Poisson Point Processes: Imaging, Tracking, and Sensing
METRON INC RESTON VA
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Multitarget tracking intensity filters are closely related to imaging problems, especially PET imaging. The intensity filter is obtained by three different methods. One is a Bayesian derivation involving target prediction and information updating. The second approach is a simple, compelling, and insightful intuitive argument. The third is a straightforward application of the Shepp-Vardi algorithm. The intensity filter is developed on an augmented target state space. The PHD filter is obtained from the intensity filter by substituting assumed known target birth and measurement clutter intensities for the intensity filters predicted target birth and clutter intensities. To accommodate heterogeneous targets and sensor measurement models, a parameterized intensity filter is developed using a marked PPP Gaussian sum model. Particle and Gaussian sum implementations of intensity filters are reviewed. Mean-shift algorithms are discussed as a way to extract target state estimates. Grenanders method of sieves is discussed for regularization of the multitarget intensity filter estimates. Sources of error in the estimated target count are discussed. Finally, the multisensor intensity filter is developed using the same PPP target models as in the single sensor filter. Both homogeneous and heterogeneous multisensor fields are discussed. Multisensor intensity filters reduce the variance of estimated target count by averaging.
- Miscellaneous Detection and Detectors