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2019 | OriginalPaper | Chapter

Monte Carlo Analysis of Local Cross–Correlation ST–TBD Algorithm

Authors : Przemyslaw Mazurek, Robert Krupinski

Published in: Computational Science – ICCS 2019

Publisher: Springer International Publishing

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Abstract

The Track–Before–Detect (TBD) algorithms allow the estimation of the state of an object, even if the signal is hidden in the background noise. The application of local cross–correlation for the modified Information Update formula improves this estimation for extended objects (tens of cells in the measurement space) compared to the direct application of the Spatio–Temporal TBD (ST–TBD) algorithm. The Monte Carlo test was applied to evaluate algorithms by using a variable standard deviation of the additive Gaussian noise. The proposed solution does not require prior knowledge of the size or measured values of the object. Mean Absolute Error for the proposed algorithm is much lower, close to zero to about 0.8 standard deviation, which is not achieved for the ST–TBD.

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Title: Monte Carlo Analysis of Local Cross–Correlation ST–TBD Algorithm
Authors: Przemyslaw Mazurek
Robert Krupinski
Publisher: Springer International Publishing
Book: Computational Science – ICCS 2019
Print ISBN: 978-3-030-22733-3

Electronic ISBN: 978-3-030-22734-0

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-22734-0_5

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