Distributed decision fusion under unknown distributions

Nageswara S. V. Rao; S. Sitharama Iyengar

doi:10.1117/1.600635

1 March 1996 Distributed decision fusion under unknown distributions

Nageswara S. V. Rao, S. Sitharama Iyengar

Optical Engineering, Vol. 35, Issue 3, (March 1996). https://doi.org/10.1117/1.600635

The problem of distributed decision fusion is studied in the case when the probability distributions of the individual detectors are not available. The detector system is available so that a training sample can be generated by sensing objects with known parameters or classification. Earlier solutions to this problem required some knowledge of the error distributions of the detectors, for example, either in a parametric form or in a closed analytical form. Here we present three methods that, given a sufficiently large training sample, yield an approximation to the optimal fusion rule with an arbitrary level of confidence. These methods are based on (i) empirical estimation, (ii) approximate decision rule, and (iii) nearest-neighbor rule. We show that a nearest-neighbor rule provides a computationally viable solution, which approximates a neural network-based one while ensuring fast computation.

Citation Download Citation

Nageswara S. V. Rao and S. Sitharama Iyengar "Distributed decision fusion under unknown distributions," Optical Engineering 35(3), (1 March 1996). https://doi.org/10.1117/1.600635

Published: 1 March 1996

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