Abstract
Many systems include sensors with large measurement delays that must be fused in a Kalman filter in real time. Often, the filter state must be propagated at a higher rate than the rate at which measurements are taken. This can lead to a significant amount of unused CPU time during the time steps in which no measurements are available. This paper presents a method of fusing delayed measurements for a restricted set of systems, which more efficiently uses processing resources at the expense of data availability. The new method splits the filter into a high-rate and a low-rate task running in parallel. The high-rate task propagates the whole states, and the low-rate task propagates and updates an error state filter, which can be distributed over several high-rate periods.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
