A Kalman filter is an extremely effective and versatile procedure for combining noisy sensor outputs to estimate the state of a system with uncertain dynamics.
For our purposes in this book:
The noisy sensors may include GPS receivers and inertial sensors (accelerometers and gyroscopes, typically) but may also include speed sensors (e.g., wheel speeds of land vehicles, water speed sensors for ships, air speed sensors for aircraft, or Doppler radar), and time sensors (clocks).
The system state in question may include the position, velocity, acceleration, attitude, and attitude rate of a vehicle on land, at sea, in the air, or in space, but the system state may include ancillary “nuisance variables” for modeling correlated noise sources (e.g., GPS Selective Availability timing errors) and time‐varying parameters of the sensors, such as scale factor, output bias, or (for clocks) frequency. Selective Availability has been suspended as of May 1, 2000.
Uncertain dynamics includes unpredictable disturbances of the host vehicle, whether caused by a human operator or by the medium (e.g., winds, surface currents, turns in the road, or terrain changes), but it may also include unpredictable changes in the sensor parameters.
Kalman Filter Basics
2000-12-15
50 pages
Article/Chapter (Book)
Electronic Resource
English
Wiley | 2019
|Wiley | 2022
|Wiley | 2000
|British Library Online Contents | 2017
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