A Low-Cost and Fail-Safe Inertial Nav-igation System for Airplanes

Robotics전자공학과

201250797깡돌가

2013.04.29

2

Introduction 1.

Algorithm and method2.

Experiment and result3.

Conclusion 4.

CONTENTS

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INTRODUCTIONInertial navigation system everywhere

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INTRODUCTION

Gyroscope Accelerometer

INS Compass

GPS

Airspeed

ASL board

Triadis ISU

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INTRODUCTION

The parameterization of orientation as well as nonlineari-ties are the major challenges inherent to the state reconstruc-tion with six degrees of freedom.

A minimal orientation representation e.g. with Tait-Bryan an-gles yields singularities, but angle/axis or the quaternion de-scription 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 re-search is ongoing. The EKF uses linearization around the esti-mated state for the propagation of covariances.

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METHOD, ALGORITHM

During normal operation, when GPS is available, respec-tive position updates are performed. At the same time, the wind is estimated, along with some of its statistical properties using airspeed measurements.

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METHOD, ALGORITHM

In the case of (temporary) unavailability of position mea-surements, 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.

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METHOD, ALGORITHM

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 accelerometerthe atmospheric pressure reduced to sea level (QFF)

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METHOD, ALGORITHM

The (noise free) INS equations are

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METHOD, ALGORITHMLinearizing the system around the states (x) allows describing the dynamics of the error states:

Via straightforward derivation, the system matrix:

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METHOD, ALGORITHM

For the sake of simplicity and limited computational power, the choice was made to discretize both the nonlinear system and the lineariza-tion with a zeroth order approximation:

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METHOD, ALGORITHMBefore formulating the propagation equation for the state error co-variance 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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METHOD, ALGORITHM

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METHOD, ALGORITHM

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EXPERIMENT, RESULT

Glider pilot’s view

Experiment is 45-minute-long trajectory of a flight lasting several hours. For the evaluation of the proposed backup fil-ter, the GPS positions were ignored for 30 minutes and re-placed by airspeed backup updates.

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EXPERIMENT, RESULT

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CONCLUSION

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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CONCLUSION