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UPDATING INTENSITIES IN A PHD FILTER BASED ON A SENSOR TRACK ID

  • US 20160033281A1
  • Filed: 07/31/2014
  • Published: 02/04/2016
  • Est. Priority Date: 07/31/2014
  • Status: Active Grant
First Claim
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1. A method of tracking multiple objects with a probabilistic hypothesis density filter, the method comprising:

  • obtaining a plurality of measurements corresponding to a first object with at least one sensor, the at least one sensor providing one or more first track IDs for the plurality of measurements;

    generating a Tk first track intensity for the first object based on the plurality of measurements, the Tk first track intensity including a weight, a state mean vector, and a state covariance matrix of statistics of a track of the first object at time Tk;

    generating a Tk+1 first predicted intensity for the first object based on the Tk first track intensity, the Tk+1 first predicted intensity corresponding to time Tk+1;

    obtaining a Tk+1 measurement from a first sensor of the at least one sensors, the first sensor providing a second track ID for the Tk+1 measurement, wherein the Tk+1 measurement corresponds to time Tk+1;

    comparing the second track ID to the one or more first track IDs; and

    selectively updating the Tk+1 first predicted intensity with the Tk+1 measurement based on whether the second track ID matches any of the one or more first track IDs to generate a Tk+1 first measurement-to-track intensity for the first object at time Tk+1.

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