Han X. Vu

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The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is a parametric mixture-fitting approach to track-before-detect. The original implementations of H-PMHT dealt with Gaussian shaped targets with fixed or known extent. More recent applications have addressed other special cases of the target shape. This article reviews these recent extensions and(More)
The Histogram PMHT is a parametric track-before-detect method that has good detection performance and low computation complexity. However, the method assumes a known clutter distribution. This paper introduces a method for learning a non-uniform clutter map where the map is represented as a mixture of parameterised components. The modified Histogram PMHT is(More)
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(More)
1 Abstract Global hybrid (electron fluid, kinetic ions) and fully kinetic simulations of the magnetosphere have been used to show surprising interconnection between shocks, turbulence and magnetic re-connection. In particular collisionless shocks with their reflected ions that can get upstream before retransmission can generate previously unforeseen(More)
Conventional active sonar processing systems typically reduce the sensor data from an intensity map to a point-measurement form via a detection thresholding process. This approach is often sufficient for detecting and tracking high signal-to-noise-ratio (SNR) targets but becomes more challenging for low SNR targets. Track-Before-Detect (TkBD) is an(More)
The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is a parametric mixture-fitting approach to the Track-before-detect (TkBD) problem. It has been shown to give performance close to numerical approximations of the optimal Bayesian filter at a fraction of the computation cost. This paper will consider an implementation of the H-PMHT for non-linear(More)
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