Handbook of Multisensor Data Fusion, 2nd Edition

4

 

 

Introduction to the Algorithmics of Data Association in Multiple-Target Tracking

 

Jeffrey K. Uhlmann

CONTENTS

4.1   Introduction

4.1.1   Keeping Track

4.1.2   Nearest Neighbors

4.1.3   Track Splitting and Multiple Hypotheses

4.1.4   Gating

4.1.5   Binary Search and kd-Trees

4.2   Ternary Trees

4.3   Priority kd-Trees

4.3.1   Applying the Results

4.4   Conclusion

Acknowledgment

References

 

 

4.1   Introduction

When a major-league outfielder runs down a long fly ball, the tracking of a moving object looks easy. Over a distance of a few hundred feet, the fielder calculates the ball’s trajectory to within an inch or two and times its fall to within milliseconds. But what if an outfielder was asked to track 100 fly balls at once? Even 100 ...