Abstract
In seismic, radar, and sonar imaging the exact determination of the reflectivity distribution is usually intractable so that approximations have to be applied. A method called synthetic aperture focusing technique (SAFT) is typically used for such applications as it provides a fast and simple method to reconstruct (3D) images. Nevertheless, this approach has several drawbacks such as causing image artifacts as well as offering no possibility to model system-specific uncertainties. In this paper, a statistical approach is derived, which models the region of interest as a probability density function (PDF) representing spatial reflectivity occurrences. To process the nonlinear measurements, the exact PDF is approximated by well-placed Extended Kalman Filters allowing for efficient and robust data processing. The performance of the proposed method is demonstrated for a 3D ultrasound computer tomograph and comparisons are carried out with the SAFT image reconstruction.
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Schwarzenberg, G.F., Mayer, U., Ruiter, N.V., Hanebeck, U.D. (2009). 3D Reflectivity Reconstruction by Means of Spatially Distributed Kalman Filters. In: Hahn, H., Ko, H., Lee, S. (eds) Multisensor Fusion and Integration for Intelligent Systems. Lecture Notes in Electrical Engineering, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89859-7_28
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DOI: https://doi.org/10.1007/978-3-540-89859-7_28
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