Multi-sensor Data Fusion Of The Two

Multisensor distributed data fusion has many practical applications,and it is a focus in technological fields. This paper deals with multisensor distributed statistic decision and multisensor distributed estimation fusion. We get some results:In multisensor distributed statistic decision,we consider multisensor distributed Neyman-Pearson decision with correlated observation data and suggest an efficient algorithm to search for optimum local compression rules for any fixed fusion rule. Simulations are provided and give addtional support to the above results.In multisensor distributed estimation fusion. We consider multisensorstatistical interval estimation fusion for the purpose of estimation of a parameter . A multisensor convex linear statistic fusion modal for optimal interval estimation fusion is established. A Gauss-Seidel iteration computation method for searching for the fusion weights is suggested. In particular,we suggest convex combination minimum variance fusion that reduces huge computation of fusion and yield approximately optimal estimate performance generally,moreover,may achievers exactly optimal performance in some cases. The above results are supported by computer simulations.