What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
Mengdao Xing mainly focuses on Synthetic aperture radar, Radar imaging, Artificial intelligence, Computer vision and Inverse synthetic aperture radar.
His Synthetic aperture radar research incorporates themes from Radar, Algorithm, Motion compensation and Image resolution.
His Radar imaging study combines topics from a wide range of disciplines, such as Azimuth, Interference, Optics, Filter and Signal.
Mengdao Xing has included themes like Signal-to-noise ratio and Fast Fourier transform in his Artificial intelligence study.
His research in Computer vision intersects with topics in Envelope, Autofocus, Doppler radar and Signal reconstruction.
His work deals with themes such as Side looking airborne radar, Scattering, Image formation, Iterative reconstruction and Compressed sensing, which intersect with Inverse synthetic aperture radar.
His most cited work include:
- Resolution Enhancement for Inversed Synthetic Aperture Radar Imaging Under Low SNR via Improved Compressive Sensing (218 citations)
- Achieving Higher Resolution ISAR Imaging With Limited Pulses via Compressed Sampling (195 citations)
- Motion Compensation for UAV SAR Based on Raw Radar Data (181 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Synthetic aperture radar, Artificial intelligence, Radar imaging, Algorithm and Computer vision.
His studies deal with areas such as Inverse synthetic aperture radar, Azimuth, Optics and Side looking airborne radar as well as Synthetic aperture radar.
In his research on the topic of Artificial intelligence, Moving target indication is strongly related with Clutter.
His study with Radar imaging involves better knowledge in Radar.
His work carried out in the field of Algorithm brings together such families of science as Interpolation, Chirp, Frequency domain, Signal and Trajectory.
The study incorporates disciplines such as Focus, Doppler radar, Pulse repetition frequency and Compressed sensing in addition to Computer vision.
He most often published in these fields:
- Synthetic aperture radar (60.51%)
- Artificial intelligence (42.04%)
- Radar imaging (38.22%)
What were the highlights of his more recent work (between 2019-2021)?
- Synthetic aperture radar (60.51%)
- Algorithm (36.52%)
- Artificial intelligence (42.04%)
In recent papers he was focusing on the following fields of study:
Mengdao Xing mainly investigates Synthetic aperture radar, Algorithm, Artificial intelligence, Radar and Computer vision.
His biological study spans a wide range of topics, including Azimuth, Signal, Image and Trajectory.
His Algorithm research is multidisciplinary, incorporating elements of Wavenumber, Radar imaging, Bistatic radar, Image formation and Iterative reconstruction.
His study in Radar imaging focuses on Inverse synthetic aperture radar in particular.
His Artificial intelligence study combines topics in areas such as Clutter, Scattering and Pattern recognition.
Computer vision is closely attributed to Subspace topology in his work.
Between 2019 and 2021, his most popular works were:
- A Novel Feature Extension Method for the Forest Disaster Monitoring Using Multispectral Data (5 citations)
- InSAR Phase Denoising: A Review of Current Technologies and Future Directions (4 citations)
- Cooperative Multitask Learning for Sparsity-Driven SAR Imagery and Nonsystematic Error Autocalibration (4 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Computer vision
Mengdao Xing focuses on Synthetic aperture radar, Algorithm, Artificial intelligence, Computer vision and Signal processing.
His Synthetic aperture radar research includes elements of Signal, Pulse repetition frequency and Optics.
His Algorithm study integrates concerns from other disciplines, such as Azimuth and Radar imaging, Bistatic radar.
His Radar imaging research is within the category of Radar.
His work investigates the relationship between Artificial intelligence and topics such as Pattern recognition that intersect with problems in Disaster monitoring and Target acquisition.
In general Computer vision study, his work on Image resolution often relates to the realm of TOPS, thereby connecting several areas of interest.
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