Any implementation of a real-time tracking system is subject to capacity constraints in terms of how many
measurements or tracks that can be processed per unit time. This paper addresses the problem of selecting which
measurements or tracks should be discarded to maximize the expected number of targets of interest being both tracked
and correctly associated with remote datasets. In particular, the problem is addressed when only a single dataset is
available.
Bias introduced due to noisy point estimates being propagated through deterministic nonlinear mappings is a reoccurring
problem in high-fidelity tracking and classification systems. This paper proves that it is a misconception that such bias
is reduced when computing the expected value of the nonlinear output that follows when treating the input as a random
vector with expectation equal to the provided estimate. Instead, this doubles the bias. An approximately unbiased
estimator and an estimate of its covariance matrix are provided. The estimator can be calculated also in the case where
the Hessian matrices associated with the nonlinear mapping are unavailable.
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