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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Veröffentlicht in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 3895 - 3899
Hauptverfasser: Krueger, Kyle R., McClellan, James H., Scott, Waymond R.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2013
Schlagworte:
Arrays
Dictionaries
Eigenvalues and eigenfunctions
Electromagnetic Induction (EMI)
Low-Rank Approximation
Noise
Optimization
Tensile stress
Vectors
ISSN:1520-6149
Online-Zugang:Volltext
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Zusammenfassung: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.
ISSN:1520-6149
DOI:10.1109/ICASSP.2013.6638388