An adaptive two-decision sequential target trajectory detection algorithm using the decision statistics of plots
DOI:
https://doi.org/10.20535/RADAP.2014.59.66-75Keywords:
target track detection, unknown SNR, Wald test, decision statistic, likelihood functionAbstract
Introduction. The synthesis of an adaptive target trajectory detection algorithm having the opportunity along with the decision of the main tasks to perform SNR estimation is of great practical importance. For this simple complement sequential test is applied.Problem statement. The problem of the adaptive target trajectory detection can be formulated as a problem of a simple hypothesis sequential testing about the target track absence against the composite alternative with SNR estimating by using a minimum mean square error estimator.
Development of an adaptive two-decision sequential target trajectory detection algorithm. In this section expressions for calculating the likelihood ratios and the SNR estimate are obtained.
Effectiveness Analysis of Algorithm. Analysis of the adaptive algorithm is carried out as an example of target trajectory detection due to surveillance radar which measures range and range rate using the statistical modeling. The decision statistics related to noise- and target-originate measurements are described by an exponential distribution and Swerling 1 distribution respectively. With the purpose of comparative analysis, effectiveness analysis of non-adaptive sequential target trajectory detection algorithm based on the Wald test is also carried out.
Conclusions. The developed adaptive algorithm reduces the decision time, as the decision in favor of the complex hypothesis of the target existence is taken without specifying simple hypothesis which having place. Average time true detection of false track when is reduced by 1.7 times compared to the non-adaptive two-decision algorithm.
References
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