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- W1569597150 abstract "The maximum likelihood probabilistic data association (ML-PDA) algorithm, developed for very low observable (VLO) target tracking, will always provide a candidate track that must then be either validated or rejected. By comparing the value of the log likelihood ratio (LLR) at the parameter estimate to a threshold value, track validation is accomplished. Using extreme value theory, we show that in the absence of a target the LLR global maximum obeys approximately a Gumbel distribution and not the Gaussian distribution previously ascribed to it in the literature. It is shown that the Gaussian approximation yields inaccurate false track acceptance probabilities. Using a Gumbel distribution, the probability of false track rejection can be obtained for a given threshold value. The probability of true track detection is obtained assuming the LLR global maximum obeys approximately a Gaussian distribution in the presence of a target. A system operating characteristic (SOC) is developed that unifies the detection processing and track algorithm performance into a single performance metric. The performance of this test is demonstrated through simulations" @default.
- W1569597150 created "2016-06-24" @default.
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- W1569597150 date "2006-08-02" @default.
- W1569597150 modified "2023-10-04" @default.
- W1569597150 title "ML-PDA Track Validation Thresholds" @default.
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- W1569597150 doi "https://doi.org/10.1109/aero.2006.1655915" @default.
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