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Array interpolation based on multivariate adaptive regression splines

: Marques Marinho, Marco Antonio; Carvalho Lustosa da Costa, Joao Paulo; Antreich, Felix; Lima Ferrer de Almeida, Andre; Galdo, Giovanni del; Pignaton de Freitas, Edison; Vinel, Alexey


Institute of Electrical and Electronics Engineers -IEEE-:
Ninth IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2016. Proceedings : 10-13 July 2016, Rio de Janeiro, Brazil
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-5090-2104-8
ISBN: 978-1-5090-2103-1
Sensor Array and Multichannel Signal Processing Workshop (SAM) <9, 2016, Rio de Janeiro>
Conference Paper
Fraunhofer IIS ()
drahtloses Kommunikationssystem

Array processing is an important topic in the signal processing field. Many important signal processing techniques such as Spatial Smoothing, Forward Backward Averaging and Root-MUSIC, rely on antenna arrays with specific and precise structures. Arrays with such ideal structures, such as a centro-hermitian structure, are often hard to build in practice. Array interpolation is used to enable the usage of these techniques with imperfect (not having a centro-hermitian structure) arrays. Most interpolation methods rely on methods based on least squares (LS) to map the output of a perfect virtual array based on the real array. In this work, the usage of Multivariate Adaptive Regression Splines (MARS) is proposed instead of the traditional LS to interpolate arrays with responses largely different from the ideal using non-linear mapping functions.