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Dingyang Wang

Publications and source records attributed to Dingyang Wang.

3 recordsLinked to original sources

A Stochastic Doppler Sensing Framework for Range-Resolved Bidirectional Particle Dynamics in a Bubbling Fluidized Bed at 60 GHz

The problem of observing particle motion inside an optically opaque, dense bubbling fluidized bed is addressed. Differential-pressure measurements identify the bulk fluidization state but do not reveal where particles move or how their velocities vary with height. A commercial 60 GHz frequency-modulated continuous-wave radar is mounted above the facility to obtain depth-resolved measurements without optical access or an intrusive probe. Each measured depth cell contains an ensemble of 600 micrometre sand grains in the Rayleigh-Mie scattering transition region. Particle displacement, cell exchange, and changes in shadowing alter echo phases and amplitudes. Their coherent sum is therefore a stochastic complex signal, whose covariance and Doppler spectrum describe ensemble motion. A statistical spectral analysis retrieves total power, mean line-of-sight radial velocity, and Doppler spectral width over range and time. Eight-minute measurements reveal an expanding radar-detectable layer and alternating motion towards and away from the radar. Positive- and negative-mean regions coexist during developed fluidization and exchange their contribution to particle power. Most detectable spectra contain one dominant Doppler lobe; a small subset supports two distinguishable lobes, including cases with one negative and one positive mean velocity. The 5-s total-power trend has a correlation of 0.734 with measured pressure drop, whereas the local fitted spectral-width trend has a weaker correlation of 0.152. This work develops a range-resolved statistical Doppler measurement that reveals bidirectional particle motion and determines when multiple local velocity contributions are distinguishable.

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Grouped Target Tracking and Seamless People Counting with a 24 GHz MIMO FMCW

The problem of radar-based tracking of groups of people moving together and counting their numbers in indoor environments is considered here. A novel processing pipeline to track groups of people moving together and count their numbers is proposed and validated. The pipeline is specifically designed to deal with frequent changes of direction and stop & go movements typical of indoor activities. The proposed approach combines a tracker with a classifier to count the number of grouped people; this uses both spatial features extracted from range-azimuth maps, and Doppler frequency features extracted with wavelet decomposition. Thus, the pipeline outputs over time both the location and number of people present. The proposed approach is verified with experimental data collected with a 24 GHz Frequency Modulated Continuous Wave (FMCW) radar. It is shown that the proposed method achieves 95.59% accuracy in counting the number of people, and a tracking metric OSPA of 0.338. Furthermore, the performance is analyzed as a function of different relevant variables such as feature combinations and scenarios.

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Analysis of Processing Pipelines for Indoor Human Tracking using FMCW radar

In this paper, the problem of formulating effective processing pipelines for indoor human tracking is investigated, with the usage of a Multiple Input Multiple Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar. Specifically, two processing pipelines starting with detections on the Range-Azimuth (RA) maps and the Range-Doppler (RD) maps are formulated and compared, together with subsequent clustering and tracking algorithms and their relevant parameters. Experimental results are presented to validate and assess both pipelines, using a 24 GHz commercial radar platform with 250 MHz bandwidth and 15 virtual channels. Scenarios where 1 and 2 people move in an indoor environment are considered, and the influence of the number of virtual channels and detectors' parameters is discussed. The characteristics and limitations of both pipelines are presented, with the approach based on detections on RA maps showing in general more robust results.

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