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The histogram-probabilistic multi-hypothesis tracker (H-PMHT) is an efficient multi-target tracking approach to the track-before-detect problem. A fundamental feature of the H-PMHT is the discretisation of the energy in the sensor data and the assumption of a multinomial measurement model on the resulting image. A problem with the H-PMHT is that the multinomial measurement model fails to account for...
The Histogram-Probabilistic Multi-Hypothesis Tracker (H-PMHT) is an efficient multi-target tracking approach to the Track-Before-Detect (TkBD) problem. However, it cannot adequately deal with fluctuating targets and this can degrade track management performance. By assuming an alternative measurement model based on a Poisson distribution, the H-PMHT algorithm can be re-derived to incorporate a time-correlated...
The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is an efficient multi-target approach to the Track-before-detect (TkBD) problem. The tracking is based on the generation of a synthetic histogram by quantising the energy in the sensor data. The resultant quantised measurement is then modelled using a multinomial distribution and target state estimation is performed via Expectation-Maximisation...
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