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Proceedings Paper

Quantization for probability-level fusion on a bandwidth budget
Author(s): John V. Black; Mark D. Bedworth
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Paper Abstract

Results are established for a simulated data fusion architecture featuring a synthetic two-class Gaussian problem, with Bayesian recognizers. The recognizers output posterior probabilities for each class. The probabilities from two or more recognizers of identical error rate are quantized using the nearest-neighbor coding rule. The coded values are decoded at a fusion center and fused. A decision is made from the fused probabilities. The performance of the architecture is examined experimentally using code values that are uniformly distributed and code values that are produced using the Linde-Buzo-Grey (LBG) algorithm. Results are produced for two to six sensors and two to 32 code values. These results are compared to fusing probabilities represented using 32 bit floating-point numbers. Using 32 uniform or LBG-produced code values, produces results that are at most only 1% worse than fusing the uncoded probabilities.

Paper Details

Date Published: 20 March 1998
PDF: 9 pages
Proc. SPIE 3376, Sensor Fusion: Architectures, Algorithms, and Applications II, (20 March 1998); doi: 10.1117/12.303675
Show Author Affiliations
John V. Black, Defence Evaluation and Research Agency Malvern (United Kingdom)
Mark D. Bedworth, Defence Evaluation and Research Agency Malvern (United Kingdom)

Published in SPIE Proceedings Vol. 3376:
Sensor Fusion: Architectures, Algorithms, and Applications II
Belur V. Dasarathy, Editor(s)

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