Array interpolation based on multivariate adaptive regression splines
2016 (English)In: 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2016Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2016.
Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop, ISSN 2151-870X
array interpolation, multivariate adaptive regression splines
Communication Systems Signal Processing
IdentifiersURN: urn:nbn:se:hh:diva-32116DOI: 10.1109/SAM.2016.7569704OAI: oai:DiVA.org:hh-32116DiVA: diva2:1010110
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Rio de Janeiro, Brazil, 10-13 July, 2016