Tensor amplitude extraction in sensor array processing
Sensor array measurements can be inverted to image a region containing targets. The resulting amplitude image is usually interpreted as target strength versus location, but often the imaged amplitude is a function of more parameters than just the location. Sparse target regions can be imaged with di...
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| Vydané v: | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 3895 - 3899 |
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| Hlavní autori: | , , |
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| Jazyk: | English |
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01.05.2013
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| ISSN: | 1520-6149 |
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| Abstract | Sensor array measurements can be inverted to image a region containing targets. The resulting amplitude image is usually interpreted as target strength versus location, but often the imaged amplitude is a function of more parameters than just the location. Sparse target regions can be imaged with dictionary based modeling which relies on enumeration of each parameter with a dense grid. With many parameters, the dictionary becomes too large, which leads to computational complexity issues. This paper shows how additional parameters, such as target orientation and symmetry, can be represented by a tensor matrix instead of a simple amplitude. Furthermore, the tensor can be treated as a continuous variable just like amplitude, which enables extraction of multiple parameters, while reducing the storage requirements of the dictionary, and reducing off-grid modeling error. |
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| AbstractList | Sensor array measurements can be inverted to image a region containing targets. The resulting amplitude image is usually interpreted as target strength versus location, but often the imaged amplitude is a function of more parameters than just the location. Sparse target regions can be imaged with dictionary based modeling which relies on enumeration of each parameter with a dense grid. With many parameters, the dictionary becomes too large, which leads to computational complexity issues. This paper shows how additional parameters, such as target orientation and symmetry, can be represented by a tensor matrix instead of a simple amplitude. Furthermore, the tensor can be treated as a continuous variable just like amplitude, which enables extraction of multiple parameters, while reducing the storage requirements of the dictionary, and reducing off-grid modeling error. |
| Author | Scott, Waymond R. McClellan, James H. Krueger, Kyle R. |
| Author_xml | – sequence: 1 givenname: Kyle R. surname: Krueger fullname: Krueger, Kyle R. organization: Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA – sequence: 2 givenname: James H. surname: McClellan fullname: McClellan, James H. organization: Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA – sequence: 3 givenname: Waymond R. surname: Scott fullname: Scott, Waymond R. organization: Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA |
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| Snippet | Sensor array measurements can be inverted to image a region containing targets. The resulting amplitude image is usually interpreted as target strength versus... |
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| SubjectTerms | Arrays Dictionaries Eigenvalues and eigenfunctions Electromagnetic Induction (EMI) Low-Rank Approximation Noise Optimization Tensile stress Vectors |
| Title | Tensor amplitude extraction in sensor array processing |
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