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Yong Jia
Researcher at Chengdu University of Technology
Publications - 41
Citations - 246
Yong Jia is an academic researcher from Chengdu University of Technology. The author has contributed to research in topics: Radar & Radar imaging. The author has an hindex of 8, co-authored 36 publications receiving 163 citations.
Papers
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Journal ArticleDOI
Multichannel and Multiview Imaging Approach to Building Layout Determination of Through-Wall Radar
TL;DR: The experimental results reveal that the presented noncoherent image fusion method gives the single-view layout images with higher signal-to-clutter-and-noise ratio than the conventional coherent algorithm based on data combination and a near-tidy panorama layout image is generated almost without the cavities and burrs.
Proceedings ArticleDOI
DOA and DOD estimation based on bistatic MIMO radar with co-prime array
TL;DR: In this paper, a reduced-dimension (RD) MUSIC algorithm based on augmented correlation matrix is proposed to estimate the direction of arrival (DOA) and direction of departure (DOD) for the bistatic co-prime multiple-input multiple-output (MIMO) radar.
Journal ArticleDOI
Sign-Coherence-Factor-Based Suppression for Grating Lobes in Through-Wall Radar Imaging
TL;DR: The experimental results with a two-transmitting eight-receiving stepped-frequency continuous-wave through-wall radar verifies the excellent performance of the sign coherence factor (SCF), which has comparable performance in suppressing grating lobes with the PCF, much better than the CF.
Journal ArticleDOI
Sidewall Detection Using Multipath in Through-Wall Radar Moving Target Tracking
TL;DR: A new algorithm to determine the position of the sidewalls by exploiting the multipath echoes of a target bounced from the sidewall, which is useful to obtain the building layout, determine the relative location of the target in the room, and remove the higher order multipath ghosts for a through-wall tracking radar.
Journal ArticleDOI
Multi-frequency and multi-domain human activity recognition based on SFCW radar using deep learning
TL;DR: An approach for human activity recognition (HAR) using deep learning is proposed based on stepped frequency continues wave (SFCW) radar, which achieves 96.42% recognition accuracy about six types of activities by incorporating three frequencies of spectrograms and range map, and surpasses two existed methods.