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From the radar detections or plots it is convinient and necessary to filter the target trajectory. Otherwise, the statistical variations, e.g., due to target RCS fluctuations, will make it more difficult to estimate the tracked object's course, speed and closest point of approach.
Tracking filters that commonly are used are different kind of Kalman filters (KF), e.g., LOS KF , Extended KF, Unscented KF, Interacting Multiple Models or Gaussian Mixture, and so on. Another filter that is very useful in dense target situations, background estimation, and non-linear target motions are versions of PHD (or Random Sets) filtering, e.g., CPHD, GPHD, FISST, and Sequential monte carlo PHD. However, the most powerful and robust filtering that also provides an association framwork, according to us, is the Multi Hypothesis Tracking (MHT). Note that for all the other filtering techniques presented here we need to add an assoication framework (e.g., Global Nearest Neighbor, Auction, PDAF, and so on)
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