Abstract
Two preprocessing approaches, dedicated to the tracking of low SNR objects, are compared—the variance and studentized range. Both approaches are applied using sliding window for noised signal for improving the detection of weak object. Influences of window size are compared and studentized range shows improvement over variance. Both approaches are compared using the Monte Carlo test with numerous tracking scenarios using ST–TBD (Spatio–Temporal Track–Before–Detect) algorithm. The results shows better performance of the studentized range for the detection and tracking weak point objects .
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© 2016 Springer International Publishing Switzerland
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Mazurek, P. (2016). Studentized Range for Spatio–Temporal Track–Before–Detect Algorithm. In: Choraś, R. (eds) Image Processing and Communications Challenges 7. Advances in Intelligent Systems and Computing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-319-23814-2_20
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DOI: https://doi.org/10.1007/978-3-319-23814-2_20
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