A sparse sampling planner for sensor resource management

Matthew Rudary; Deepak Khosla; James Guillochon; P. Alex Dow; Barbara Blyth

doi:10.1117/12.666255

17 May 2006 A sparse sampling planner for sensor resource management

Matthew Rudary, Deepak Khosla, James Guillochon, P. Alex Dow, Barbara Blyth

Proceedings Volume 6235, Signal Processing, Sensor Fusion, and Target Recognition XV; 62350A (2006) https://doi.org/10.1117/12.666255
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

The goal of sensor resource management (SRM) is to allocate resources appropriately in order to gain as much information as possible about a system. We introduce a centralized non-myopic planning algorithm, C-SPLAN, that uses sparse sampling to estimate the value of resource assignments. Sparse sampling is related to Monte Carlo simulation. In the SRM problem we consider, our network of sensors observes a set of tracks; each sensor can be set to operate in one of several modes and/or viewing geometries. Each mode incurs a different cost and provides different information about the tracks. Each track has a kinematic state and is of a certain class; the sensors can observe either or both of these, depending on their mode of operation. The goal in this problem is to maximize the overall rate of information gain, i.e. rate of improvement in kinematic tracking and classification accuracy of all tracks in the Area of Interest. The rate is defined by several metrics with the cost of the sensor mode being the primary factor. We compare C-SPLAN's performance on several tracking and target identification problems to that of other algorithms.

Citation Download Citation

Matthew Rudary, Deepak Khosla, James Guillochon, P. Alex Dow, and Barbara Blyth "A sparse sampling planner for sensor resource management", Proc. SPIE 6235, Signal Processing, Sensor Fusion, and Target Recognition XV, 62350A (17 May 2006); https://doi.org/10.1117/12.666255

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