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This paper designs a novel filter based on characteristic function for multidimensional observation systems, essentially extending the proposed filter, which just fitted to one-dimensional observations. For the dynamic model from grinding process cylindrical workpieces, this filter could result in enhanced and incremental productivity and quality control in manufacturing processes. In the processing of the filter design, a new form of filter will be given to adapt to the multidimensional observations, the matrix format of performance index will be designed to fit to matrix format of filter gain, the selecting range of the weighting function vector will be given to ensure the uniform boundedness of the designed performance index, and the filter gain can be obtained by minimizing the performance index. Finally, we illustrate the effectiveness of the proposed method by simulation examples in the field of the target tracking and the grinding process cylindrical workpieces.

This paper is concerned with the distributed filter design problem for a class of state-saturated systems with quantization effects through a sensor network. The filter for each sensor is constructed and an augmented dynamic error system is obtained. The purpose of the addressed problem is to find an upper bound for the estimation error covariance and find a proper filter gain to minimize the bound. It is proved that the desired upper bound can be described by constructing a set of difference equations. Moreover, it is minimized by appropriately designing the parameters of the distributed at each time step. A numerical simulation is exploited to indicate the effectiveness of the developed filters.