Bayesian closely spaced object resolution technique

Walter E. Lillo; Nielson Wade Schulenburg

doi:10.1117/12.179051

6 July 1994 Bayesian closely spaced object resolution technique

Walter E. Lillo, Nielson Wade Schulenburg

Proceedings Volume 2235, Signal and Data Processing of Small Targets 1994; (1994) https://doi.org/10.1117/12.179051
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

A technique is described for recovering positional and radiometric information on unresolved objects that are so closely spaced that their individual blur functions overlap. Emphasis is on point sources. A Bayesian method has been formulated and applied with real data to resolving `clumps' of stars. The method is able to provide error bars in the individual pulse positions and amplitudes from a single data set rather than from the deviations observed after measuring many independent sets of data. The Bayesian technique is advantageous for estimating the number of discrete objects in a given clump using the rules of probability theory without the need for contrived penalty factors. By the way it formulates the model, the Bayesian approach naturally includes a factor which reflects the reduction in the number of degrees of freedom for a model with a greater number of sources. As a result, the algorithm is able to compute the highest probability for the model with the correct number of sources in the clump even though a model with more sources has smaller residuals. The technique is applied to real visible CCD data of observations of star clusters NGC 6819 and is shown to be internally consistent in counting.

Citation Download Citation

Walter E. Lillo and Nielson Wade Schulenburg "Bayesian closely spaced object resolution technique", Proc. SPIE 2235, Signal and Data Processing of Small Targets 1994, (6 July 1994); https://doi.org/10.1117/12.179051

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