Filtering method of linear random system and related device

Abstract

The invention discloses a filtering method of a linear random system. The filtering method comprises the steps of adding a noise superposing item to the linear random system, and obtaining a noise superposed linear random system; performing observation value expression construction processing according to the noise superposed linear random system, and obtaining an observation value expression; andaccording to an optimal filtering method, performing filtering processing through using the noise superposed linear random system and the observation value expression as parameters, thereby obtaininga filtering result. Through adding the noise superposing item into to the linear random system, the linear random system bears the system noise interference at a previous time point and the system noise interference at the current time point, thereby relatively completely simulating a noise interference environment in an actual condition, further improving subsequent filtering precision and realizing higher filtering processing accuracy. The invention further discloses a linear random system filtering device, another filtering device and a computer-readable storage system, wherein the beneficial effects described above are achieved.

Description

technical field [0001] The present application relates to the field of automatic control, in particular to a filtering method, a filtering device, another filtering device and a computer-readable storage medium for a linear stochastic system. Background technique [0002] With the continuous development of information technology, in the current industrial system, control, communication and computer technology are constantly integrated into information processing and mechanical device operation at different levels. After applying these technologies, the random control of the automatic control system can be realized, and a random control system is obtained. Among them, the stochastic control system is a dynamic system affected by random factors. In continuous research, three aspects of stochastic control systems are mainly improved, namely modeling, filtering and stochastic adaptive control. [0003] Generally, in the process of modeling, it mainly constructs a linear stocha...

AI Technical Summary

Problems solved by technology

However, with the improvement of detection accuracy, it is gradually found that the noise and interference in the actual environment are particularly complex, and with the development of applications, the design requirements of the system become more complex, making the existing technology unable to meet the design filtering accuracy requirements

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