Search Results (98)

This paper proposes a novel Global Navigation Satellite System (GNSS)-based remote sensing method for simulating Radar Doppler Map (RDM) features through joint electromagnetic scattering modeling and signal processing, enabling characteristic parameter extraction for both point and ship targets in multi-satellite scenarios. Simulations demonstrate that the B3I signal achieves a significantly enhanced range resolution (tens of meters) compared to the B1I signal (hundreds of meters), attributable to its wider bandwidth. Furthermore, we introduce an Unscented Particle Filter (UPF) algorithm for dynamic target tracking and state estimation. Experimental results show that four-satellite configurations outperform three-satellite setups, achieving <10 m position error for uniform motion and <18 m for maneuvering targets, with velocity errors within ±2 m/s using four satellites. The joint detection framework for multi-satellite, multi-target scenarios demonstrates an improved detection accuracy and robust localization performance. Full article

A reliable ocean surface current (OSC) estimate is difficult to retrieve from synthetic aperture radar (SAR) data due to the challenge of accurately partitioning the Doppler shifts induced by wind waves and OSC. Recent research on SAR-based OSC retrieval is typically based on the assumption that the SAR Doppler shifts caused by wind waves and OSC are linearly superimposed. However, this assumption may lead to large errors in regions where nonlinear wave–current interactions are significant. To address this issue, we developed a novel deep learning model, OSCNet, for OSC retrieval. The model leverages Sentinel-1 Interferometric Wide (IW) Level 2 Ocean products collected from July 2023 to September 2024, combined with wave data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and geostrophic currents from newly available SWOT Level 3 products. The OSCNet model is optimized by refining input ocean surface physical parameters and introducing a ResNet structure. Moreover, the Normalized Radar Cross-Section (NRCS) is incorporated to account for wave breaking and backscatter effects on Doppler shift estimates. The retrieval performance of the OSCNet model is evaluated using SWOT data. The mean absolute error (MAE) and root mean square error (RMSE) are found to be 0.15 m/s and 0.19 m/s, respectively. This result demonstrates that the OSCNet model enhances the retrieval of OSC from SAR data. Furthermore, a mesoscale eddy detected in the OSC map retrieved by OSCNet is consistent with the collocated sea surface chlorophyll-a observation, demonstrating the capability of the proposed method in capturing the variability of mesoscale eddies. Full article

A methodology utilizing low-resolution camera data is proposed to mitigate clutter effects on radar sensors in smart indoor environments. The proposed technique suppresses clutter in distance–velocity (range–Doppler) images obtained from millimeter-wave radar by estimating clutter locations using approximate spatial information derived from low-resolution camera images. Notably, the inherent blur present in low-resolution images closely corresponds to the distortion patterns induced by clutter in radar signals, making such data particularly suitable for effective sensor fusion. Experimental validation was conducted in indoor path-tracking scenarios involving a moving subject within a 10 m range. Performance was quantitatively evaluated against baseline range–Doppler maps obtained using radar data alone, without clutter mitigation. The results show that our approach improves the signal-to-noise ratio by 2 dB and increases the target detection rate by 8.6% within the critical 4–6 m range, with additional gains observed under constrained velocity conditions. Full article

The synthetic aperture passive positioning (SAPP) method has attracted the attention of researchers due to its high positioning resolution. However, there are still key technical issues regarding SAPP methods, such as residual frequency offset (RFO) coupling at Doppler frequency leading to decreased positioning accuracy, and non-periodic discontinuous signals emitted by unknown radiation sources (NRSs) causing positioning algorithm failure. Therefore, this paper proposes an ultra-short baseline SAPP method based on interferometer assistance. Firstly, conjugate multiplication is applied to the received signals of the interferometer’s dual antennas to obtain a single frequency received signal corresponding to the straight-line distance. Subsequently, the proposed step search (SS) algorithm based on cross-correlation analysis is used to obtain the receiving frequency of the single frequency signal, and the initial positioning distance is calculated using the corresponding mapping relationship based on this frequency. Finally, NRS positioning is completed in the two-dimensional coordinates of azimuth and range by combining with the signal arrival angle. The positioning results of this method are insensitive to RFO, and even if NRS emits non-periodic discontinuous signals, the proposed method can successfully locate them. In addition, the Cramer–Rao lower bound (CRLB) of the localization for this method is derived. The simulation and unmanned aerial vehicle (UAV) experimental results demonstrate the effectiveness and feasibility of this method. Full article

Background: Ultrasonography is essential for diagnosing endometrial pathologies, such as hyperplasia, polyps, and endometrial cancer. The International Endometrial Tumor Analysis (IETA) group provides guidelines for using ultrasound to assess endometrial thickness, texture, and irregularities, aiding in the diagnosis of these conditions. The aim of this study was to evaluate the utility of various endometrial morphological features, as assessed by gray-scale ultrasound, and endometrial vascular features, as assessed by power Doppler ultrasound, in differentiating benign and malignant endometrial pathologies. A secondary objective was to compare the effectiveness of these ultrasound techniques in assessing myometrial invasion. Methods: A total of 162 women, both pre- and postmenopausal, with or without abnormal vaginal bleeding were enrolled in a prospective study. All participants underwent transvaginal gray-scale and color Doppler ultrasound examinations, conducted by examiners with over 15 years of experience in gynecological ultrasonography. Endometrial morphology and vascularity characteristics were evaluated based on the IETA group criteria, which include parameters such as endometrial uniformity, echogenicity, the three-layer pattern, regularity of the endometrial–myometrial border, Doppler color score, and vascular pattern (single dominant vessel with or without branching, multiple vessels with focal or multifocal origin, scattered vessels, color splashes, and circular flow). Sonographic findings were compared with histopathological results for comprehensive assessment. Results: The mean age of the study population was 56.46 ± 10.84 years, with a range from 36 to 88 years. Approximately 53.08% of the subjects were postmenopausal. The mean endometrial thickness, as measured by transvaginal ultrasonography, was 18.02 ± 10.94 mm with a range of 5 to 64 mm (p = 0.028), and it was found to be a significant predictor of endometrial malignancy. The AUC for the ROC analysis was 0.682 (95% CI: 0.452–0.912), with a cut-off threshold of 26 mm, yielding a sensitivity of 62.5% and a specificity of 89%. Vascularization was absent in 68.4% of patients with polyps. Among the cases with submucosal myomas, 80% exhibited a circular flow pattern. Malignant lesions were identified in 22.84% of the cases. Subjective ultrasound assessment of myometrial invasion, categorized as <50% or ≥50%, corresponded in all cases with the histopathological evaluation, demonstrating the effectiveness of ultrasound in evaluating myometrial invasion in endometrial cancer. Conclusions: In this study, cystic atrophic endometrium was identified as the most prevalent cause of postmenopausal bleeding. The most significant ultrasound parameters for predicting malignancy included heterogeneous endometrial echogenicity, increased endometrial thickness, and the presence of multiple vessels with multifocal origins or scattered vascular patterns. Additionally, color Doppler blood flow mapping was demonstrated to be an effective diagnostic tool for the differential diagnosis of benign intrauterine focal lesions. Full article

Sub-Doppler laser-cooled cesium-133 atoms are utilized as quantum sensors to achieve precise mapping of magnetic fields across a region in ultra-high vacuum (UHV), with a spatial resolution of 1 cm and a sensitivity of approximately 550 pT/√Hz, enabling accurate measurements within the nanotesla [nT] range. The cold cesium-133 atoms used for magnetic field measurements in this paper are a key component of the cesium fountain frequency standard at CSIR-NPL, which contributes to both timekeeping and magnetic sensing. The results show magnetic field fluctuations within 1 nT with a spatial resolution of 1 cm. The uncertainty in these measurements is of the order of 1.24 × 10⁻¹⁶, ensuring reliable and precise spatially resolved magnetic field mapping. Full article

Advanced communication systems, particularly in the context of autonomous driving and integrated sensing and communication (ISAC), require high precision and refresh rates for environmental perception, alongside reliable data transmission. This paper presents a novel approach to enhance the ISAC performance in existing 4G and 5G systems by utilizing a two-dimensional offset in the Delay–Doppler (DD) domain, effectively leveraging the sounding reference signal (SRS) resources. This method aims to improve spectrum efficiency and sensing accuracy in vehicular networks. However, a key challenge arises from interference between multiple users after the wireless propagation of signals. To address this, we propose a deep learning-based interference mitigation solution using an UNet architecture, which operates on the Range–Doppler maps. The UNet model, with its encoder–decoder structure, efficiently filters out unwanted signals, therefore enhancing the system performance. Simulation results show that the proposed method significantly improves the accuracy of environmental sensing and resource utilization while mitigating interference, even in dense network scenarios. Our findings suggest that this DD-domain-based approach offers a promising solution to optimizing ISAC capabilities in current and future communication systems. Full article

Hand gesture recognition (HGR) is a convenient and natural form of human–computer interaction. It is suitable for various applications. Much research has already focused on wearable device-based HGR. By contrast, this paper gives an overview focused on device-free HGR. That means we evaluate HGR systems that do not require the user to wear something like a data glove or hold a device. HGR systems are explored regarding technology, hardware, and algorithms. The interconnectedness of timing and power requirements with hardware, pre-processing algorithm, classification, and technology and how they permit more or less granularity, accuracy, and number of gestures is clearly demonstrated. Sensor modalities evaluated are WIFI, vision, radar, mobile networks, and ultrasound. The pre-processing technologies stereo vision, multiple-input multiple-output (MIMO), spectrogram, phased array, range-doppler-map, range-angle-map, doppler-angle-map, and multilateration are explored. Classification approaches with and without ML are studied. Among those with ML, assessed algorithms range from simple tree structures to transformers. All applications are evaluated taking into account their level of integration. This encompasses determining whether the application presented is suitable for edge integration, their real-time capability, whether continuous learning is implemented, which robustness was achieved, whether ML is applied, and the accuracy level. Our survey aims to provide a thorough understanding of the current state of the art in device-free HGR on edge devices and in general. Finally, on the basis of present-day challenges and opportunities in this field, we outline which further research we suggest for HGR improvement. Our goal is to promote the development of efficient and accurate gesture recognition systems. Full article

Human activity recognition (HAR) using radar technology is becoming increasingly valuable for applications in areas such as smart security systems, healthcare monitoring, and interactive computing. This study investigates the integration of convolutional neural networks (CNNs) with conventional radar signal processing methods to improve the accuracy and efficiency of HAR. Three distinct, two-dimensional radar processing techniques, specifically range-fast Fourier transform (FFT)-based time-range maps, time-Doppler-based short-time Fourier transform (STFT) maps, and smoothed pseudo-Wigner–Ville distribution (SPWVD) maps, are evaluated in combination with four state-of-the-art CNN architectures: VGG-16, VGG-19, ResNet-50, and MobileNetV2. This study positions radar-generated maps as a form of visual data, bridging radar signal processing and image representation domains while ensuring privacy in sensitive applications. In total, twelve CNN and preprocessing configurations are analyzed, focusing on the trade-offs between preprocessing complexity and recognition accuracy, all of which are essential for real-time applications. Among these results, MobileNetV2, combined with STFT preprocessing, showed an ideal balance, achieving high computational efficiency and an accuracy rate of 96.30%, with a spectrogram generation time of 220 ms and an inference time of 2.57 ms per sample. The comprehensive evaluation underscores the importance of interpretable visual features for resource-constrained environments, expanding the applicability of radar-based HAR systems to domains such as augmented reality, autonomous systems, and edge computing. Full article

Gesture recognition technology based on millimeter-wave radar can recognize and classify user gestures in non-contact scenarios. To address the complexity of data processing with multi-feature inputs in neural networks and the poor recognition performance with single-feature inputs, this paper proposes a gesture recognition algorithm based on ResNet Long Short-Term Memory with an Attention Mechanism (RLA). In the aspect of signal processing in RLA, a range–Doppler map is obtained through the extraction of the range and velocity features in the original mmWave radar signal. Regarding the network architecture in RLA, the relevant features of the residual network with channel and spatial attention modules are combined to prevent some useful information from being neglected. We introduce a residual attention mechanism to enhance the network’s focus on gesture features and avoid the impact of irrelevant features on recognition accuracy. Additionally, we use a long short-term memory network to process temporal features, ensuring high recognition accuracy even with single-feature inputs. A series of experimental results show that the algorithm proposed in this paper has higher recognition performance. Full article

In this paper, the idea of a radar based on orthogonal frequency division multiplexing (OFDM) is applied to 5G NR Positioning Reference Signals (PRS). This study demonstrates how the estimation of the communication channel using the PRS can be applied for the identification of objects moving near the 5G NR receiver. In this context, this refers to a 5G NR base station capable of detecting a high-speed train (HST). The anatomy of a 5G NR frame as a sequence of OFDM symbols is presented, and different PRS configurations are described. It is shown that spectral analysis of time-varying channel impulse response weights, estimated with the help of PRS pilots, can be used for the detection of transmitted signal reflections from moving vehicles and the calculation of their time and frequency/Doppler shifts. Different PRS configurations with varying time and frequency reference signal densities are tested in simulations. The peak-to-noise-floor ratio (PNFR) of the calculated radar range–velocity maps (RVM) is used for quantitative comparison of PRS-based radar scenarios. Additionally, different echo signal strengths are simulated while also checking various observation window lengths (FFT lengths). This study proves the practicality of using PRS pilots in remote sensing; however, it shows that the most dense configurations do not provide notable improvements, while also demanding considerably more resources. Full article

A frequency domain imaging algorithm, featured as joint two-dimensional (2D) time and frequency domain resampling, used for high-resolution high-squint large-scene (HHL) spaceborne sliding spotlight synthetic aperture radar (SAR) processing is proposed in this paper. Due to the nonlinear beam rotation during HHL data acquisition, the Doppler centroid varies nonlinearly with azimuth time and traditional sub-aperture approaches and two step approach fail to remove the inertial Doppler aliasing of spaceborne sliding spotlight SAR data. In addition, curved orbit effect and long synthetic aperture time make the range histories difficult to model and introduce space-variants in both range and azimuth. In this paper, we use the azimuth deramping and 2D time-domain azimuth resampling, collectively referred to as preprocessing, to eliminate the aliasing in Doppler domain and correct the range-dependent azimuth-variants of range histories. After preprocessing, the squint sliding spotlight SAR data could be considered as equivalent broadside strip-map SAR during processing. Frequency domain focusing, mainly involves phase multiplication and resampling in 2D frequency and RD domain, is then applied to compensate for the residual space-variants and achieve the focusing of SAR data. Moreover, in order to adapt higher resolution and larger scene cases, the combination of the proposed algorithm and partitioning strategy is also discussed in this paper. Processing results of simulation data and Gaofen-3 experimental data are presented to demonstrate the feasibility of the proposed methods. Full article

An intelligent approach is proposed and investigated in this paper for the detection of ultra-low-altitude sea-skimming moving targets for airborne pulse Doppler radar. Without suppressing interferences, the proposed method uses both target and multipath information for detection based on their distinguishable image features and deep learning (DL) techniques. First, the image features of the target, multipath, and sea clutter in the real-measured range-Doppler (RD) map are analyzed, based on which the target and multipath are defined together as the generalized target. Then, based on the composite electromagnetic scattering mechanism of the target and the ocean surface, a scattering-based echo generation model is established and validated to generate sufficient data for DL network training. Finally, the RD features of the generalized target are learned by training the DL-based target detector, such as you-only-look-once version 7 (YOLOv7) and Faster R-CNN. The detection results show the high performance of the proposed method on both simulated and real-measured data without suppressing interferences (e.g., clutter, jamming, and noise). In particular, even if the target is submerged in clutter, the target can still be detected by the proposed method based on the multipath feature. Full article

Automotive millimeter-wave (MMW) synthetic aperture radar (SAR) systems can achieve high-resolution images of detection areas, providing environmental perceptions that facilitate intelligent driving. However, curved path is inevitable in complex urban road environments. Non-uniform spatial sampling, brought about by curved path, leads to cross-coupling and spatial variation deteriorates greatly, significantly impacting the imaging results. To deal with these issues, we developed an Extended Omega-K Algorithm (EOKA) for an automotive SAR with a curved path. First, an equivalent range model was constructed based on the relationship between the range history and Doppler frequency. Then, using azimuth time mapping, the echo data was reconstructed with a form similar to that of a uniform linear case. As a result, an analytical two-dimensional (2D) spectrum was easily derived without using of the method of series reversion (MSR) that could be exploited for EOKA. The results from the parking lot, open road, and obstacle experimental scenes demonstrate the performance and feasibility of an MMW SAR for environmental perception. Full article

Underwater simultaneous localization and mapping (SLAM) has significant challenges due to the complexities of underwater environments, marked by limited visibility, variable conditions, and restricted global positioning system (GPS) availability. This study provides a comprehensive analysis of sensor fusion techniques in underwater SLAM, highlighting the amalgamation of proprioceptive and exteroceptive sensors to improve UUV navigational accuracy and system resilience. Essential sensor applications, including inertial measurement units (IMUs), Doppler velocity logs (DVLs), cameras, sonar, and LiDAR (light detection and ranging), are examined for their contributions to navigation and perception. Fusion methodologies, such as Kalman filters, particle filters, and graph-based SLAM, are evaluated for their benefits, limitations, and computational demands. Additionally, innovative technologies like quantum sensors and AI-driven filtering techniques are examined for their potential to enhance SLAM precision and adaptability. Case studies demonstrate practical applications, analyzing the compromises between accuracy, computational requirements, and adaptability to environmental changes. This paper proceeds to emphasize future directions, stressing the need for advanced filtering and machine learning to address sensor drift, noise, and environmental unpredictability, hence improving autonomous underwater navigation through reliable sensor fusion. Full article