A5081671032- Synthetic Aperture Radar (SAR) Applications and Techniques
- Advanced SAR Imaging Techniques
- Robotics and Sensor-Based Localization
- Microwave Imaging and Scattering Analysis
- Sparse and Compressive Sensing Techniques
- Space Satellite Systems and Control
- Machine Learning and ELM
- Radar Systems and Signal Processing
- Advanced X-ray and CT Imaging
- Inertial Sensor and Navigation
- Underwater Acoustics Research
- Seismic Imaging and Inversion Techniques
- Target Tracking and Data Fusion in Sensor Networks
- Domain Adaptation and Few-Shot Learning
- Satellite Image Processing and Photogrammetry
- Geophysical Methods and Applications
- Advanced Image and Video Retrieval Techniques
- Medical Imaging Techniques and Applications
- Medical Image Segmentation Techniques
- Spacecraft Dynamics and Control
- Microfluidic and Bio-sensing Technologies
- Image and Object Detection Techniques
- Bacteriophages and microbial interactions
- Molecular Communication and Nanonetworks
Sun Yat-sen University
2021-2024
In this article, we propose a dual-frequency synthetic aperture radar tomography (TomoSAR) method with fewer flights for efficient urban building reconstruction. By utilizing signals, our overcomes the limitations of traditional single-frequency TomoSAR in terms data acquisition time and temporal decoherence. The proposed can achieve imaging results comparable to methods only half number required. We present an model derive theoretical accuracy model. A analysis shows that are approximately...
Purpose This study aims to establish the laser links between satellites among large-scale distributed satellite systems; a combined attitude control strategy containing two stages is proposed in this paper. Design/methodology/approach These are: one initial pointing change of other based on position information each satellite; high precision tracking scan uncertainty cone because accuracy not enough link. At stage, method determine target presented satellite, and fuzzy adaptive algorithm...
Synthetic aperture radar (SAR) image registration is a key technology in SAR processing. The accuracy and efficiency of directly affect the quality subsequent In order to further improve computational traditional SAR-SIFT (SAR-scale invariant feature transform, SAR-SIFT) algorithm, an improved algorithm based on Kernel entropy component analysis (KECA) proposed. Firstly, SAR-Harris scale space established, extreme points are selected main directions calculated; then, descriptor generated,...
This paper aims to perform imaging and detect moving targets in a 3D scene for space-borne air target indication (AMTI). Specifically, we propose feasible framework distributed LEO SAR via spectral estimation. contains four subsystems: the satellite radar modeling, information processing, baseline design framework, spectrum estimation imaging. Firstly our method, develop relative motion model between platform modeling. In very short time, is approximated as uniform motion. We then establish...
This paper presents a simulation method for spaceborne SAR raw data. The consists of geometric modeling, electromagnetic modeling and echo simulation. It can complete tasks different urban scenes, satellites radar parameters. effectiveness the proposed is verified by point target actual scene. In addition, we also design simulations distributed 3D TomoSAR reconstruction experiment. experiment results not only demonstrate correctness from another perspective, but show huge potential in...
Tomographic Synthetic Aperture Radar (TomoSAR) has garnered significant interest due to its capability for three-dimensional reconstruction along the elevation direction from multiple observations. The traditional TomoSAR imaging method poor effects and low accuracy. After introducing CS TomoSAR, although accuracy been improved, based on requires hundreds or thousands of iterations, resulting in long calculation times efficiency. Additionally, manual parameter tuning poses a challenge,...
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR), as a frontier topic in the domain of artificial intelligence regarding anthropology, has witnessed some good results deep learning technology progressively develops. However, it is still limited by complexity structure network need large number parameters and high dimensions, easily leading to great consumption time. To solve this problem, paper proposes an effective efficient method, which called multi-scale Broad Learning...