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US 10,241,191 B2Granted
Multi-sensor target tracking using multiple hypothesis testing
AssigneePrinceton Satellite Systems, Inc.
InventorsMichael A. Paluszek, Joseph Mueller, Stephanie Thomas
Life of this patent
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Abstract
Disclosed a multi-sensor multiple hypotheses testing tracking system. The multi-hypothesis testing system associates measurements from multiple sensors with tracks. Measurements are incorporated using a Kalman Filter and the same filters are used to propagate the trajectories of the tracks.
Claims10
A system for tracking a plurality of moving vehicles that reduces a number of computations required, the system comprising:
- a plurality of sensors that form a plurality of scans by measuring positions of the plurality of moving vehicles over a plurality of time intervals, wherein each of the plurality of scans is a collection of measurements from the plurality of sensors taken in a particular time interval;
- a processor that is communicatively coupled to the plurality of sensors;
- wherein the processor:
- receives, from the plurality of sensors, a first collection of measurements of a first scan measured during a first time interval, wherein each measurement of the first collection of measurements is a single measurement from one of the plurality of sensors,
- generates a first group of tracks using a dynamic state transition model, wherein the first group of tracks includes a track for each of the first collection of measurements;
- forms a first hypothesis for the first group of tracks, wherein the first hypothesis is a set of compatible tracks, and two or more tracks are compatible if each track describes a different target and do not share an identical measurement from any of the plurality of scans;
- removes one or more particular tracks from the first group of tracks that are not included in the first hypothesis to form a first pruned group by assigning a score to each track group;
- predicts using a Kalman filter, expected positions of each of the plurality of moving vehicles based on the first pruned group;
- updates the dynamic state transition model to form an updated dynamic transition model based on the first pruned group,
- receives, from the plurality of sensors, a second collection of measurements from a second scan during a second time interval,
- forms a second hypothesis, based on the updated dynamic transition model and a plurality of covariances calculated for each of the second collection of measurements, and
- tracks the plurality of moving vehicles based on the second hypothesis.
9 more elements
A method for tracking a plurality of moving vehicles that reduces a required number of computations, the method comprising:
- receiving, from a plurality of sensors, a first collection of measurements of a first scan measured during a first time interval, wherein the plurality of sensors form a plurality of scans by measuring positions of the plurality of moving vehicles over a plurality of time intervals, wherein each of the plurality of scans is a collection of measurements from the plurality of sensors taken in a particular time interval;
- generating a first group of tracks for each of the first plurality of measurements using a dynamic state transition model, wherein the first group of tracks includes a track for each of the first collection of measurements;
- forming a first hypothesis for the first group of tracks, wherein the first hypothesis is a set of compatible tracks, and two or more tracks are compatible if each track describes a different target and do not share an identical measurement from any of the plurality of scans;
- removing one or more particular tracks from the first group of tracks that are not included in the first hypothesis to form a first pruned group by assigning a score to each track;
- predicting, using a Kalman filter, expected positions of each of the plurality of moving vehicles based on the first pruned group;
- updating the dynamic state transition model to form an updated dynamic transition model based on the first pruned group;
- receiving, from the plurality of sensors, a second collection of measurements from a second scan during a second time interval;
- forming a second hypothesis based on the updated dynamic transition model and a plurality of covariances calculated for each of the second collection plurality of measurements;
- and
- tracking the plurality of moving vehicles based on the second hypothesis.
7 more elements
Dependencies are inferred from the claim text.