4.7 Article

Compressive Sensing With Wigner $D$-Functions on Subsets of the Sphere

期刊

IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 70, 期 -, 页码 5652-5667

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2022.3223848

关键词

Compressive sensing; antenna metrology; slepian functions

资金

  1. NSF [CCF-1704204, CCF-2106834]

向作者/读者索取更多资源

In this paper, we prove a compressive sensing guarantee on the rotation group by defining Slepian functions on a measurement sub-domain and transforming the inverse problem to the Slepian functions. By requiring measurements on a select-able sub-domain, our approach provides higher accuracy and reduces the number of measurements compared to other methods using Wigner D-functions. Numerical examples demonstrate the superiority of our method in reconstruction quality.
In this paper, we prove a compressive sensing guarantee for restricted measurement domains on the rotation group, $\mathrm{SO}(\text{3})$. We do so by first defining Slepian functions on a measurement sub-domain $R$ of the rotation group $\mathrm{SO}(\text{3})$. Then, we transform the inverse problem from the measurement basis, the bounded orthonormal system of band-limited Wigner $D$-functions on $\mathrm{SO}(\text{3})$, to the Slepian functions in a way that limits increases to signal sparsity. Contrasting methods using Wigner $D$-functions that require measurements on all of $\mathrm{SO}(\text{3})$, we show that the orthogonality structure of the Slepian functions only requires measurements on the sub-domain $R$, which is select-able. Due to the particulars of this approach and the inherent presence of Slepian functions with low concentrations on $R$, our approach gives the highest accuracy when the signal under study is well concentrated on $R$. We provide numerical examples of our method in comparison with other classical and compressive sensing approaches. In terms of reconstruction quality, we find that our method outperforms the other compressive sensing approaches we test and is at least as good as classical approaches but with a significant reduction in the number of measurements.

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