Abstract
The problem addressed in this chapter is to detect and localize point reflectors or small inclusions embedded in a medium from MSR measurements. We use random matrix theory tools and the results of Chap. 6 to study these problems in the presence of measurement noise. The measurement noise can be modeled by an additive complex Gaussian matrix with zero mean. We consider an SVD based detection test. By the Neyman-Pearson lemma we design the most powerful test for a given false alarm rate and provide the probability of detection of a point reflector hidden or not in noise. Then we build algorithms that estimate the number, the location, and the strength of points reflectors embedded in the medium. Using again the results in Chap. 6 we adopt these algorithms for small inclusion detection and localization.
Keywords
- Measurement Noise
- False Alarm Rate
- Singular Vector
- Powerful Test
- Frobenius Norm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2013 Springer International Publishing Switzerland
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Ammari, H. et al. (2013). Detection and Imaging from MSR Measurements. In: Mathematical and Statistical Methods for Multistatic Imaging. Lecture Notes in Mathematics, vol 2098. Springer, Cham. https://doi.org/10.1007/978-3-319-02585-8_9
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DOI: https://doi.org/10.1007/978-3-319-02585-8_9
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02584-1
Online ISBN: 978-3-319-02585-8
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