UPDATING INTENSITIES IN A PHD FILTER BASED ON A SENSOR TRACK ID
First Claim
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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Accused Products
Abstract
In one embodiment, a method of tracking multiple objects with a probabilistic hypothesis density filter is provided. The method includes obtaining 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 measurements. A Tk+1 first predicted intensity is generated for the first object based on a Tk first track intensity. A Tk+1 measurement from a first sensor of the at least one sensors is obtained, the first sensor providing a second track ID for the Tk+1 measurement. The second track ID is compared to the one or more first track IDs, and the Tk+1 first predicted intensity is selectively updated 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.
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Citations
20 Claims
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1. A method of tracking multiple objects with a probabilistic hypothesis density filter, the method comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A tracking system comprising:
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one or more processing devices; and one or more data storage devices including instructions which, when executed by the one or more processing devices, cause the one or more processing devices to track multiple objects with a probabilistic hypothesis density filter, wherein the instructions cause the one or more processing devices to; obtain 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; generate 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; generate 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; obtain 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; compare the second track ID to the one or more first track IDs; and selectively update 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. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A computer readable medium including instructions which, when executed by one or more processing devices, cause the one or more processing devices to:
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obtain 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; generate 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; generate 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; obtain 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; compare the second track ID to the one or more first track IDs; and selectively update 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. - View Dependent Claims (18, 19, 20)
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Specification