Guanxing Wang

Guanxing Wang

Remote Sensing · Time-Series Analysis · Self-Supervised Reconstruction

gxwang111@gmail.com
(+86) 18811693591

Research Profile

PhD candidate in Information and Communication Engineering at Beijing Institute of Technology and visiting PhD researcher at the University of Auckland. My research focuses on remote sensing signal and image processing, time-series analysis, self-supervised reconstruction, and 3D sensing under noisy and incomplete observations. I have developed physics-aware methods for radar time-series imputation and high-resolution imaging, and I am currently extending this work toward efficient multimodal learning for long-horizon time-series analysis. I have participated in 10+ national-level research projects, published 5 SCI journal papers, and have 3 first-author manuscripts under review.
Research Interests
Remote Sensing Image Processing Time-Series Analysis Self-Supervised Learning Radar Imaging 3D Reconstruction Geospatial AI

Research Experience

Remote Sensing Image Reconstruction under Challenging Observations

PhD Research Sep. 2020 – Present

Research on remote sensing image reconstruction, enhancement, and 3D perception from noisy and incomplete observations.

  • Remote Sensing Reconstruction: Developed physics-aware and learning-based methods for high-resolution radar imaging under low-SNR, sparse-sampling, and complex-motion conditions.
  • Time-Frequency Analysis & Estimation: Modelled non-stationary radar time series and developed nonlinear parameter-estimation methods for weak-target detection under low-SNR conditions.
  • Image Enhancement: Developed self-supervised approaches for image denoising and structure-preserving enhancement.
  • 3D Reconstruction: Investigated multi-view fusion and NeRF/3DGS-based methods for high-fidelity 3D reconstruction from sparse observations.
Remote sensing reconstruction

Time-Series Imputation and Efficient Multimodal Learning

University of Auckland Visiting PhD Research Dec. 2025 – Dec. 2026

Research on time-series imputation and efficient multimodal learning for incomplete and long-horizon observations.

  • Time-Series Imputation: Developed self-supervised methods for reconstructing irregularly missing radar echoes from spatiotemporal context.
  • Sparse Imaging: Developed physics-aware reconstruction methods for high-resolution radar imaging with up to an 80% echo missing rate.
  • Multimodal Time-Series Learning: Investigated VLM-based modelling of long-horizon time series by retaining informative temporal segments and reducing redundant observations.
  • Efficient Representation: Explored attention-guided temporal token selection for efficient time-series reasoning.
Time-series imputation and multimodal learning

UAV-Based Urban Remote Sensing and 3D Reconstruction

NSFC Distinguished Young Scholars-Funded Project Core Researcher Jul. 2021 – Jul. 2024

Developed UAV-based remote sensing and 3D reconstruction methods for complex urban environments.

  • Data Acquisition: Conducted 40+ UAV sorties at 170–260 m for urban sensing and multi-view data collection.
  • Motion Estimation: Developed physics-based motion estimation and error-compensation methods for low-SNR and complex-motion observations.
  • Remote Sensing Imaging: Applied high-resolution imaging methods to real UAV radar observations under challenging conditions.
  • 3D Reconstruction: Developed multi-view reconstruction methods, achieving sub-meter reconstruction accuracy on representative urban scenes.
UAV urban remote sensing

Long-Horizon Radar Time-Series Analysis and 3D Reconstruction

National Natural Science Foundation of China Key Project Core Researcher Aug. 2022 – Aug. 2024

Research on long-duration radar observations, image enhancement, and 3D reconstruction of non-cooperative targets.

  • Long-Horizon Time-Series Analysis: Modelled long-duration observations by jointly considering target motion and scatterer distribution, enabling simultaneous estimation of motion parameters and 3D structure.
  • Image Enhancement: Developed self-supervised enhancement methods, achieving 10–15 dB SNR improvement.
  • 3D Sensing: Investigated sparse-view NeRF/3DGS-based methods for high-fidelity 3D reconstruction.
  • Experimental Validation: Conducted 100+ experiments and processed TB-scale sensor data for algorithm development and validation.
Long-horizon radar time-frequency analysis

Selected Publications & Patents

Education

Beijing Institute of Technology
Sep. 2020 – Expected Mar. 2027
PhD Candidate in Information and Communication Engineering
Research focus: Remote sensing, image processing, self-supervised reconstruction, radar imaging, and 3D reconstruction.
University of Auckland
Dec. 2025 – Dec. 2026
CSC-Sponsored Visiting PhD Researcher, School of Computer Science
Research focus: Time-series imputation, multimodal time-series analysis, machine learning, and signal processing.
Beijing Institute of Technology
Aug. 2016 – Jun. 2020
BEng in Electronic Information Engineering
GPA: 3.95/4.0, Top 5%

Relevant coursework: Signals and Systems, Digital Signal Processing, Communication Principles.

Technical Skills

Remote Sensing & Signal Processing
  • Radar & Remote Sensing
    • SAR / ISAR imaging
    • Image reconstruction and enhancement
    • Target detection and parameter estimation
    • Time-frequency analysis
Time-Series Analysis
  • Temporal Modelling
    • Missing-data imputation
    • Long-horizon sequence modelling
    • Temporal representation learning
    • Attention-guided temporal selection
Machine Learning
  • Learning Methods
    • Self-supervised learning
    • Transformer and multimodal learning
    • Diffusion models
    • Deep unfolding
3D Sensing & Reconstruction
  • 3D Methods
    • Multi-view reconstruction
    • Sparse-view reconstruction
    • NeRF
    • 3D Gaussian Splatting (3DGS)
Programming
  • Languages & Frameworks
    • Python / PyTorch
    • MATLAB
    • C / C++
Experimental & Data Skills
  • Platforms & Tools
    • UAV-based remote sensing and real-world data acquisition
    • Large-scale sensor data processing
    • COLMAP / MeshLab / CST / FEKO / STK
    • mmWave radar / LiDAR / anechoic chamber experiments

Honors & Awards

Leadership & Activities

Summer Teaching Volunteer Program, China

Project Leader
  • Initiated and organized educational outreach programs in rural areas, coordinating volunteer recruitment, curriculum design, school engagement, and team management.
Teaching volunteer program

American Heart Association & Beijing Red Cross

First Aid Instructor
  • Delivered CPR and first-aid training to more than 1,000 participants across universities, companies, and public events.
First aid training
Contact

If you are interested in my research, collaboration, or postdoctoral opportunities, please leave a message below.