K-near optimal solutions to improve data association in multiframe processing

Aubrey B. Poore; Xin Yan

doi:10.1117/12.364041

4 October 1999 K-near optimal solutions to improve data association in multiframe processing

Aubrey B. Poore, Xin Yan

Proceedings Volume 3809, Signal and Data Processing of Small Targets 1999; (1999) https://doi.org/10.1117/12.364041
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

Abstract

The problem of data association remains central in multitarget, multisensor, and multiplatform tracking. Lagrangian relaxation methods have been shown to yield near optimal answers in real-time. The necessity of improvement in the quality of these solutions warrants a continuing interest in these methods. A partial branch-and-bound technique along with adequate branching and ordering rules are developed. Lagrangian relaxation is used as a branching method and as a method to calculate the lower bound for subproblems. The result shows that the branch-and-bound framework greatly improves the solutions in less time than relaxation alone.

Citation Download Citation

Aubrey B. Poore and Xin Yan "K-near optimal solutions to improve data association in multiframe processing", Proc. SPIE 3809, Signal and Data Processing of Small Targets 1999, (4 October 1999); https://doi.org/10.1117/12.364041

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available