A Low-Cost and Fail-Safe Inertial
Navigation System for Airplanes
Robotics
전자공학과
201250797
깡돌가
2013.04.29
1.
Introduction
2.
Algorithm and method
3.
Experiment and result
4.
Conclusion
2
Inertial navigation system everywhere
3
INS
Gyroscope
Accelerometer
Compass
GPS
Airspeed
ASL
board
Triadis
ISU
4
The parameterization of orientation as well as nonlinearities are the
major challenges inherent to the state reconstruction with six
degrees of freedom.
A minimal orientation representation e.g. with Tait-Bryan angles
yields singularities, but angle/axis or the quaternion description
cannot be directly used due to the additional unit length constraint.
Concerning the second difficulty, namely nonlinearities, various
alternatives to the EKF have been proposed and related research is
ongoing. The EKF uses linearization around the estimated state for
the propagation of covariances.
5
During normal operation, when GPS is available, respective
position updates are performed. At the same time, the wind is
estimated, along with some of its statistical properties using
airspeed measurements.
6
In the case of (temporary) unavailability of position
measurements, however, the system will switch to its back-up
mode, where the airspeed vector measurement is used as
filter update instead of the position update.
7
It is well understood that MEMS inertial sensors suffer from bias drift
and even scale change, mostly caused by temperature and
mechanical stress variation. Therefore, the biases are typically
included into the state vector.
the bias of the gyroscope and accelerometer
the atmospheric pressure reduced to sea level (QFF)
8
The (noise free) INS
equations are
9
Linearizing the system around the states (x) allows describing the dynamics
of the error states:
Via straightforward derivation, the system matrix:
10
For the sake of simplicity and limited computational power, the choice was
made to discretize both the nonlinear system and the linearization with a
zeroth order approximation:
11
Before formulating the propagation equation for the state error
covariance matrix P, some attention is paid to the process noise. We
assume that zero-mean Gaussian White Noise dw is corrupting the
system. In the linear case,
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Experiment is 45-minute-long trajectory of a flight lasting
several hours. For the evaluation of the proposed backup filter,
the GPS positions were ignored for 30 minutes and replaced by
airspeed backup updates.
Glider pilot’s view
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A robust state estimation framework for airplanes was
presented that is based on Extended Kalman Filtering. The
generality of the proposed framework makes it applicable to
both unmanned and manned airplanes. Not only inertial
sensors, magnetometers and GPS updates are used, but also
both static and dynamic pressure measurements. The resulting
filter is robust in the sense that it can cope with even long
GPS outage.
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