Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30886
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Sparse multidimensional exponential analysis with an application to radar imaging
Author(s): Cuyt, Annie
Hou, Yuan
Knaepkens, Ferre
Lee, Wen-shin
Contact Email: wen-shin.lee@stir.ac.uk
Keywords: exponentional analysis
parametric method
multidimensional
sparse model
sparse data
inverse problems
Issue Date: 2020
Citation: Cuyt A, Hou Y, Knaepkens F & Lee W (2020) Sparse multidimensional exponential analysis with an application to radar imaging. SIAM Journal on Scientific Computing, 42 (3), p. B675–B695. https://doi.org/10.1137/19M1278004
Abstract: We present a d-dimensional exponential analysis algorithm that offers a range of advantages compared to other methods. The technique does not suffer the curse of dimensionality and only needs O((d + 1)n) samples for the analysis of an n-sparse expression. It does not require a prior estimate of the sparsity n of the d-variate exponential sum. The method can work with sub-Nyquist sampled data and offers a validation step, which is very useful in low SNR conditions. A favourable computation cost results from the fact that d independent smaller systems are solved instead of one large system incorporating all measurements simultaneously. So the method also lends itself easily to a parallel execution. Our motivation to develop the technique comes from 2D and 3D radar imaging and is therefore illustrated on such examples.
DOI Link: 10.1137/19M1278004
Rights: Publisher policy allows this work to be made available in this repository. Published in SIAM Journal on Scientific Computing by SIAM. The original publication is available at: https://doi.org/10.1137/19M1278004
Licence URL(s): https://storre.stir.ac.uk/STORREEndUserLicence.pdf

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