20 March 1998 Quantization for probability-level fusion on a bandwidth budget
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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.
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John V. Black, John V. Black, Mark D. Bedworth, Mark D. Bedworth, } "Quantization for probability-level fusion on a bandwidth budget", Proc. SPIE 3376, Sensor Fusion: Architectures, Algorithms, and Applications II, (20 March 1998); doi: 10.1117/12.303675; https://doi.org/10.1117/12.303675
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