Scalable Multilevel Quantization For Distributed Detection

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 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 person-by-person optimum quantization at each sensor. The complexity of the algorithm is quasi-linear for i.i.d. sensors. Experimental results indicate that the proposed scheme is superior in comparison to the current state-of-the-art.