centralized two-stage Kalman estimation method with related measurement noise

Abstract

The invention relates to a centralized two-stage Kalman estimation method with related measurement noise. Aimied at the filtering problem of a multi-sensor measurement system with related measurementnoise influencing a measurement value, a measurement equation with measurement noise irrelevance is re-established by introducing a decorrelation technology, and an optimal estimation value of a system state is obtained through a two-stage Kalman filter. Compared with a two-stage Kalman method directly used, the method provided by the invention has the advantages that although the data fusion results are the same, the calculation complexity is greatly reduced.

Description

technical field

[0001] The invention belongs to the technical field of filters, in particular to a centralized two-stage Kalman estimation method with correlated measurement noise. Background technique

[0002] Kalman filtering techniques require accurate models of process dynamics and measurements. In many practical situations, biases affect system dynamics and observations, and can lead to performance degradation if biases are not incorporated into the model. The two-stage approach is very effective in dealing with state estimation of systems with unknown biases, as it can improve computational performance and prevent the occurrence of the curse of dimensionality.

[0003] In the 1960s, Friedland proposed a two-stage Kalman filter (TKF), whose basic idea is to decouple the enhanced state filter (ASKF) into two filters, namely a low-dimensional "unbiased" filter and a " biased" filter, and the best estimate can be viewed as the output of the unbiased filter corrected by t...

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