A method for distributed and quantitative estimation fusion of multi-sensor subject to unknown noise variance

In order to solve the problem of distributed and quantitative estimation fusion using multi-sensor of underwater target detection when the probability distribution of the observation noise and the channel noise is not completely known, we make use of the superiority of expectation maximization (EM algorithm) completely in parameter estimation problem when the observation data is missing, based on EM algorithm, an algorithm of distributed and quantitative estimation fusion is proposed. In this method, the unknown parameters of underwater acoustic channel noise and the quantization probability of local quantizer are modeled as the binary Gaussian mixture model parameters, and then, we use the invariance of the maximum likelihood estimation to get the result of the estimation fusion. Simulation results show that: the estimation performance of the algorithm is comparable to the methods which need ideal channel condition when the number of local sensors samples is larger than 6000 and the signal to noise ratio is highter than 6 dB. This method supplies a simplified path of estimation fusion for underwater distributed and cooperated target detection problem.