Advances in displaying uncertain estimates of multiple targets

David Frederic Crouse

doi:10.1117/12.2015147

23 May 2013 Advances in displaying uncertain estimates of multiple targets

David Frederic Crouse

Proceedings Volume 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII; 874504 (2013) https://doi.org/10.1117/12.2015147
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Both maximum likelihood estimation as well as minimum mean optimal subpattern assignment (MMOSPA) estimation have been shown to provide meaningful estimates in instances of target identity uncertainty when the number of targets present is known. Maximum likelihood measurement to track association (2D assignment) has been widely studied and is reviewed in this paper. However, it is widely believed that approximate MMOSPA estimation can not be performed in real time except when considering a very small number of targets. This paper demonstrates the MMOSPA estimator arises as a special case of a minimum mean Wasserstein metric estimator when the number of targets is unknown. Additionally, it is shown that approximate MMOSPA estimates can be calculated in microseconds to miliseconds without extensive optimization, making MMOSPA estimation a practicable alternative to more traditional estimators.

Citation Download Citation

David Frederic Crouse "Advances in displaying uncertain estimates of multiple targets", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 874504 (23 May 2013); https://doi.org/10.1117/12.2015147

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