KEYWORDS: Sensors, Probability theory, Data fusion, Evolutionary algorithms, Sensor fusion, Information fusion, Algorithms, Sensor performance, Fermium, Frequency modulation
In this paper it is illustrated how Bayes equations and frequency data may use as a measure of performance for belief fusion algorithms. A review of Bayes equations for single and multiple sources is provided. A simple performance measure is then calculated and applied to some belief fusion examples from the literature. Their performance measures are qualitatively similar, but the quantitative differences among these techniques appear to be arbitrary.
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