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Particle filter tracking, a type of sequential Monte Carlo method, has long been considered to be a
very promising but time-consuming tracking technique. Methods have been developed to include a particle
filter as part of a Variable Structure, Interactive Multiple Model (VS-IMM) structure and to integrate it into
the Multiple Hypothesis Tracker (MHT) scoring structure. By integrating a particle filter as just one of
many filters in Raytheon's MHT, the particle filter is applied sparingly on difficult off-road targets. This
dramatically reduces the computation time as well as improves tracking performance in circumstances in
which the other filters do not excel. Moreover, terrain information may be taken into account in the
particle propagation process. In particular, an Unscented Particle Filter (UPF) was implemented in order
to address the potential dominance of a small set of degenerate particles and/or poor prior distribution
sampling from hampering the ability of the particle filter to accurately handle a maneuver.
The Unscented Particle Filter treats every particle as its own Kalman filter. After the distribution
of particles is adjusted in order to take into account the terrain, each particle is divided into sigma point
states. These sigma points are propagated forward in time and then recombined to form a new composite
particle state and covariance. These reformed particles are used in scoring and can be updated with a new
observation. Since the Unscented Particle Filter includes the covariances in these calculations, this particle
filter approach is more accurate and potentially requires fewer particles than an ordinary particle filter. By
adding an Unscented Particle Filter to the other filters in an MHT tracker, the advantages of the UPF can be
utilized in an efficient manner in order to enhance tracking performance.
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