Share Email Print
cover

Proceedings Paper

Application of online measures to monitor and evaluate multiplatform fusion performance
Author(s): Stephen C. Stubberud; Charlene Kowalski; Dale M. Klamer
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

A primary concern of multiplatform data fusion is assessing the quality and utility of data shared among platforms. Constraints such as platform and sensor capability and task load necessitate development of an on-line system that computes a metric to determine which other platform can provide the best data for processing. To determine data quality, we are implementing an approach based on entropy coupled with intelligent agents. To determine data quality, we are implementing an approach based on entropy coupled with intelligent agents. Entropy measures quality of processed information such as localization, classification, and ambiguity in measurement-to-track association. Lower entropy scores imply less uncertainty about a particular target. When new information is provided, we compuete the level of improvement a particular track obtains from one measurement to another. The measure permits us to evaluate the utility of the new information. We couple entropy with intelligent agents that provide two main data gathering functions: estimation of another platform's performance and evaluation of the new measurement data's quality. Both functions result from the entropy metric. The intelligent agent on a platform makes an estimate of another platform's measurement and provides it to its own fusion system, which can then incorporate it, for a particular target. A resulting entropy measure is then calculated and returned to its own agent. From this metric, the agent determines a perceived value of the offboard platform's measurement. If the value is satisfactory, the agent requests the measurement from the other platform, usually by interacting with the other platform's agent. Once the actual measurement is received, again entropy is computed and the agent assesses its estimation process and refines it accordingly.

Paper Details

Date Published: 27 July 1999
PDF: 8 pages
Proc. SPIE 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII, (27 July 1999); doi: 10.1117/12.357170
Show Author Affiliations
Stephen C. Stubberud, ORINCON Corp. (United States)
Charlene Kowalski, ORINCON Corp. (United States)
Dale M. Klamer, ORINCON Corp. (United States)


Published in SPIE Proceedings Vol. 3720:
Signal Processing, Sensor Fusion, and Target Recognition VIII
Ivan Kadar, Editor(s)

© SPIE. Terms of Use
Back to Top