ROAST: Rapid Orthogonal Approximate Slepian Transform

• Published in: IEEE transactions on signal processing 2018-11, Vol.66 (22), p.5887-5901
• Main Authors: Zhihui Zhu, Karnik, Santhosh, Wakin, Michael B., Davenport, Mark A., Romberg, Justin
• Format: Article
• Language: English
• Subjects: Approximation; Complexity; Discrete Fourier transforms; Discrete prolate spheroidal sequences; Eigenvalues and eigenfunctions; Error detection; fast Fourier transform; Indexes; Mathematical analysis; signal approximation and compression; Signal processing algorithms; Subspaces; Time-frequency analysis
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2018.2872002

In this paper, we provide a Rapid Orthogonal Approximate Slepian Transform (ROAST) for the discrete vector that one obtains when collecting a finite set of uniform samples from a baseband analog signal. The ROAST offers an orthogonal projection, which is an approximation to the orthogonal projection onto the leading discrete prolate spheroidal sequence (DPSS) vectors (also known as Slepian basis vectors). As such, the ROAST is guaranteed to accurately and compactly represent not only oversampled bandlimited signals but also the leading DPSS vectors themselves. Moreover, the subspace angle between the ROAST subspace and the corresponding DPSS subspace can be made arbitrarily small. The complexity of computing the representation of a signal using the ROAST is comparable to the fast Fourier tranform, which is much less than the complexity of using the DPSS basis vectors. We also give non-asymptotic results to guarantee that the proposed basis not only provides a very high degree of approximation accuracy in a mean squared error sense for bandlimited sample vectors, but also that it can provide high-quality approximations of all sampled sinusoids within the band of interest.