Title :
Interactive fusion in distributed detection: Architecture and performance analysis
Author :
Akofor, Earnest ; Chen, Bing
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY, USA
Abstract :
Within the Neyman-Pearson framework we investigate the effect of feedback in two-sensor tandem fusion networks with conditionally independent observations. While there is noticeable improvement in performance of the fixed sample size Neyman-Pearson (NP) test, it is shown that feedback has no effect on the asymptotic performance characterized by the Kullback-Leibler (KL) distance. The result can be extended to an interactive fusion system where the fusion center and the sensor may undergo multiple steps of interactions.
Keywords :
sensor fusion; statistical testing; KL distance; Kullback-Leibler distance; NP test; Neyman-Pearson framework; Neyman-Pearson test; asymptotic performance; conditionally independent observations; distributed detection; feedback; fusion center; interactive fusion system; two-sensor tandem fusion networks; Awards activities; Bayes methods; Computer architecture; Convergence; Equations; Linear programming; Random variables; Distributed detection; Interactive fusion; Kullback-Leibler distance; Neyman-Pearson test;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
DOI :
10.1109/ICASSP.2013.6638463
