Subspace techniques for multidimensional model order selection in colored noise

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• 作者：Kefei Liu^a ; ^{kefeilau@gmail.com} ; Joã ; o Paulo C.L. da Costa^b ; ^{jpdacosta@unb.br} ; Hing Cheung So^a ; ^{hcso@ee.cityu.edu.hk} ; Lei Huang^c ; ^{lhuang@hitsz.edu.cn}
• 关键词：Model order selection ; Estimation error ; Colored noise ; Shift invariance ; Multidimensional harmonic retrieval ; Multilinear algebra
• 刊名：Signal Processing
• 出版年：2013
• 出版时间：July, 2013
• 年：2013
• 卷：93
• 期：7
• 页码：1976-1987
• 全文大小：905 K

文摘

R-dimensional (R-D) harmonic retrieval (HR) in colored noise, where , is required in numerous applications including radar, sonar, mobile communications, multiple-input multiple-output channel estimation and nuclear magnetic resonance spectroscopy. Tensor-based subspace approaches to R-D HR such as R-D unitary ESPRIT and R-D MUSIC provide super-resolution performance. However, they require the prior knowledge of the number of signals. The matrix based (1-D) ESTimation ERror (ESTER) is subspace based detection method that is robust against colored noise. To estimate the number of signals from R-D measurements corrupted by colored noise, we propose two R-D extensions of the 1-D ESTER by means of the higher-order singular value decomposition. The first R-D ESTER combines R shift invariance equations each applied in one dimension. It inherits and enhances the robustness of the 1-D ESTER against colored noise, and outperforms the state-of-the-art R-D order selection rules particularly in strongly correlated colored noise environment. The second R-D scheme is developed based on the tensor shift invariance equation. It performs best over a wide range of low-to-moderate noise correlation levels, but poorly for high noise correlation levels showing a weakened robustness to colored noise. Compared with the existing R-D ESTER scheme, both proposals are able to identify much more signals when the spatial dimension lengths are distinct.