@inproceedings{611a11f92e6a4ce09e242daeaaf0b8e7,
title = "Nuclear norm minimization for blind subspace identification (N2BSID)",
abstract = "In many practical applications of system identification, it is not feasible to measure both the inputs applied to the system as well as the output. In such situations, it is desirable to estimate both the inputs and the dynamics of the system simultaneously; this is known as the blind identification problem. In this paper, we provide a novel extension of subspace methods to the blind identification of multiple-input multiple-output linear systems. We assume that our inputs lie in a known subspace, and we are able to formulate the identification problem as rank constrained optimization, which admits a convex relaxation. We show the efficacy of this formulation with a numerical example.",
author = "Dexter Scobee and Lillian Ratliff and Roy Dong and Henrik Ohlsson and Michel Verhaegen and Sastry, {S. Shankar}",
year = "2015",
month = feb,
day = "8",
doi = "10.1109/CDC.2015.7402521",
language = "English (US)",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2127--2132",
booktitle = "54rd IEEE Conference on Decision and Control,CDC 2015",
address = "United States",
note = "54th IEEE Conference on Decision and Control, CDC 2015 ; Conference date: 15-12-2015 Through 18-12-2015",
}