Fast multilevel quantization for distributed detection based on Gaussian approximation

An iterative algorithm is derived for multilevel quantization of sensor observations in distributed sensor networks, where each sensor transmits a summary of its observation to the fusion center and the fusion center makes the final decision. The proposed scheme is composed of a person-by-person optimum quantization at each sensor and a Gaussian approximation to the distribution of the test statistic at the fusion center. The complexity of the algorithm is linear both for identically and nonidentically distributed independent sensors. Experimental results indicate that the proposed scheme is promising in comparison to the current state-of-the-art.