Sparsity aware dynamic gesture recognition using radar sensors with angular diversity

Sparsity aware dynamic gesture recognition using radar sensors with angular diversity

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In this study, two radar sensors with angular diversity are used to recognise dynamic hand gestures by analysing the sparse micro-Doppler signatures. The radar echoes are firstly mapped into the time–frequency domain through the Gaussian-windowed Fourier dictionary at each radar sensor. Then the sparse time–frequency features are extracted via the orthogonal matching pursuit algorithm. Finally, the sparse time–frequency features at two radar sensors are fused and input into the modified-Hausdorff-distance-based nearest neighbour classifier to achieve the dynamic hand gesture recognition. The experimental results based on the measured data under three different experimental scenes demonstrate that (i) the recognition accuracy can be improved by fusing the features extracted at two radar sensors when each radar sensor works well on its own; (ii) the recognition accuracy produced by feature fusion keeps satisfactory even if one of the radar sensors has poor performance, which means that the feature fusion can improve the robustness of the recognition system; and (iii) it would be more helpful if the line-of-sights of the two radar sensors are set to be orthogonal to each other.


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