Transcription of Kalman Filtering Tutorial - Biorobotics
{{id}} {{{paragraph}}}
Example: bachelor of science
Kalman Filtering Tutorial - Biorobotics
1 Understanding and ApplyingKalman FilteringLindsay KleemanDepartment of Electrical and Computer Systems EngineeringMonash University, Clayton2 a basic understanding of Kalman Filtering and assumptionsbehind its (but cannot avoid) mathematical treatment to broaden some practicalities and examples of C++ software is a Kalman Filter and What Can It Do?A Kalman filter is an optimal estimator - ie infers parameters of interest fromindirect, inaccurate and uncertain observations. It is recursive so that newmeasurements can be processed as they arrive.
4 What if the noise is NOT Gaussian? Given only the mean and standard deviation of noise, the Kalman filter is the best linear estimator. Non-linear estimators may be better.
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: