A new computational method for ambiguity assessment of solutions to assignment problems arising in target tracking

Alexander D. Mont; Christopher P. Calderon; Aubrey B. Poore

doi:10.1117/12.2053400

13 June 2014 A new computational method for ambiguity assessment of solutions to assignment problems arising in target tracking

Alexander D. Mont, Christopher P. Calderon, Aubrey B. Poore

Author Affiliations +

Proceedings Volume 9092, Signal and Data Processing of Small Targets 2014; 90920J (2014) https://doi.org/10.1117/12.2053400
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

We present a new approach to estimating the probability of each association in a 2D assignment problem defined by likelihood ratios. Our method divides the set of feasible hypotheses into clusters, and converts a collection of hypotheses into a collection of clusters containing them, reducing the variance of the estimate. Simulations show that our method often generates substantially more accurate probability estimates in less time than traditional methods. Our method can obtain reasonably accurate probabilities of association based on only the input cost matrix and single best candidate solution, eliminating the need for a K-best solution method or MCMC sampling.

Citation Download Citation

Alexander D. Mont, Christopher P. Calderon, and Aubrey B. Poore "A new computational method for ambiguity assessment of solutions to assignment problems arising in target tracking", Proc. SPIE 9092, Signal and Data Processing of Small Targets 2014, 90920J (13 June 2014); https://doi.org/10.1117/12.2053400

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