Search Results (273)

This study addresses the nonlinear estimation problem in the strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation by proposing an improved filtering algorithm based on $S{E}_2(3)$ Lie group state representation. A dynamic model satisfying the group affine condition is established to systematically construct both left-invariant and right-invariant error state spaces, upon which two nonlinear filtering approaches are developed. Although the fast unscented transformation method is not novel by itself, its first integration with the $S{E}_2(3)$ Lie group model for SINS/DVL integrated navigation represents a significant advancement. Experimental results demonstrate that under large misalignment angles, the proposed method achieves slightly lower attitude errors compared to linear approaches, while also reducing position estimation errors during dynamic maneuvers. The 12,000 s endurance test confirms the algorithm’s stable long-term performance. Compared with conventional unscented Kalman filter methods, the proposed approach not only reduces computation time by 90% but also achieves real-time processing capability on embedded platforms through optimized sampling strategies and hierarchical state propagation mechanisms. These innovations provide an underwater navigation solution that combines theoretical rigor with engineering practicality, effectively overcoming the computational efficiency and dynamic adaptability limitations of traditional nonlinear filtering methods. Full article

Wind turbine radar echoes exhibit significant scattering power and Doppler spectrum broadening effects, which can interfere with the detection of meteorological targets and subsequently impact weather prediction and disaster warning decisions. In operational weather radar applications, the influence of wind farm on radar observations must be fully considered by meteorological departments and related institutions. In this paper, a Wind Turbine Clutter Classification Algorithm based on Random Forest (WTCDA-RF) classification is proposed. The level-II radar data is processed in blocks, and the spatial position invariance of wind farm clutter is leveraged for feature extraction. Samples are labeled based on position information, and valid samples are screened and saved to construct a vector sample set of wind farm clutter. Through training and optimization, the proposed WTCDA-RF model achieves an ACC of 90.92%, a PRE of 89.37%, a POD of 92.89%, and an F1-score of 91.10%, with a CSI of 83.65% and a FAR of only 10.63%. This not only enhances the accuracy of weather forecasts and ensures the reliability of radar data but also provides operational conditions for subsequent clutter removal, improves disaster warning capabilities, and ensures timely and accurate warning information under extreme weather conditions. Full article

Vehicular communication is crucial for the future of intelligent transportation systems. However, providing continuous high-data-rate connectivity for vehicles in hard-to-reach areas, such as highways, rural regions, and disaster zones, is challenging, as deploying ground base stations (BSs) is either infeasible or too costly. In this paper, multiple unmanned aerial vehicles (UAVs) using millimeter-wave (mmWave) bands are proposed to deliver high-data-rate and secure communication links to vehicles. This is due to UAVs’ ability to fly, hover, and maneuver, and to mmWave properties of high data rate and security, enabled by beamforming capabilities. In this scenario, the vehicle should autonomously select the optimal UAV to maximize its achievable data rate and ensure long coverage periods so as to reduce the frequency of UAV handovers, while considering the UAVs’ battery lives. However, predicting UAVs’ coverage periods and optimizing mmWave beam directions are challenging, since no prior information is available about UAVs’ positions, speeds, or altitudes. To overcome this, out-of-band communication using orthogonal time-frequency space (OTFS) modulation is employed to enable the vehicle to estimate UAVs’ speeds and positions by assessing channel state information (CSI) in the Delay-Doppler (DD) domain. This information is used to predict maximum coverage periods and optimize mmWave beamforming, allowing for the best UAV selection. Compared to other benchmarks, the proposed scheme shows significant performance in various scenarios. Full article

Background: Digital ulcers (DUs) are among the most debilitating vascular complications in SSc and are commonly attributed to microvascular damage. However, recent evidence suggests a potential involvement of macrovascular abnormalities, including subclinical atherosclerosis and altered hemodynamic parameters. Objectives: This study aimed to investigate the association between a history of DUs and macrovascular involvement in SSc patients through carotid and vertebral Doppler ultrasonography, with a focus on hemodynamic indices such as Peak Systolic Velocity (PSV), End-Diastolic Velocity (EDV), Resistive Index (RI), and Intima–Media Thickness (IMT). Methods: A cross-sectional study was conducted on 107 SSc patients. Clinical, serological, cardiovascular, and metabolic data were collected, and carotid–vertebral ultrasound was performed. Patients were stratified based on DU history. Statistical analyses assessed associations between DU status and carotid–vertebral US findings. Results: Patients with DUs showed a significantly higher PSV in both right (86.9 ± 67.9 vs. 64.2 ± 20.5 cm/s, p = 0.010) and left ICA (78.9 ± 29.6 vs. 63.4 ± 18.2 cm/s, p = 0.002). Right ICA RI vas elevated in the DU group (p = 0.021). PSV in the external carotid arteries was also bilaterally increased in DU patients (p < 0.005). DU-positive patients had a higher prevalence of left carotid plaques (p = 0.012) and right-sided ICA RI > 0.75 (p = 0.01). Logistic regression identified DU history as an independent predictor of PSV at ICA (β = 31.89, p = 0.043) and carotid plaque presence at any side (OR 14.34, p = 0.012). Conclusions: A history of digital ulcers in SSc patients is associated with altered carotid hemodynamics and an increased subclinical atherosclerotic burden. These findings suggest that DUs may reflect not only microvascular damage, but also macrovascular dysfunction, supporting the need for integrated vascular assessment in SSc clinical practice. Full article

Background: Pulmonary thromboendarterectomy (PTE) remains the preferred treatment for surgical accessible thrombus in patients with chronic thromboembolic pulmonary hypertension (CTEPH). However, residual pulmonary hypertension (PH) can persist post-PTE. Methods: A retrospective single-center analysis of patients that underwent PTE between 2013 and 2023. At 3-month follow-up, we performed a qualitative Echo-Doppler (DE) assessment and applied a semi-quantitative DE scoring system (DESS), assigning point values for six DE parameters: right ventricle (RV) size, RV shape (systolic base–apex ratio), RV function, septal position, tricuspid regurgitation (TR) and RV outflow tract notching (RVOTN). Higher scores suggested a more significant residual PH syndrome. Results: A total of 188 subjects (80%) did not require further PH intervention at ≥3 months (Group A); 48 (20%) required ongoing PH treatment (Group B). The pre-PTE median DESS was 10 and the post-PTE median DESS was 3.00 (range 0–16). The maximum DESS was 17. Using ROC analysis, post-PTE DESS strongly discriminated between Group A and B (AUC 0.76; 95% CI 0.65–0.89; p < 0.001). A post-PTE DESS of >6.5 differentiated Group A and B. Evidence of TR (OR 0.191, CI 0.103–0.279; p < 0.0001) and RV enlargement (OR 0.242; CI 0.153–0.330; p < 0.0001) at follow-up was associated with a need for additional PH interventions. Conclusions: Serial DE examination is a viable, noninvasive method to assess significant residual PH post-PTE. Full article

Background: Vascular dysfunction is increasingly recognized as a shared contributor to both cognitive impairment and late-life depression (LLD). However, the combined diagnostic value of cerebral hemodynamics, neuroimaging markers, and neuropsychological outcomes remains underexplored. This study aimed to investigate the associations be-tween transcranial Doppler (TCD) ultrasound parameters, cognitive performance, and depressive symptoms in older adults with mild cognitive impairment (MCI) and LLD. Importantly, we evaluated the integrative value of TCD-derived indices alongside MRI-confirmed white matter lesions (WMLs) and standardized neurocognitive and affective assessments. Methods: In this cross-sectional study, 96 older adults were enrolled including 78 cognitively unimpaired individuals and 18 with MCI. All participants underwent structured clinical, neuropsychological, and imaging evaluations including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS-15), MRI-based Fazekas scoring of WMLs, and TCD ultrasonography of the middle cerebral artery. Hemodynamic variables included mean blood flow velocity (MBFV), end-diastolic velocity (EDV), pulsatility index (PI), and resistive index (RI). Logistic regression and receiver operating characteristic (ROC) analyses were used to identify independent predictors of MCI. Results: Participants with MCI showed significantly lower MBFV and EDV, and higher PI and RI (p < 0.05 for all) compared with cognitively unimpaired participants. In multivariate analysis, lower MBFV (OR = 0.64, p = 0.02) and EDV (OR = 0.70, p = 0.03), and higher PI (OR = 3.2, p < 0.01) and RI (OR = 1.9, p < 0.01) remained independently associated with MCI. ROC analysis revealed excellent discriminative performance for RI (AUC = 0.919) and MBFV (AUC = 0.879). Furthermore, PI correlated positively with depressive symptom severity, while RI was inversely related to the GDS-15 scores. Conclusions: Our findings underscore the diagnostic utility of TCD-derived hemodynamic parameters—particularly RI and MBFV—in identifying early vascular contributions to cognitive and affective dysfunction in older adults. The integration of TCD with MRI-confirmed WML assessment and standardized cognitive/mood measures represents a novel and clinically practical multi-modal approach for neurovascular profiling in aging populations. Full article

Background and Objectives: Lower limb defects often present significant reconstructive challenges due to limited soft tissue availability and exposure of critical structures. Perforator-based flaps offer reliable solutions, with minimal donor site morbidity. This study aimed to evaluate the efficacy of infrared thermography (IRT) in preoperative planning and postoperative monitoring of perforator-based flaps, assessing its accuracy in identifying perforators, predicting complications, and optimizing outcomes. Materials and Methods: A prospective observational study was conducted on 76 patients undergoing lower limb reconstruction with fascio-cutaneous perforator flaps between 2022 and 2024. Perforator mapping was performed concurrently with IRT and Doppler ultrasonography (D-US), with intraoperative confirmation. Flap design variables and systemic parameters were recorded. Postoperative monitoring employed thermal imaging on days 1 and 7. Outcomes were correlated with thermal, anatomical, and systemic factors using statistical analyses, including t-tests and Pearson correlation. Results: IRT showed high sensitivity (97.4%) and positive predictive value (96.8%) for perforator detection. A total of nine minor complications occurred, predominantly in patients with diabetes mellitus and/or elevated glycemia (p = 0.05). Larger flap-to-defect ratios (A/C and B/C) correlated with increased complications in propeller flaps, while smaller ratios posed risks for V-Y and Keystone flaps. Thermal analysis indicated significantly lower flap temperatures and greater temperature gradients in flaps with complications by postoperative day 7 (p < 0.05). CRP levels correlated with glycemia and white blood cell counts, highlighting systemic inflammation’s impact on outcomes. Conclusions: IRT proves to be a reliable, non-invasive method for perforator localization and flap monitoring, enhancing surgical planning and early complication detection. Combined with D-US, it improves perforator selection and perfusion assessment. Thermographic parameters, systemic factors, and flap design metrics collectively predict flap viability. Integration of IRT into surgical workflows offers a cost-effective tool for optimizing reconstructive outcomes in lower limb surgery. Full article

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

Accurate Doppler frequency estimation for Low Earth Orbit (LEO)-based Signals of Opportunity (SoOP) positioning faces significant challenges from extreme dynamics (±40 kHz Doppler shift, 0.4 Hz/ms fluctuation) and severe SNR fluctuations induced by multi-beam switching. Empirical analysis reveals that phased-array beamforming generates three-tiered SNR fluctuation patterns during unpredictable beam handovers, rendering conventional single-algorithm solutions fundamentally inadequate. To address this limitation, we propose KDFE (KNN-Driven Fusion Estimator)—an adaptive framework integrating the Rife–Vincent algorithm and MLE via intelligent switching. Global FFT processing extracts real-time Doppler-SNR parameter pairs, while a KNN-based arbiter dynamically selects the optimal estimator by: (1) Projecting parameter pairs into historical performance space, (2) Identifying the accuracy-optimal algorithm for current beam conditions, and (3) Executing real-time switching to balance accuracy and robustness. This decision model overcomes the accuracy-robustness trade-off by matching algorithmic strengths to beam-specific dynamics, ensuring optimal performance during abrupt SNR transitions and high Doppler rates. Both simulations and field tests demonstrate KDFE’s dual superiority: Doppler estimation errors were reduced by 26.3% (vs. Rife–Vincent) and 67.9% (vs. MLE), and 3D positioning accuracy improved by 13.6% (vs. Rife–Vincent) and 49.7% (vs. MLE). The study establishes a pioneering framework for adaptive LEO-SoOP positioning, delivering a methodological breakthrough for LEO navigation. Full article

High-frequency (HF) communication is critical for applications such as over-the-horizon positioning and ionospheric detection. However, precise time-delay estimation in complex HF channels faces significant challenges from multipath fading, Doppler shifts, and noise. This paper proposes a Modulation Signal-based Adaptive Time-Delay Estimation (MATE) algorithm, which effectively decouples carrier and modulation signals and integrates phase-locked loop (PLL) and delay-locked loop (DLL) techniques. By leveraging the autocorrelation properties of 8PSK (Eight-Phase Shift Keying) signals, MATE compensates for carrier frequency deviations and mitigates multipath interference. Simulation results based on the Watterson channel model demonstrate that MATE achieves an average time-delay estimation error of approximately 0.01 ms with a standard deviation of approximately 0.01 ms, representing a 94.12% reduction in mean error and a 96.43% reduction in standard deviation compared to the traditional Generalized Cross-Correlation (GCC) method. Validation with actual measurement data further confirms the robustness of MATE against channel variations. MATE offers a high-precision, low-complexity solution for HF time-delay estimation, significantly benefiting applications in HF communication systems. This advancement is particularly valuable for enhancing the accuracy and reliability of time-of-arrival (TOA) detection in HF-based sensor networks and remote sensing systems. Full article

In Global Navigation Satellite System (GNSS)-denied environments, opportunistic positioning using non-cooperative Low Earth Orbit (LEO) satellite signals has shown strong potential. However, dynamic platforms face challenges in maintaining sufficient satellite counts and favorable geometric distributions due to limited signal quality and short observation windows. To address this, we propose a fast satellite selection algorithm based on the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA) for dynamic, multi-constellation LEO opportunistic navigation. By introducing Pareto non-dominated solutions, the algorithm balances Doppler Geometric Dilution of Precision (DGDOP), signal strength, residual visibility time, and receiver sensitivity. Through iterative optimization, it constructs a subset of satellites with minimal DGDOP while reducing computational burden, enabling real-time fusion and switching at the receiver end. We validate the algorithm through UAV-based experiments in dynamic scenarios. Compared to GWO, PSO, and NSGA-II, the proposed method achieves computation time reductions of 27.06%, 27.05%, and 68.57%, respectively. It also reduces the overall navigation solution time to 54.96% of that required when using all visible satellites, significantly enhancing real-time responsiveness and system robustness. These results demonstrate that the NSWOA-based satellite selection algorithm outperforms existing intelligent methods in both computational efficiency and optimization accuracy, making it well-suited for real-time, multi-constellation LEO dynamic opportunistic navigation. Full article

Wind shear at the Hong Kong International Airport (HKIA) poses a significant safety risk due to terrain-induced airflow disruptions near the runways. Accurate assessment is essential for safeguarding aircraft during take-off and landing, as abrupt changes in wind speed or direction can compromise flight stability. This study introduces a hybrid framework for short-term wind shear prediction based on data collected from Doppler LiDAR systems positioned near the central and south runways of the HKIA. These systems provide high-resolution measurements of wind shear magnitude along critical flight paths. To predict wind shear more effectively, the proposed framework integrates a signal processing technique with a deep learning strategy. It begins with optimized variational mode decomposition (OVMD), which decomposes the wind shear time series into intrinsic mode functions (IMFs), each capturing distinct temporal characteristics. These IMFs are then modeled using bidirectional gated recurrent units (BiGRU), with hyperparameters optimized via the Tree-structured Parzen Estimator (TPE). To further enhance prediction accuracy, residual errors are corrected using Extreme Gradient Boosting (XGBoost), which captures discrepancies between the reconstructed signal and actual observations. The resulting OVMD–BiGRU–XGBoost framework exhibits strong predictive performance on testing data, achieving R² values of 0.729 and 0.926, RMSE values of 0.931 and 0.709, and MAE values of 0.624 and 0.521 for the central and south runways, respectively. Compared with GRUs, LSTM, BiLSTM, and ResNet-based baselines, the proposed framework achieves higher accuracy and a more effective representation of multi-scale temporal dynamics. It contributes to improving short-term wind shear prediction and supports operational planning and safety management in airport environments. Full article

Bistatic active sonar enables robust and precise target position and tracking, making it a key technology for autonomous underwater vehicles (AUVs) in underwater surveillance. This paper proposes a multi-information-assisted target tracking algorithm for bistatic active sonar, leveraging spatial and temporal echo signal structures to address the challenges of AUVs in shallow water. First, broadened cluster formations in sonar echoes are analyzed, leading to the integration of a spatial clustering-based data association. This paper departs from conventional methods by fusing target position, echo amplitude, and Doppler information during the movement of AUVs, which can improve the efficiency of association probability computation. The re-derived multi-information-assisted association probability calculation method and algorithmic workflow are explicitly designed for real-time implementation in AUV systems. Simulation experiments verify the feasibility of integrating Doppler and amplitude information. The sea trial data from simulated AUV-deployed bistatic sonar contained only amplitude information due to experimental limitations. By utilizing this amplitude information, the algorithm proposed in this paper demonstrates a 23.95% performance improvement over the traditional probabilistic data association algorithm. The proposed algorithm provides AUVs with enhanced tracking autonomy, significantly advancing their capability in ocean engineering applications. Full article

With the growth of Internet of Things (IoT) business demands, NB-IoT integrating low earth orbit (LEO) satellite communication systems is considered a crucial component for achieving global coverage of IoT networks in the future. However, the long propagation delay and significant Doppler frequency shift of the satellite-to-ground link pose substantial challenges to the uplink and downlink synchronization in LEO satellite-based NB-IoT networks. To address this challenge, we first propose a Multiple Segment Auto-correlation (MSA) algorithm to detect the downlink Narrow-band Primary Synchronization Signal (NPSS), specifically tailored for the large Doppler frequency shift of LEO satellites. After detection, downlink synchronization can be realized by determining the arrival time and frequency of the NPSS. Then, to complete the uplink synchronization, we propose a position-based scheme to obtain the Timing Advance (TA) values and pre-compensated Doppler shift value. In this scheme, we formulate a time difference of arrival (TDOA) equation using the arrival times of NPSSs from different satellites or at different times as observations. After solving the TDOA equation using the Chan method, the uplink synchronization is completed by obtaining the TA values and pre-compensated Doppler shift value from the terminal position combined with satellite ephemeris. Finally, the feasibility of the proposed scheme is verified in an Iridium satellite constellation. Compared to conventional GNSS-assisted methods, the approach proposed in this paper reduces terminal power consumption by 15–40%. Moreover, it achieves an uplink synchronization success rate of over 98% under negative SNR conditions. Full article

This paper presents the results of theoretical and experimental investigations of a Hong–Ou–Mandel interferometer in which an optical beam splitter is replaced by an ultrasonic wave. The ultrasonic wave acts as an acousto-optical beam splitter for light, which is based on the phenomenon of Bragg diffraction on an ultrasonic wave. The Doppler effect was considered in the theoretical considerations and confirmed experimentally. It has been shown theoretically and experimentally that the Doppler effect changes the frequency of two-photon states at the outputs of an acousto-optical beam splitter. The frequency of the two-photon state in the positive diffraction order is increased by the frequency of the ultrasonic wave, whereas in the negative diffraction order, it is reduced by the frequency of the ultrasonic wave. It should be emphasized that there are no states $\left|{1⟩}_1\right|{1⟩}_2$ in the outputs (diffraction orders), which disappear as a result of Hong–Ou–Mandel interference; consequently, the probability of detecting coincidences of photons between the plus first and minus first diffraction orders is zero, as it occurs in the Hong–Ou–Mandel interferometer. The frequency difference between the two-photon states at the outputs of the acousto-optical beam splitter was confirmed by recording the two-photon beat phenomenon. The obtained results changed the current view that the Doppler effect caused by ultrasonic waves can be neglected in the interaction of correlated pairs of photons with ultrasonic waves. Full article