions. The results show a location estimate accuracy of 10 km (single bubble, reference case), where in multiple bubble scenarios only the strongest disturbance would be resolved properly. The minimum detectable disturbance level and the estimation accuracy depend on the receiver noise level, and in the case of several bubbles on the distance between them. The remarks of the evaluation supported the interpretation of results for two COSMIC occultations.