@inproceedings{d33caae4548e47d68dea3e8ccf32b27d,
title = "Chirp parameter estimation from tensor decomposition",
abstract = "The non-stationary properties of chirp signals restrict the application of the parameter estimation algorithms of single-frequency signals in the scene of chirp signals, simultaneously the traditional chirp parameter estimation algorithms also have limitations, to overcome some of the limitations this paper proposes a new algorithm which can estimate chirp parameter from tensor decomposition. The new algorithm uses the received discrete data aligning according to a certain form to build multidimensional data structures and then applies the tensor decomposition in chip parameter estimation using the shift invariance of subspace, the algorithm provides a new way of thinking in the field of chirp parameter estimation and it can apply in the scene of multiple chirp signals, the simulations prove the effectiveness of the algorithm.",
keywords = "Chirp, Parameter estimation, Tensor decomposition",
author = "Mingyuan Ge and Guohua Wei and Xinpeng Zhou",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 12th IEEE International Conference on Signal Processing, ICSP 2014 ; Conference date: 19-10-2014 Through 23-10-2014",
year = "2014",
doi = "10.1109/ICOSP.2014.7015356",
language = "English",
series = "International Conference on Signal Processing Proceedings, ICSP",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "October",
pages = "2057--2062",
editor = "Baozong Yuan and Qiuqi Ruan and Xiaofang Tang",
booktitle = "ICSP 2014 - 2014 IEEE 12th International Conference on Signal Processing, Proceedings",
address = "United States",
edition = "October",
}