Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Scalable multilevel quantization for distributed detection
 Institute of Electrical and Electronics Engineers IEEE; IEEE Signal Processing Society: ICASSP 2021, IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings : June 611, 2021, Virtual Conference, Toronto, Ontario, Canada Piscataway, NJ: IEEE, 2021 ISBN: 9781728176062 ISBN: 9781728176055 S.52005204 
 International Conference on Acoustics, Speech and Signal Processing (ICASSP) <46, 2021, Online> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer IMM () 
 quantization (signal); error probability; convolution; Conferences; signal processing algorithms; linear programming; minimization 
Abstract
A scalable algorithm is derived for multilevel quantization of sensor observations in distributed sensor networks, which consist of a number of sensors transmitting a summary information of their observations to the fusion center for a final decision. The proposed algorithm is directly minimizing the overall error probability of the network without resorting to minimizing pseudo objective functions such as distances between probability distributions. The problem formulation makes it possible to consider globally optimum error minimization at the fusion center and a personbyperson optimum quantization at each sensor. The complexity of the algorithm is quasilinear for i.i.d. sensors. Experimental results indicate that the proposed scheme is superior in comparison to the current stateoftheart.