Weiwei Qin

Altitude above Ground Level (AGL) is an important parameter in aircraft flight operations and safety. Conventional aircraft altitude measurement techniques are largely constrained by their dependence on active signal transmission, sensitivity to environmental conditions, limited operational coverage, and inherently localized single-point measurement geometry. This paper presents a method that leverages Global Navigation Satellite System-Reflectometry (GNSS-R) to acquire AGL height, providing a complementary solution to current measurement systems. The proposed system employs an upward-Right-Hand Circular Polarization (RHCP) antenna and a downward-Left-Hand Circular Polarization (LHCP) antenna together with an Intermediate Frequency (IF) collector for raw data collection. The collected data are subsequently processed offline using a Software Defined Receiver (SDR). This paper proposes the Short‑Time Shortest‑Path (STSP) method to retrieve high‑accuracy GNSS‑R AGL, which uses only 25 incoherent accumulations and selects the shortest path delay when the reflected waveform exhibits multiple peaks. For a feasibility assessment, we installed the system on an Aurora SA60L aircraft and conducted an approximately 288-km flight from Dongying City to Tai’an City of Shandong Province, China. Dual-frequency Global Navigation Satellite System (GNSS) IF data on the shared frequency bands were collected during the flight over diverse terrains, including rural, urban, suburban, and mountainous areas. BeiDou Navigation Satellite System (BDS) B1C/B2a, Global Positioning System (GPS)/Quasi-Zenith Satellite System (QZSS) L1 C/A/L5, and Galileo E1C/E5a signals were leveraged to compute the code- level path -delay differences between direct and reflected signals. The measurements were used to retrieve the aircraft's heights above multiple distinct specular reflection points. The results demonstrate the capability of GNSS-R technology to obtain reliable AGL height estimates under various surface conditions. The GNSS-R derived AGL solutions were compared with the GNSS-Digital Elevation Model (DEM) for accuracy assessment. The results show that the proposed method achieves a height estimation accuracy of better than 4 m in flat terrain and about 15 m in mountainous areas.

To address the issues of insufficient single-modality robustness and limited multi-scale object detection accuracy in remote sensing image detection (RSID) in complex environments, this paper proposes a multimodal RSID network named VI-MSFFN. The model adopts a symmetric parallel dual-branch architecture to achieve independent extraction and collaborative modeling of visible and infrared modal features. A cross-modal multi-scale sparse cross-attention fusion module is proposed and applied to the P4 and P5 feature layers, and a high-low-level feature collaborative cross-modal fusion strategy was constructed to achieve efficient and robust cross-modal feature fusion while enhancing multi-scale object modeling capability and suppressing feature redundancy and noise. Additionally, a progressive feature interaction and fusion architecture was designed to combine spatial and frequency domain information to strengthen deep object representation. The experimental results on the VEDAI and Drone Vehicle datasets demonstrate that VI-MSFFN achieves state-of-the-art (SOTA) performance in detection accuracy, robustness, and generalization ability. The proposed method effectively solves the detection challenges of RSID and has significant application value in the field of multi-modal RSID.

Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising approach for global ocean wind monitoring with high temporal resolution. However, accurate wind speed retrieval remains challenging due to the complex scattering mechanisms and the nonlinear coupling between delay–Doppler maps (DDMs) and observation geometries. To address these limitations, a Physics-Aware Hybrid CNN–Transformer Network (PA-HCTN) is proposed accordingly. The model integrates a CNN for local DDM feature extraction, a Transformer encoder for global context modeling, and a cross-attention module to dynamically fuse auxiliary physical parameters. A geophysical model function (GMF)-constrained loss is incorporated to enhance physical consistency. Evaluated on CYGNSS and ERA5 data, the PA-HCTN achieves an RMSE of 1.35 m/s and an R² of 0.75, outperforming existing benchmarks and significantly mitigating high-wind-speed underestimation. In addition, through independent validation using NDBC buoy data from four sites, the results demonstrate the effectiveness of the hybrid architecture and physics-aware design for GNSS-R wind retrieval.

Energetic materials undergo chemical bond cleavage and structural reorganization under external stimuli such as heat and irradiation, leading to performance degradation or change. This study employs molecular dynamics simulations to investigate the atomic-scale mechanisms of irradiation-induced damage in β-HMX and its subsequent thermal decomposition mechanism. Results revealed that irradiation defects are primarily localized within collision cascade regions. Irradiation-generated nitrogen oxides and HNO _x radicals provide new reaction sites during HMX thermal decomposition, significantly altering pyrolysis pathways and increasing the activation energy. Concurrently, irradiation-induced defects restructure the reaction network, modifying the propagation efficiency of radical chain reactions. This work provides atomic-level insights into the microscopic mechanism through which irradiation influences the thermal decomposition of HMX, offering a theoretical foundation for understanding the chemical evolution and stability of energetic materials under extreme conditions.

To address the issue of decreased positioning accuracy caused by interference or blockage of GNSS signals in vehicle navigation systems, this paper proposes a GNSS/MEMS IMU array fusion localization method based on an improved grey prediction model. First, a multi-feature fusion GNSS confidence evaluation algorithm is designed to assess the reliability of GNSS data in real time using indicators such as signal strength, satellite visibility, and solution consistency; second, to overcome the limitations of traditional grey prediction models in processing vehicle complex motion data, two key improvements are proposed: (1) a dynamic background value optimization method based on vehicle motion characteristics, which dynamically adjusts the weight coefficients in the background value construction according to vehicle speed, acceleration, and road curvature, enhancing the model’s sensitivity to changes in vehicle motion state; (2) a residual sequence compensation mechanism, which analyzes the variation patterns of historical residual sequences to accurately correct the prediction results, significantly improving the model’s prediction accuracy in nonlinear motion scenarios; finally, an adaptive fusion framework under normal and denied GNSS conditions is constructed, which directly fuses data when GNSS is reliable, and uses the improved grey model prediction results as virtual measurements for fusion during signal denial. Simulation and vehicle experiments verify that: compared to the traditional GM(1,1) model, the proposed method improves prediction accuracy by 31%, 52%, and 45% in straight, turning, and acceleration scenarios, respectively; in a 30-s GNSS denial scenario, the accuracy is improved by over 79% compared to pure INS methods.

Visual grounding for remote sensing (RSVG) focuses on localizing specific objects in remote sensing (RS) imagery based on linguistic expressions. Existing methods typically employ pre-trained models to locate the referenced objects. However, due to the insufficient capability of cross-modal interaction and alignment, the extracted visual features may suffer from semantic drift, limiting the performance of RSVG. To address this, the article introduces a novel RSVG framework named the text–visual interaction multilevel feature alignment network (TVI-MFAN), which leverages a text–visual interaction attention (TVIA) module to dynamically generate adaptive weights and biases at both spatial and channel dimensions, enabling the visual feature to focus on relevant linguistic expressions. Additionally, a multilevel feature alignment network (MFAN) aggregates contextual information by using cross-modal alignment to enhance features and suppress irrelevant regions. Experiments demonstrate that the proposed method achieves 75.65% and 80.24% (2.42% and 3.1% absolute improvement) accuracy on the OPT-RSVG and DIOR-RSVG dataset, validating its effectiveness.

Conventional techniques to measure drone heights from the ground, including global navigation satellite systems (GNSSs), barometers, acoustic sensors, and LiDAR, are limited by their measurement ranges, an inability to directly obtain the height from the ground, or poor concealment. To overcome these shortcomings, we propose the use of GNSS reflectometry (GNSS-R) to determine a drone’s height from the ground. We conducted experiments over farmland and an urban road using a drone that carried an upward-looking right-hand circularly polarized (RHCP) antenna, a downward-looking left-hand circularly polarized (LHCP) antenna, and an intermediate frequency (IF) data collector to test the performance. Three flights were conducted in a bare soil scenario, a sparse apple orchard scenario, and an urban road scenario. A software-defined receiver was used to process the IF signal data to compute the one-dimensional time-delay-dependent power peak positions of the direct and reflected GNSS signals. Based on these peak positions, the path delay measurements between the direct and reflected signals were derived per second based on the BDS B1C and B2a, GPS C/A, and L5 signals. The drone heights were then retrieved. The results showed that the drone height retrieval accuracy could reach approximately 0.5–2 m.

In the case of a boom in space resource development, space debris will increase dramatically and cause serious problems for the spacecraft in orbit. To address this problem, a novel context sensing-YOLOv5 (CS-YOLOv5) is proposed for small and weak space object detection, which could realize the extraction of local context information and the enhancement and fusion of spatial information. To enhance the expression ability of feature information and the identification ability of the network, we propose the cross-layer context fusion module (CCFM) through multiple branches in parallel to learn the context information of different scales. At the same time, to map the small-scale features sequentially to the features of the previous layer, we design the adaptive weighting module (AWM) to assist the CCFM in further enhancing the expression of features. Additionally, to solve the problem that the spatial information of small objects is easily lost, we designed the spatial information enhancement module (SIEM) to adaptively learn the weak spatial information of small objects that need to be protected. To further enhance the generalization ability of CS-YOLOv5, we propose a contrast mosaic data augmentation to enrich the diversity of the sample. Extensive experiments are conducted on self-built datasets, which strongly prove the effectiveness of our method in space object detection.