New method for distributed and quantitative estimation fusion of multi-sensor based on EM algorithm

For multi-sensor distributed and quantitative estimation fusion problem of underwater target detection, a model of distributed and quantitative estimation fusion is established. The channel noise and its statistical property which is not fully known to fusion center is considered, The superiority of Expectation Maximization (EM) algorithm completely is used in parameter estimation problem when the observation data is missing. A new algorithm of distributed and quantitative estimation fusion is proposed based on EM algorithm. 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. Then, the invariance of the maximum likelihood estimation is used to get the result of the estimation fusion. Simulation results show that the estimation performance of the new algorithm is comparable to the methods which need ideal channel condition when the number of local sensors samples is larger than 5000 and the signal to noise ratio is higher than 6 dB. This new algorithm provides a theoretical basis for realizing the distributed and quantitative estimation fusion system of underwater target detection.