Tracking and data association
Elements of information theory
Elements of information theory
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A gaussian sum approach to the multi-target identification-tracking problem
Automatica (Journal of IFAC)
Derivation and evaluation of improved tracking filter for use in dense multitarget environments
IEEE Transactions on Information Theory
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The problem of tracking targets in clutter naturally leads to a Gaussian mixture representation of the probability density function of the target state vector. Modern tracking methods maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on simple merging and pruning rules to control the growth of hypotheses. This paper proposes a structured, cost-function-based approach to the hypothesis control problem, utilizing the Integral Square Error (ISE) cost measure. A comparison of track life performance versus computational cost is made between the ISE-based filter and previously proposed approximations including simple pruning, Singer's n-scan memory filter, Salmond's joining filter, and Chen and Liu's Mixture Kalman Filter (MKF). The results demonstrate that the ISE-based mixture reduction algorithm provides mean track life which is significantly greater than that of the compared techniques using similar numbers of mixture components, and mean track life competitive with that of the compared algorithms for similar mean computation times.