Tensor decomposition based R-dimensional matrix pencil method
In this paper, we extend the standard matrix pencil (MP) method to R-dimensional (R-D) tensor based MP. Higher-order singular value decomposition (HOSVD) is used to obtain the signal subspace. Performance of tensor based MP method is evaluated by computer simulations. Comparing with the conventional...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/79906 http://hdl.handle.net/10220/8768 http://ieeexplore.ieee.org/xpl/articleDetails.jsp;jsessionid=Q8SWQQXL4TNfbzZgpD25Wg8ky21Jw2VHhMJ1hhJgcb5D2pJP1X2y!-1489032362?arnumber=6290509&contentType=Conference+Publications |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | In this paper, we extend the standard matrix pencil (MP) method to R-dimensional (R-D) tensor based MP. Higher-order singular value decomposition (HOSVD) is used to obtain the signal subspace. Performance of tensor based MP method is evaluated by computer simulations. Comparing with the conventional matrix based MP methods, better performance is obtained for tensor based R-D MP methods by exploiting the structure of the measurement data. Furthermore, it is straightforward to extend the proposed R-D tensor MP to other MP type methods, such as R-D unitary tensor MP, R-D beamspace tensor MP. |
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