In this paper, a two-dimensional (2D) autoregressive (AR) model is employed as a mitigation algorithm for interference in the Frequency Modulated Continuous Wave (FMCW) radars. The AR model, due to its simple structure, can perform super efficiently in multi-dimensional estimation problems and can be a suitable replacement for complex Neural Network (NN) based algorithms. This study addresses the advanced requirements of the 2DAR algorithm for mitigating interference in actual frames collected from real-world experiments. Three approaches to interference generation were incorporated to reproduce and study the most common and likely circumstances of mutual interference. A dynamic sampling direction selection framework is developed to address the unpredictable shapes of interfered segments within a frame. An iterative signal reconstruction algorithm is proposed to reconstruct the damaged areas using clean samples. Finally, the parallelizable processes were vectorized to make them implementable in the real world. The 2DAR mitigator’s performance was assessed using a diverse dataset of frames collected from real-world experiments, each containing unique target, noise, and interference attributes. The derived mitigator improved the signal in all experimental cases, down to the noise floor, and increased the Signal to Interference plus Noise Ratio (SINR) to almost 15 dB. Finally, the performance of different order models was compared in an identical hardware and software environment to provide a scaled indicator of the computation escalation in different model orders.

Bistatic configuration is one of the useful arrangements in radar applications. There are many benefits to bistatic geometry, one of which is the capability to intercept stealthy targets and reduce the vulnerability of the receiver side, stemming from the separation of the transmitter and receiver(s). In a network of radars, monostatic, bistatic, and even multistatic configurations can coexist, providing spatial diversity for better, more accurate detection and ranging. A key requirement for bistatic or multistatic configurations is the integrity of the transmitter and receiver signals. Specifically, for a network of FMCW radars, it is challenging to share the Local Oscillator (LO) over long distances due to the millimeter wavelength order. Additionally, other parameters can cause incoherence between independent FMCW radars, including ramp bandwidth, chirp duration, oscillator phase noise, carrier frequency, and phase. In this paper, a study of all these impacting parameters is conducted. An effective and low-cost technique to improve the coherence in the timing profiles of two radars is also proposed. 

The development of new radar systems operating at sub-THz and THz frequencies with high-resolution imaging has led to new applications and enabled remote sensing for shortrange applications, such as non-destructive testing or surfaceroughness analysis. Furthermore, these remote-sensing systems open an opportunity to study downscaled real-life scenarios that involve long-range remote sensing in a closed laboratory environment. In this paper, we introduce a method for studying the radar signatures of objects sensed in the far-field region using X band radars, employing a scaled sub-THz 2D inverse synthetic aperture radar (ISAR) system that performs simultaneous monostatic and bistatic radar imaging. The method is experimentally studied via a VNA-based radar system operating at D-band with application to a downscaled model of a 3.3 m -long unmanned aerial vehicle (UAV). The results demonstrate that the proposed method provides insight into the radar signatures of physically large targets in the laboratory environment. Furthermore, the results demonstrate that monostatic and bistatic imaging provide complementary information on the radar signature of the object under test. 

This study proposes the inflated modified Kumaraswamy (iMK) distribution, a flexible probability model defined on the unit interval [0,1]. It captures asymmetric behaviors while accommodating inflation at zero, one, or both boundaries, as commonly observed in normalized difference vegetation index (NDVI) data. Based on the iMK distribution, we develop a new regression model (iMKreg) suitable for double-bounded responses. From this model, we derive a detection tool for invasive plant species, particularly applicable to NDVI imagery. Model performance was evaluated using synthetic NDVI data, with further assessment of predictive accuracy and detection efficacy conducted on real-world measured NDVI image. The application to detecting black-grass (Alopecurus myosuroides) in wheat crops in southern Sweden shows that the iMKreg model outperforms both standard Gaussian-based linear regression and existing inflated Kumaraswamy regression models. 

A wide deployment of radars, such as a surveillance radar network and many vehicles equipped with automotive radars in traffic, will make the signal environment very challenging. If the radars are active simultaneously, one radar will interfere with other radars. In a critical case, two radars having similar parameters interfere with each other and the separation between them is double to the range to a target. In this case, the target can totally be hidden by the relatively strong interference. In this paper, an interference suppression method is introduced for this type of interference. The method is based on plane rotation in the range-time domain and interference detection and removal in the range-Doppler domain. The experiment shows the feasibility of the method. 

Urban tunneling projects pose significant geotechnical challenges, especially in densely populated regions where heterogeneous subsurface conditions increase the risk of ground displacements. Monitoring these displacements is therefore essential to ensure infrastructure safety and minimize potential impacts on surrounding communities. Traditional geotechnical monitoring methods, such as ground-based sensors, achieve sub-millimeter precision with high temporal resolution but are limited in spatial coverage and incur high operational costs. In addition, they may interrupt construction activities and disturb neighborhoods, restricting their deployment to areas directly above critical infrastructure. This limitation often results in incomplete datasets and contributes to legal disputes over alleged tunneling-induced damage. This work investigates the application of Persistent Scatterer Interferometry (PSI) as a complementary technique for settlement monitoring in the expansion of São Paulo Metro Line 2. This large-scale project is expected to benefit approximately 1.2 million people, with a public investment of R$ 13.4 billion. The construction, which began in 2021, is being excavated in Paleogene sediments of the São Paulo and Resende formations of the São Paulo Basin, as well as Quaternary alluvial deposits. Owing to the rift-related tectonic heritage that originated this basin, the local geology is highly heterogeneous, which may result in differential settlement and further reinforces the need for comprehensive monitoring strategies.

Using high-resolution X-band images (1m resolution) from the ICEYE microsatellite constellation, this study employs SARPROZ to evaluate the dataset's coherence and baseline characteristics and assesses the potential of PSI for wide-area monitoring in a dense urban environment. The preliminary results demonstrated the significant challenges inherent in processing high-resolution X-band data from emerging constellations. Specifically, the large perpendicular baselines present in the dataset increased the sensitivity to topographic phase errors and geometric decorrelation, which, combined with strong atmospheric phase screen (APS) effects, hindered the isolation of the deformation signal through conventional linear phase modeling. These findings highlight the critical role of baseline optimization and advanced APS mitigation strategies when applying PSI to microsatellite constellations in tropical urban settings. Despite these constraints, this study provides valuable insights into the feasibility of integrating satellite-based SAR data with in situ monitoring for tunneling projects, offering a pathway toward more comprehensive, reliable, and cost-effective settlement monitoring frameworks to support informed decision-making in large-scale infrastructure development.

The emergence of new radar systems operating in the terahertz frequency range facilitates new applications such as material defect detection, material characterization, hyperaccuracy localization, and scattering analysis of rough surfaces. To locate defects inside the material, the synthetic aperture radar (SAR) principle can be realized using a radar system, and localization of defects can be implemented in SAR images. Since a material typically exhibits a non-unity refractive index, neglecting this factor during SAR image formation can cause smearing and displacement of internal defects in the reconstructed image. These effects become particularly critical for materials with high refractive indices or when defects are located deep within the medium. In this paper, we present a wave propagation model inside a non-unity refractive index material, and then the model is utilized to develop a backprojection algorithm for SAR imaging inside a non-unity refractive index material. The simulation results are provided to show the effects of non-unity refractive indices on SAR images, whereas the experimental results help us to verify the proposed wave propagation model in practice. The experiments are based on an SAR testbed based on a vector network analyzer operating in the frequency range 220-330 GHz and an electrical insulator with internal damages. 

Det sker en revolution inom sensorområdet med AI, nya sensorkoncept och nya sensorer. Förstudien RAIL-SENSE har undersökt hur förutsättningarna är att sjösätta ett projekt som undersöker hur nya sensorer kan användas och utvecklas för att ge ett effektivare och säkrare järnvägsnät. I början av förstudien undersöktes förseningsdata med orsakskoder som erhållits från Trafikverket Statistikcenter. Denna analys gjordes för att sammanställa de vanligaste förseningsorsakerna i tågtrafiken. Orsakerna har i sin tur utnyttjats för att ta fram lämpliga scenario enligt arbetspaket 1 (AP1). Dessa scenarier har sedan används som underlag till mätningar i arbetspaket 2 (AP2) i förstudien. 

Under slutet av maj genomfördes mätningar vid Tortuna Testcenter (TTC), enligt AP2. För att genomföra mätningar upprättades avtal mellan Trafikverket, BTH och KTH. Mätningarna som gjordes på TTC baserades på de scenario som framkommit i AP1. Vid experimentet brukades en ny typ av radarutrustning, lidar och kameror. Experimenten utfördes på stationsområdet och vid en vägkorsning enligt föreslagna scenario. Analyser av data har visat att experimenten var framgångsrika och delar av resultaten kommer att presenteras vid vetenskapliga konferenser under 2026. Dessa analyser tillsammans med slutsatser från AP1 har resulterat i projektansökan RAIL-SENSE+.  TG0 ansökan av RAIL-SENSE+ har beviljats och en TG2 ansökan lämnades in i januari 2026. Alla mål i förstudien är nu avklarade. Vi har därtill presenterat förstudien RAIL-SENSE och projektförslaget RAIL-SENSE+ vid KAJTs höstmöte 2025.

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.