An algorithm for the difference correlation filter for multi-parameter marine timing observation data

With the deepening of the ocean research, more and more demands on the quality of observational data are proposed. In order to effectively reduce the uncertainty of the measured data, the measured temperature and salinity data related to the seawater CO₂ partial pressure was taken as an example. As the instrument platform can measure multi-parameter simultaneously, and the measurement indicators can be assumed to change stably in small time and space range. On this basis, most of the original observation sequences were confirmed to meet the hypothesis of second-order differential smoothness by ADF stationary test theory. Then, an expression for observation data uncertainty based on the differential statistical characteristic of observation sequence, and an algorithm of multi-parameter difference correlation Federated Filter based on the outliers recognition, are proposed in this paper. Comparing with the common filtering algorithm, it was found that the new algorithm can effectively integrate the relevant information of parameters, reduce the uncertainty of the sequence, and maximize the protection of the original measurement data.