The problem of associating data in a domain with noisy sensor inputs is of considerable importance in a wide variety of problem areas. Data association algorithms provide an approach for automatically correlating and combining incoming sensor data. A number of association algorithms have been developed; however, evaluating the effectiveness of these algorithms is difficult because traditional evaluation methods fail to provide meaningful meansures of relative merit. These traditional measures are troublesome because the type I and type II errors upon which they are based lose all meaning after reports are combined in a data base. This paper describes a test bed which uses an alternative approach for measuring the performance of association algorithms. Like the traditional measures, the approach described here requires the use of simulated sensor data. The evaluation procedure is based on a measure of the distance between a baseline representation and the representation produced by the association algorithm at some time instant. Two choices for this baseline representation are listed and scores are defmed between these baselines and an algorithm's representation. A description of the test bed architecture which implements this evaluation procedure is provided, as well as, sample outputs from performing algorithm evaluations in the test bed.

Date Published: 1 October 1990
PDF: 11 pages
Proc. SPIE 1306, Sensor Fusion III, (1 October 1990); doi: 10.1117/12.21630

Published in SPIE Proceedings Vol. 1306:
Sensor Fusion III
Robert C. Harney, Editor(s)