Search Results (385)

Accurate absolute radiometric calibration is critical for ensuring the data quality of spaceborne Synthetic Aperture Radar (SAR) systems and supporting quantitative remote sensing applications. Absolute radiometric calibration generally relies on ground reference targets with known radar cross-section (RCS) deployed at dedicated calibration sites. Such ground-based calibration methods are costly and time-consuming, and calibration frequency is constrained by the distribution of calibration sites and the satellite revisit cycles. Additionally, for specialized SAR missions, such as deep space exploration, deploying calibration equipment on the observed extraterrestrial surface is infeasible. This study proposes a space-based absolute calibration concept using a small calibration satellite carrying a well-characterized reference (e.g., a passive reflector or an active transponder) and flying in formation with the SAR satellite. The relative motion ensures a side-looking acquisition geometry, enabling the SAR to image the accompanying target and derive calibration factors. The overall calibration process is divided into two stages: determination of an in-orbit calibration factor using the calibration satellite, followed by its transformation to accommodate ground imaging conditions. This method effectively isolates the radar system gain to characterize the intrinsic hardware response. Furthermore, by operating entirely in space, it avoids atmospheric and ground-clutter distortions, ensuring a fully space-based, end-to-end calibration process dominated primarily by sensor systematic errors. Moreover, it allows for more frequent and flexible calibration, eliminating reliance on ground calibration sites and infrastructure. The feasibility and advantages of the proposed concept are demonstrated through comprehensive simulations, covering orbit analysis, echo simulation, and image processing. Full article

Indoor speech propagation causes minute vibrations in surrounding objects, enabling remote speech recovery through passive eavesdropping. Unlike traditional methods that rely on acoustic waves, passive eavesdropping uses object vibrations, making it difficult to defend against, even in soundproof environments. However, weak vibration signals and noise interference make speech recovery challenging. Existing studies mainly focus on deep learning for signal reconstruction, requiring large datasets and high computational power, which complicates real-time, on-device deployment. To address this, we propose a lightweight passive speech recovery system based on millimeter-wave radar. Without prior knowledge of object locations or numbers, the system can adaptively fuse multi-source signals for real-time speech reconstruction. To counteract the noise characteristics of millimeter-wave radar and the weak amplitude of vibration signals, we designed a set of low-complexity noise suppression and signal enhancement algorithms, ensuring efficient operation on edge devices. Experimental results demonstrate that in single-target scenarios, the proposed system achieved a Mel Cepstral Distortion (MCD) of 3.923 and a Word Error Rate (WER) of 12.9%. In multi-target scenarios, the SNR improved by 3.65 dB, MCD decreased by an average of 1.52, and WER decreased by an average of 15.83%, making the method effective and practical in complex acoustic environments. Full article

Long-term stability of dam infrastructure is crucial for flood protection, water resource management, and drinking water supply. In many regions, the increasing impact of climate change and structural aging necessitates advanced monitoring approaches for embankment and gravity dams. PSI has emerged as a valuable technique for detecting surface deformation rates with millimeter precision. This study presents a comprehensive monitoring concept that combines satellite-based PSI analyses with the first operational use of ECRs at dam sites in North Rhine-Westphalia (NRW), Germany. Over a period of more than two years, ECRs were observed under real-world conditions using Sentinel-1 data. Compared to traditional passive reflectors, ECRs offer improved signal stability and a compact design, making them particularly suitable for confined or sensitive dam environments. The analysis of displacement time series confirms the suitability of ECRs for long-term deformation monitoring in complex dam settings. Intercomparison of two PSI time series demonstrated high internal consistency (correlation > 0.9, RMSE < 1 mm), while validation against in situ measurements confirmed millimeter-level agreement with RMSE values between 2 and 5 mm and correlations up to 0.7. In addition, a dedicated web-based platform was developed to provide processed ECR-based PSI results to dam operators, offering interactive visualizations, time-series access, and standardized downloads. This integration of advanced interferometric synthetic aperture radar (InSAR) methods, innovative hardware, and user-oriented service delivery marks a significant step toward operational dam monitoring using satellite remote sensing. Full article

To address the issues of information leakage risks faced by the base station (BS) when communicating with multiple users in an integrated sensing and communication (ISAC) system, as well as the blockage of the direct link between the BS and the target to be detected, which limits sensing functionality, this paper introduces the active intelligent reflecting surface (IRS) into the ISAC system. By creating a virtual line-of-sight (LoS) path, signal blockage is effectively mitigated, while the active IRS enhances the incident signal strength and adjusts the reflection phase shifts, thereby improving the reliability and security of communication. This paper proposes a joint optimization scheme for the active IRS-assisted ISAC system, which jointly designs the BS beamforming and the IRS reflection coefficient matrix. A non-convex optimization problem is formulated with the objective of maximizing the radar output signal-to-noise ratio (SNR) subject to communication performance constraints. To solve this problem, this paper employs an iterative algorithm based on alternating optimization (AO), fractional programming (FP), and semidefinite relaxation (SDR). Simulation results demonstrate that the proposed scheme significantly outperforms the benchmark schemes without IRS assistance and with passive IRS assistance in terms of enhancing the sensing performance of the ISAC system. Full article

The rapid integration of Unmanned Aerial Vehicles (UAVs) into civilian airspace has introduced complex security challenges, particularly regarding the protection of critical infrastructure and personal privacy. While conventional detection mechanisms such as radar and optical sensors are widely deployed, they are frequently limited by line-of-sight obstructions and the small radar cross-section of modern commercial drones. Acoustic analysis presents a viable passive alternative; however, accurate three-dimensional localization remains a computationally demanding task, further complicated by the use of directional sensors with non-uniform sensitivity patterns. In this paper, a deep learning framework is proposed to address these ambiguities. The method involves the fusion of raw acoustic data with explicit sensor geometry metadata within a neural network architecture. To enhance localization precision, a composite loss function is introduced, which independently optimizes planar and altitude coordinates while penalizing outlier predictions. Experimental validation was conducted using a custom dataset of real-world drone flights, utilizing a distributed array of directional microphones. The results demonstrate that the proposed system effectively mitigates the spatial irregularities of ad hoc sensor deployment, achieving robust localization performance in complex acoustic environments. Full article

In communication-centric integrated sensing and communication (ISAC) systems, passive radars exploit existing communication signals of opportunity for sensing. To compute delay-Doppler or range–velocity maps (DDMs and RVMs, respectively), modern orthogonal frequency division multiplexing (OFDM)-based sensing systems use the channel frequency response (CFR) originally estimated in communication receivers for equalization. In OFDM-based passive radars utilizing 4G LTE or 5G NR waveforms, CFR estimation typically relies only on reference signals. However, simulation-based studies that assume a priori knowledge of user data symbols indicate potential performance gains when incorporating user data and other downlink channels. In this work, we present an experimental evaluation of an OFDM-based passive radar that jointly utilizes all commonly present components of the 5G NR downlink waveform: synchronization signals (PSS and SSS), broadcast and control channels (PBCHs and PDCCHs, respectively), data channels (PDSCHs), and reference signals (PBCH DM-RSs, PDCCH DM-RSs, PDSCH DM-RSs, and CSI-RSs). Our results show that utilizing user data from fully occupied 5G downlink signals, under the assumption of full knowledge of PDSCH locations, significantly improves both the probability of detection (POD) and the peak height, measured by the peak-to-noise-floor ratio (PNFR), compared with pilot-only sensing. Since perfect knowledge of the user data payload is not assumed, we estimate the transmission bit error rate (BER) and analyze its impact on sensing performance. Finally, we investigate more realistic scenarios in which only a subset of PDSCH resource element locations is known, as in practical 5G deployments, and evaluate how partial data location knowledge affects the POD and PNFR under different BER conditions. Full article

Breast cancer remains the most frequently diagnosed cancer in women worldwide, with outcomes strongly dependent on stage at detection. Conventional imaging modalities such as mammography, ultrasound and MRI are limited by reduced sensitivity in dense breasts, radiation exposure, high cost and restricted availability in low-resource settings. This review critically examines microwave imaging (MWI) as a non-invasive, radiation-free and an emerging resource-efficient breast imaging modality that exploits dielectric contrast between healthy and malignant breast tissues. We first summarise experimental and clinical evidence on breast dielectric properties and their implications for numerical phantoms and device design. We then review passive, active (tomographic and radar-based) and hybrid MWI systems, including key clinical prototypes such as SAFE, MammoWave, MARIA and Wavelia, and analyse associated image-reconstruction algorithms from classical inverse scattering to advanced beamforming, Huygens-based methods and AI based reconstruction. Finally, we discuss outstanding challenges—tissue heterogeneity, calibration, hardware constraints and computational complexity—and identify future directions including AI-assisted reconstruction, multimodal hybrid imaging and large-scale clinical validation needed to translate MWI into routine breast cancer screening and diagnosis. Full article

Limited computational resources prevent GNSS-based passive radar systems from processing all accessible signals, necessitating intelligent signal selection for efficient target tracking. This paper proposes an adaptive selection method based on Rényi divergence. Within the Cardinality Balanced Multi-Bernoulli (CBMeMBer) filter framework, the method establishes an optimization model that maximizes the expected information gain under a fixed signal-number constraint. To comprehensively validate performance, simulations are conducted under three scenarios: multi-target linear motion, single-target tracking (for comparison with the classical Geometric Dilution of Precision (GDOP) criterion), and multi-target nonlinear maneuvering. Results demonstrate that the proposed algorithm significantly reduces computational load while achieving tracking accuracy superior to random selection and comparable to using all satellites. Compared to the GDOP-based method, it exhibits improved steady-state tracking accuracy by leveraging its dynamic, information-driven selection mechanism. This work provides an effective solution for intelligent resource management in resource-constrained GNSS-based passive radar systems. Full article

Background: Integrating Radio Frequency Identification (RFID) technology into storage areas within the wiring harness manufacturing industry enables real-time component traceability and supports the implementation of fully automated inventory processes. While RFID systems provide continuous data regarding component type, quantity, and location, periodic inventory validation is still required to verify and correct records in the warehouse management system. Methods: This study examines the feasibility of passive ultra-high-frequency (UHF) RFID technology for automatic inventory management in a components warehouse. It also reviews relevant scientific literature on autonomous RFID signal measurement and Synthetic Aperture Radar (SAR)-based localization methods, which are subsequently adapted for inventory applications. An experimental setup is developed to characterize the reading field, hysteresis effects, and the influence of distance and tag orientation on detection performance. Results: The findings indicate that RFID-based automatic inventory is achievable with high accuracy and stability, especially when tag trajectories correspond to areas of high detection probability and antenna polarization is optimally configured. Conclusions: The proposed RFID-based system can be implemented with minimal hardware changes and low investment, thereby improving stock accuracy, traceability, and operational efficiency in automotive component logistics. Full article

This work presents an experimental evaluation of a high-speed analog-to-digital conversion method based on passive reference waveform crossings combined with time-to-digital converter (TDC) time-tagging. Unlike conventional level-crossing event-driven analog-to-digital converters (ADCs) that require dynamically updated digital-to-analog converters (DACs), the proposed architecture compares the input waveform against a broadband periodic sampling function without active threshold control. Crossing instants are detected by a high-speed comparator and converted into rising and falling edge timestamps using a multi-channel TDC. A commercial ScioSense GPX2-based time-tagger with 30 ps single-shot precision was used for validation. A range of test signals—including 5 MHz sine, sawtooth, damped sine, and frequency-modulated chirp waveforms—were acquired using triangular, sinusoidal, and sawtooth sampling functions. Stroboscopic sampling was demonstrated using reference frequencies lower than the signal of interest, enabling effective undersampling of periodic radio frequency (RF) waveforms. The method achieved effective bandwidths approaching 100 MHz, with amplitude reconstruction errors of 0.05–0.30 RMS for sinusoidal signals and 0.15–0.40 RMS for sawtooth signals. Timing jitter showed strong dependence on the relative slope between the acquired waveform and sampling function: steep regions produced jitter near 5 ns, while shallow regions exhibited jitter up to 20 ns. The study has several limitations, including the bandwidth and dead-time constraints of the commercial TDC, the finite slew rate and noise of the comparator front-end, and the limited frequency range of the generated sampling functions. These factors influence the achievable timing precision and reconstruction accuracy, especially in low-gradient signal regions. Overall, the passive waveform-crossing method demonstrates strong potential for wideband, sparse, and rapidly varying signals, with natural scalability to multi-channel systems. Potential application domains include RF acquisition, ultra-wideband (UWB) radar, integrated sensing and communication (ISAC) systems, high-speed instrumentation, and wideband timed antenna arrays. Full article

Microwave, millimeter-wave, and terahertz devices are fundamental to modern 5G/6G communications, automotive imaging radar, and sensing systems. As essential RF front-end elements, passive microwave array components on static platforms remain constrained by fixed geometry and single-frequency excitation, leading to limited spatial resolution and weak interference suppression. Phase-steered arrays offer angular control but lack range-dependent response, preventing true two-dimensional focusing. Frequency-Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) architectures introduce element-wise frequency offsets to enrich spatial–spectral degrees of freedom, yet conventional linear or predetermined nonlinear offsets cause range–angle coupling, periodic lobes, and restricted beamforming flexibility. Existing optimization strategies also tend to target single objectives and insufficiently address target- or scene-induced perturbations. This work proposes a nonlinear frequency-offset design for passive microwave arrays using a Dingo–Gray Wolf hybrid intelligent optimizer. A multi-metric fitness function simultaneously enforces sidelobe suppression, null shaping, and frequency-offset smoothness. Simulations in static scenarios show that the method achieves high-resolution two-dimensional focusing, enhanced interference suppression, and stable performance under realistic spatial–spectral mismatches. The results demonstrate an effective approach for improving the controllability and robustness of passive microwave array components on static platforms. Full article

Accurate extrinsic calibration between radar and camera sensors is essential for reliable multi-modal perception in robotics and autonomous navigation. Traditional calibration methods often rely on artificial targets such as checkerboards or corner reflectors, which can be impractical in dynamic or large-scale environments. This study presents a fully targetless calibration framework that estimates the rigid spatial transformation between radar and camera coordinate frames by aligning their observed trajectories of a moving object. The proposed method integrates You Only Look Once version 5 (YOLOv5)-based 3D object localization for the camera stream with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Sample Consensus (RANSAC) filtering for sparse and noisy radar measurements. A passive temporal synchronization technique, based on Root Mean Square Error (RMSE) minimization, corrects timestamp offsets without requiring hardware triggers. Rigid transformation parameters are computed using Kabsch and Umeyama algorithms, ensuring robust alignment even under millimeter-wave (mmWave) radar sparsity and measurement bias. The framework is experimentally validated in an indoor OptiTrack-equipped laboratory using a Skydio 2 drone as the dynamic target. Results demonstrate sub-degree rotational accuracy and decimeter-level translational error (approximately 0.12–0.27 m depending on the metric), with successful generalization to unseen motion trajectories. The findings highlight the method’s applicability for real-world autonomous systems requiring practical, markerless multi-sensor calibration. Full article

The spatial resolution of current microwave remote sensing soil moisture (SM) data is about 25 km in global scale. The coarse scale hinders the application of SM product at regional scale. The global 9 km SM can be released by radar observations of Soil moisture Active and Passive (SMAP) satellite since 2015. For the failed radar sensor, SMAP 9 km SM is less than three months. Therefore, European Space Agency Climate Change Initiative (CCI) SM data is downscaled to 9 km using spatial temporal fusion model in the study. And the 43-year 9 km SM is downscaled by CCI data from 1978 to 2020. Results display that downscaled 9 km SM gets more detailed spatial information than CCI data. Moreover, temporal variation of CCI data in anomaly can be well captured by downscaled data. The evaluations against in-situ data indicate that temporal accuracies of downscaled data (r = 0.676, μbRMSE = 0.069 m³/m³) are comparable with CCI data (r = 0.670, μbRMSE = 0.070 m³/m³). Overall, downscaled data improves the spatial resolution of CCI data and inherits the temporal accuracy with slight improvement. Higher spatial resolution SM offers greater application potential. Additionally, the model herein enriches SM downscaling techniques. Full article

Phased antenna arrays have revolutionized modern wireless systems by enabling dynamic beamforming, multibeam synthesis, and user tracking to enhance data rates and reduce interferences, yet their reliance on expensive active components (e.g., phase shifters, amplifiers) embedded in antenna array elements limits adoption in cost-sensitive sub-GHz applications. Therefore, the active phased antenna arrays are still considered as high-end technology and primarily designed only for high-frequency bands and demanding applications such as radars and mobile base stations in microwave bands. In contrast, various important radio communication services still operate in sub-GHz bands with no adequate solution for modern antenna systems with beamforming capability. This paper introduces a 3D antenna array with switched-beam or multibeam capability, designed to eliminate mechanical rotators and active circuitry while maintaining all-sky coverage. By integrating collinear radiating elements with a Butler matrix feed network, the proposed 3D array achieves transmit/receive multibeam operation in the 435 MHz amateur satellite band and adjacent 433 MHz ISM band. Simulations demonstrate a design that provides selectable eight beams, enabling horizontal 360° coverage with only one radio connected to the Butler matrix. If eight noncoherent radios are used simultaneously, the proposed antenna array acts as a multibeam all-sky coverage antenna. Innovations in our design include a 3D circular collinear topology combining the broad and adjustable elevation coverage of collinear antennas with azimuthal beam steering, a passive Butler matrix enabling bidirectional transmit/receive multibeam operation, and scalability across sub-GHz bands where collinear antennas dominate (e.g., Lora WAN, trunked radio). Results show sufficient gain, confirming feasibility for low-earth-orbit satellite tracking or long-range IoT backhaul, and maintenance-free beamforming solutions in sub-GHz bands. Given the absence of practical beamforming or multibeam-capable solutions in this frequency band, our novel concept—featuring non-coherent cooperation across multiple ground stations and/or beams—has the potential to fundamentally transform how the growing number of CubeSats in low Earth orbit can be efficiently supported from the ground segment perspective. Full article

Time-division duplex (TDD) transceivers have found broad utility in millimeter-wave 5G communication, radar and imaging applications. The co-design of the switch and transmit/receive (T/R) amplifiers becomes essential in optimizing the passive loss and chip size. This work presents a Ka-band T/R amplifier with an embedded switch topology. The amplification cores from the TX and RX channels reuse the matching network to the T/R common port, and the full combination of switching and matching structures is enabled within a compact two-winding transformer. Implemented in 40 nm CMOS technology, the proof-of-concept Ka-band T/R amplifier occupies a core chip area of 0.163 mm². Experimental results show that it achieves a peak gain of 17.2 dB with a −3 dB bandwidth of 22.6–30.2 GHz in TX mode and a peak of 17.1 dB with a −3 dB bandwidth of 23.4–31.0 GHz in RX mode. The compact size and wideband gain response make the proposed T/R amplifier suitable for Ka-band TDD applications. Full article