TAILIEUCHUNG - GPS - đường dẫn quán tính và hội nhập P7

It 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 (., wheel speeds of land vehicles, water speed sensors for ships, air speed sensors for aircraft, or Doppler radar), and time sensors (clocks). | Global Positioning Systems Inertial Navigation and Integration Mohinder S. Grewal Lawrence R. Weill Angus P. Andrews Copyright 2001 John Wiley Sons Inc. Print ISBN 0-471-35032-X Electronic ISBN 0-471-20071-9 7 Kalman Filter Basics INTRODUCTION What is a Kalman Filter It 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 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 . 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 . winds surface currents turns in the road or terrain changes but it may also include unpredictable changes in the sensor parameters. 179 180 KALMAN FILTER BASICS More abstract treatments of the Kalman filter are presented in 18 19 40 46 67 69 71 72 and a more basic introduction can be found in 31 . How it Works The Kalman filter maintains two types of variables 1. Estimated State Vector. The components of the estimated state vector include the following a The variables of interest . what we want or need to know such as position and velocity . b Nuisance variables that are of no intrinsic interest but may be necessary to the estimation process. These nuisance .

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