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Particle Filtering for Tracking

This article discusses the concept of particle filtering for tracking in nonlinear and non-Gaussian problems. It includes demonstrations on tracking in clutter, tracking with constraints, tracking dim targets, and mutual triangulation. The article concludes with the potential benefits and applications of particle filtering in improving tracking performance.

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Particle Filtering for Tracking

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  1. Particles for Tracking Simon Maskell 2 December 2002

  2. Contents • Particle filtering (on an intuitive level) • Nonlinear non-Gaussian problems • Some Demos • Tracking in clutter • Tracking with constraints • Tracking dim targets • Mutual triangulation • Conclusions

  3. Particle Filter • Kalman filter is optimal if and only if • dynamic model is linear Gaussian • measurement model is linear Gaussian • Extended Kalman filter (EKF) approximates models • Ok, if models almost linear Gaussian in locality of target • Hence large EKF based tracking literature • Particle filter approximates pdf explicitly as a sample set • Better, if EKF’s approximation loses lots of information

  4. Particle Filter • Consider • A nonlinear function • Two candidate distributions • Different diversity of hypotheses • Different part of function

  5. Particle Filter • Look at variation in gradient of tangent across hypotheses • Determined by diversity of hypotheses and curvature • Bearings only tracking • Nonlinearity pronounced since range typically uncertain

  6. Particle Filter • An Extended Kalman Filter infers states from measurements • Restricts the models to be of a given form • A particle filter generates a number of hypotheses • Predicts particles forwards • Hypotheses appear to use dynamics and measurements • Importance sampling • Choice of importance density is VERY VERY important

  7. Particle Filter • Offers the potential to capitalise on models • Approximating models can lose information • Lost information can be critical to performance • Solution structure can mirror problem structure • Specific examples of potential to improve performance • May not need to explore a deep history of associations • Using difficult information • Doppler Blind Zones / Terrain Masking • Out-of-sequence measurements • Stealthy Targets

  8. Some Demos • Tracking in clutter • Heavy tailed likelihood • Tracking with constraints • Obscuration can be informative • Tracking dim targets • Correlate images through time • Mutual triangulation • Bearing of sensors and sensors’ bearings of target

  9. Conclusions • Particle Filtering can offer significant gains • Can capitalise on model fidelity • Can mirror problem structure • Questions?

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