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Proceedings Paper

Robust fusion with reliabilities weights
Author(s): Jean-Francois Grandin; Miguel Marques
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Paper Abstract

The reliability is a value of the degree of trust in a given measurement. We analyze and compare: ML (Classical Maximum Likelihood), MLE (Maximum Likelihood weighted by Entropy), MLR (Maximum Likelihood weighted by Reliability), MLRE (Maximum Likelihood weighted by Reliability and Entropy), DS (Credibility Plausibility), DSR (DS weighted by reliabilities). The analysis is based on a model of a dynamical fusion process. It is composed of three sensors, which have each it's own discriminatory capacity, reliability rate, unknown bias and measurement noise. The knowledge of uncertainties is also severely corrupted, in order to analyze the robustness of the different fusion operators. Two sensor models are used: the first type of sensor is able to estimate the probability of each elementary hypothesis (probabilistic masses), the second type of sensor delivers masses on union of elementary hypotheses (DS masses). In the second case probabilistic reasoning leads to sharing the mass abusively between elementary hypotheses. Compared to the classical ML or DS which achieves just 50% of correct classification in some experiments, DSR, MLE, MLR and MLRE reveals very good performances on all experiments (more than 80% of correct classification rate). The experiment was performed with large variations of the reliability coefficients for each sensor (from 0 to 1), and with large variations on the knowledge of these coefficients (from 0 0.8). All four operators reveal good robustness, but the MLR reveals to be uniformly dominant on all the experiments in the Bayesian case and achieves the best mean performance under incomplete a priori information.

Paper Details

Date Published: 6 March 2002
PDF: 12 pages
Proc. SPIE 4731, Sensor Fusion: Architectures, Algorithms, and Applications VI, (6 March 2002); doi: 10.1117/12.458382
Show Author Affiliations
Jean-Francois Grandin, Thales Group (France)
Miguel Marques, Thales Group (France)

Published in SPIE Proceedings Vol. 4731:
Sensor Fusion: Architectures, Algorithms, and Applications VI
Belur V. Dasarathy, Editor(s)

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