Cost-Function-Based Gaussian Mixture Reduction for Target Tracking

@inproceedings{Williams2003CostFunctionBasedGM,
  title={Cost-Function-Based Gaussian Mixture Reduction for Target Tracking},
  author={Jason L. Williams and Peter S. Maybeck and Peter. Maybeck},
  year={2003}
}
The problem of tracking targets in clutter naturally leads to a Gaussian mixture representation of the probability density function of the target state vector. Stateof-the-art Multiple Hypothesis Tracking (MHT) techniques maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on ad hoc merging and pruning rules to control the growth of hypotheses. This paper proposes a structured cost-functionbased approach to the hypothesis control problem… CONTINUE READING
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