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Author Kefei Liu, A. ♦ Hing Cheung So, A. ♦ Lei Huang, C.
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Colored Noise ♦ Multidimensional Model Order Selection ♦ 1-d Ester ♦ Nuclear Magnetic Resonance Spectroscopy ♦ Numerous Application ♦ Tensor-based Subspace Approach ♦ Estimation Error ♦ Wide Range ♦ Spatial Dimension Length ♦ State-of-the-art R-d Order Selection Rule ♦ Higher-order Singular Value Decomposition ♦ Low-to-moderate Noise Correlation Level ♦ Weakened Robustness ♦ High Noise Correlation Level ♦ Harmonic Retrieval ♦ Super-resolution Performance ♦ R-d Ester Scheme ♦ R-d Unitary Esprit ♦ Mobile Communication ♦ Prior Knowledge ♦ Second R-d Scheme ♦ Multiple-input Multiple-output Channel Estimation ♦ Colored Noise Environment ♦ R-d Music ♦ R-d Measurement ♦ R-d Hr ♦ Tensor Shift Invariance Equation ♦ Detection Method ♦ R-d Extension
Abstract R-dimensional (R-D) harmonic retrieval (HR) in colored noise, where R≥2, 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.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study