English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

State estimation is critical to control systems, especially when the statescannot be directly measured. This paper presents an approximate optimal filter,which enables to use policy iteration technique to obtain the steady-state gainin linear Gaussian time-invariant systems. This design transforms the optimalfiltering problem with minimum mean square error into an optimal controlproblem, called Approximate Optimal Filtering (AOF) problem. The equivalenceholds given certain conditions about initial state distributions and policyformats, in which the system state is the estimation error, control input isthe filter gain, and control objective function is the accumulated estimationerror. We present a policy iteration algorithm to solve the AOF problem insteady-state. A classic vehicle state estimation problem finally evaluates theapproximate filter. The results show that the policy converges to thesteady-state Kalman gain, and its accuracy is within 2 %.

Approximate Optimal Filter for Linear Gaussian Time-invariant Systems