Calman filtering method with unknown observation noise collaboration matrix

Abstract

The invention provides a Kalman filter method with recursive estimation of the unknown observation noise covariance matrix for the discrete-time time-invariant system, and solves the situation that the observation noise covariance matrix is ​​completely unknown in the discrete-time linear time-invariant system The system state filtering estimation problem under . Step 1, use the observation sequence to construct a new statistical sequence; Step 2, calculate the covariance matrix recurrence formula of {ξk}; Step 3, calculate the observation noise covariance matrix estimation sequence {f(R)k}; Step 4, calculate The estimated sequence of the covariance matrix is ​​then calculated by algebraic relationships to calculate the real-time estimate of the covariance matrix of the observation noise; step five, substitute the estimated sequence of the covariance matrix of the observation noise into the standard Kalman filter method to calculate the real-time state estimation of the system and Covariance matrix of state estimate biases.

Description

technical field [0001] The invention belongs to the field of discrete time self-adaptive filtering, in particular to a Kalman filtering method with unknown observation noise covariance matrix recursive estimation. Background technique [0002] Due to its own superiority, after more than 50 years of development, different forms of Kalman filter theory have been theoretically promoted and applied in different engineering fields. [0003] The Kalman filter method is a time-domain state estimation method. Because it uses the description method of the state space, and its recursive form is easy to implement by computer, the state estimation based on the state space can be applied to the advanced control method in modern control theory , to obtain good system performance. For the system described by the linear state space model, the standard Kalman filter method can obtain the estimation of the internal state of the system from the observation sequence with observation noise, imp...

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Problems solved by technology

[0008] In view of this, the present invention provides a Kalman filter method with unknown observation noise covariance matrix recursive estimation for discrete time time invariant systems, the purpose is to solve the observation noise covariance in discrete time linear time invariant systems When the variance matrix is ​​completely unknown, the problem of system state filtering estimation taking into account the real-time requirements of the filtering algorithm

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