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TFR Reconstruction From Incomplete m-D Signal via Adaptive Hadamard Product Parametrization | IEEE Journals & Magazine | IEEE Xplore

TFR Reconstruction From Incomplete m-D Signal via Adaptive Hadamard Product Parametrization


Abstract:

In micromotion signature analysis, the radar return signal with missing sampling may cause defocused time–frequency representation (TFR) and thus prevent micromotion char...Show More

Abstract:

In micromotion signature analysis, the radar return signal with missing sampling may cause defocused time–frequency representation (TFR) and thus prevent micromotion characteristics acquisition. To address the issue, we present an adaptive time–frequency distribution reconstruction method based on L_{1} regularization. First, the L_{1} regularization is expressed as a combination of two L_{2} regularizations based on Hadamard product parametrization. Then, the iterated Tikhonov regularization is applied to solve each L_{2} regularization alternatively. Moreover, the regularization parameter is updated adaptively based on the matching pursuit principle at each iteration. Finally, the reconstructed TFR is updated based on least-square-error criterion to eliminate the attenuation of signal amplitude. Simulation and measurement data examples have demonstrated the effectiveness of the method.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)
Article Sequence Number: 3501405
Date of Publication: 16 January 2023

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