Lagrangian relaxation approaches to closed loop scheduling of track updates

Kruger A. B. White; Jason L. Williams

doi:10.1117/12.919552

15 May 2012 Lagrangian relaxation approaches to closed loop scheduling of track updates

Kruger A. B. White, Jason L. Williams

Proceedings Volume 8393, Signal and Data Processing of Small Targets 2012; 83930C (2012) https://doi.org/10.1117/12.919552
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Many modern agile sensor systems are capable of being adaptively tasked in response to an evolving environment. This paper describes an algorithm developed in the framework of previous work by Casta~non, Wintenby and Krishnamurthy. The goal is to schedule the time and dwell time for updates of targets under track using a phased array radar. This problem is addressed using Lagrangian relaxation, decoupling the joint optimisation into a series of single target problems. After discretising the single target decision state (i.e., the covariance matrix), these single target problems are solved as Markov decision processes. An example of a method for selecting the state space discretisation is outlined and the results used to generate a closed loop schedule for a set of track states.

Citation Download Citation

Kruger A. B. White and Jason L. Williams "Lagrangian relaxation approaches to closed loop scheduling of track updates", Proc. SPIE 8393, Signal and Data Processing of Small Targets 2012, 83930C (15 May 2012); https://doi.org/10.1117/12.919552

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