|Article title||DEVELOPMENT OF THE UNIVERSAL METHOD OF FILTRATION OF RANDOM CONSTANT COMPONENT FROM INPUT SIGNAL UNDER PRIOR UNCERTAINTY CONDITIONS|
|Authors||A. S. Shalimov, S. P. Timoshenkov|
|Section||SECTION II. SYSTEMS OF CONTROL AND MODELING|
|Month, Year||01, 2018 @en|
|Abstract||The aim of the study is to develop the method of filtration of the informative signal from primary transducers of physical magnitudes, such as MEMS, from input signal under prior uncertainty conditions. The missing of full and reliable information about the form of the informative signal and noise, belonging to the same frequency range, stipulate certain difficulty in the work with such signals. This stipulates the actuality of the method, which will be able to archive the above mentioned target. The method can be interesting particularly in all fields of the present-day devices, operating with sensors, prediction of the exact values of which cannot be achieved. In order to solve the above mentioned task it seems to be appropriate to use the theory of random processes splashes and, on the base of existing solutions, form the new approach by making a hypothesis of presence the link between the mode of duration of positive splash and the period of following the values in input signal, which has minimum value of correlation score. This method gives the ability to consider the input signal as the realization of the random process, which can be described with the help of known distribution law, and to formulate the boundary conditions for achieving the universality. The present work shows that the most significant influence on the universality of the method is made by the necessity to obtain the functional dependence between the value of analyzed level and the rms value of the input signal. The universality of method is determined by the need of operation with parameters of input signal, which can be given with the help of standard measuring equipment. The implemented investigation shows that the presented method has the maximum efficiency of 10 % during the filtration of the informative signal from the random constant component under the prior uncertainty conditions.|
|Keywords||Prior uncertainty; optimal filtration; methods of detection and prediction of signals; MEMS.|
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