Grid Size Selection for Nonlinear Least-Squares Optimization in Spectral Estimation and Array Processing

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6 Citationer (Scopus)
210 Downloads (Pure)

Abstrakt

In many spectral estimation and array processing problems, the process
of finding estimates of model parameters often involves the optimisation
of a cost function containing multiple peaks and dips. Such
non-convex problems are hard to solve using traditional optimisation
algorithms developed for convex problems, and computationally intensive
grid searches are therefore often used instead. In this paper,
we establish an analytical connection between the grid size and the
parametrisation of the cost function so that the grid size can be selected
as coarsely as possible to lower the computation time. Additionally,
we show via three common examples how the grid size depends
on parameters such as the number of data points or the number
of sensors in DOA estimation. We also demonstrate that the computation
time can potentially be lowered by several orders of magnitude
by combining a coarse grid search with a local refinement step.
OriginalsprogEngelsk
TitelSignal Processing Conference (EUSIPCO), 2016 24th European
ForlagIEEE
Publikationsdatoaug. 2016
Sider1653-1657
ISBN (Elektronisk)978-0-9928-6265-7
DOI
StatusUdgivet - aug. 2016
Begivenhed European Signal Processing Conference - Hotel Hilton Budapest, Budapest, Ungarn
Varighed: 29 aug. 20162 sep. 2016
http://www.eusipco2016.org/

Konference

Konference European Signal Processing Conference
LokationHotel Hilton Budapest
LandUngarn
ByBudapest
Periode29/08/201602/09/2016
Internetadresse
NavnProceedings of the European Signal Processing Conference (EUSIPCO)
ISSN2076-1465

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    Citationsformater

    Nielsen, J. K., Jensen, T. L., Jensen, J. R., Christensen, M. G., & Jensen, S. H. (2016). Grid Size Selection for Nonlinear Least-Squares Optimization in Spectral Estimation and Array Processing. I Signal Processing Conference (EUSIPCO), 2016 24th European (s. 1653-1657). IEEE. Proceedings of the European Signal Processing Conference (EUSIPCO) https://doi.org/10.1109/EUSIPCO.2016.7760529