Reduced estimations of a proper local maximum of a posteriori probability in high dimension identification problems

摘要

In practical construction of proper local maximum estimations of the a posteriori probability (PLMAP) of nondiscrete distributed parameters by essentially nonlinear measurements in high dimension prob- lems, it is necessary to decrease the dimension of the estimated vector. In this work, the synthesis and the anal- ysis of reduced tPLMAP-estimators are considered, and their application is justified as "linearizing observers" for Kalman filters of the initial high dimension.