imu ant az ahmed

8/8/2019 IMU Ant Az Ahmed

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Dr. Ahmed Bassyouni IEEE Meeting-Syracuse

IMU & GPS Sensors Integrated toAntenna Drive Control Loop

Ahmed Bassyouni

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Azimuth and Elevation Errors of Radar Antenna

Antennas Radar may suffer from poor accuracy in Azimuth andElevation angular motion due to External and Internal sources oferrors.

External Error Sources

Surface Deflections due to thermal effects and force of wind Disturbance of motion due to Gust and Wind Additional weights due to accumulated snow and dust

Internal Error Sources

Changes in the control loop parameters due to aging or misuse. Poor reliability due to undetected errors of HW and SW designs

or installation

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Dr. Ahmed Bassyouni IEEE Meeting-Syracuse 3

These Sources are:

1. Forces OF WIND AND GHUST applied to the antenna2. Fluctuating and systematic errors in the drive control3. Environmental effects that may cause vibrations4. Geometrical structure of the antenna, spacing of radiators,5. Channels mismatch due to the errors of phase shifters output



4/20


We can model the beam Pointing Error as

= + T + a + w

Where pointing error due to phase shifters T Pointing error due to temperature effect a pointing error due to antenna structure w Pointing error due to forces of wind

Trades analysis and control techniques are developedfor these models

Today we focus only to model the wind effect



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The force on the antenna due to wind with velocity V is given by

Where is the static air density, C D is Drag Coefficient and A is the projectedantenna area.

This equation is valid for both steady and time-varying wind gusts. The windvelocity V is composed of a mean velocity V m and a gust component V g.

2

2

1AVCF DD

Modeling of Wind Applied to Antenna Surface

gm VVV


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The projected area A is related to the array face area A 0 by the azimuthangle of the array relative to wind and the antenna tilt back angle by

)Tiltcos()AZcos(AA 0


The antenna deflection due to wind is proportional to force F, such thatwe may write

2V)AZcos(K

The constant K depends on the antenna design and mechanical structure;it is the transfer coefficient that relates the wind velocity to the deflection atparticular antenna azimuth angle .


7/20Dr. Ahmed Bassyouni IEEE Meeting-Syracuse 7

For a rotating antenna with constant rotation rate , we then have

2)t(VV)tcos(K)t( gm


where t is time and is shown explicitly to emphasize the time varyingnature of the deflection due to wind variability and antenna rotation.



A proposed antenna controller consists of the estimator, the PIcontroller, and the flexible mode controller.

The antenna state space model(A,B,C) includes the disturbances vand the measurement noise w

x Ax Bu v

y Cx w

The disturbances (predominantly wind gusts) have covariance V . Themeasurement noise has covariance W . It is assumed additionally that theinput and output noises are not correlated. This assumption is equivalentto independence of their sources. Indeed, the measurement noise isindependent of the wind disturbances.

Controller Model for Disturbed Antenna



The purpose of the estimator is to evaluate the antenna dynamicstates using the rate input, u , and the encoder output, y , of theantenna. The estimated state vector is denoted , and the errorbetween the actual encoder output and the estimated output isdefined as

y Cx y y

The estimated state is obtained from the following equation:

e x Ax Bu K




Antenna

Flexible modeController Estimator

PI Controller

+

+

+

y

v w

r

_

+

eu pI

u

++

u f u

++

x

Controller Modules



11/20Dr. Ahmed Bassyouni IEEE Meeting-Syracuse

Electronic sensors to determine forces acting on a body* Accelerometers * Gyros (Angular Rate Sensors

Limitations that System Engineer has to overcome:Inherent errors in position and velocity due to integration of sensorerror and sensor drift and Kalman Filter Latency

Uncertain dynamics may cause sudden errors thatdecrease the Azimuth and Elevation accuracy

The System Engineer has to resolve the problemapplying advanced technology and creative ideas

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Improve the Azimuth and Elevation Accuracy



GPS position and velocity are blended with the inertial data. Otherwise, if theGPS data is not available, the system will operate without any GPS aiding.

The inertial navigator computes position, velocity and orientation of the IMU.

The Kalman filter estimates the errors in the inertial navigator along with IMU,distance


The IMU consists of three accelerometers and threegyroscopes (gyros) so that accelerations alongspecific axis and angular rotations can be measured.From the IMU angular rotations and accelerationalong its axis are used to calculate roll, pitch andyaw angles.

The IMU feeds its data into KF


13/20

IMU

Angular Rate AccelerationTime

GPS

Position Velocity Time

Position Velocity Acceleration Angular RateTime

Kalman Filter

High short termP, V accuracy

High Long termP,V accuracy

High long term andshort term accuracy

Advantage of Integrating IMU and GPS




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Standard Kalman Filter Loop


1k k k k x x v k k k k z H x w

KF is built to estimate therandom process 1k x


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Strap downINS

ProcessorIMU

ErrorController Algorithm

Kalman Filter Algorithm

GPS AzMeasure

GPSRx2

GPSRx1

OutputPosition

Filter Correction Estimated Error

GPS Observables

Integrated IMU and GPS for Accurate Positioning




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Difference between the position form the IMU and GPS is processedIn KF (typically @ 10Hz) to estimate the slowly growing position errorIn the IMU.

Since this error is a function of Azimuth error (as modeled in thedifferential equations in KF) , observing the inertial position errorsmeans the orientation errors and IMU sensor errors can also beestimated.


System Accuracy Process

sin .cos sin .sin

cos cos N E D Azimuth Az

, , N E D Orientation errors w r to North, East, and Down axis

AzimuthOffset angle between IMU and GPS


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AntennaSystem

AzimuthDrive

IMU&GPSIntegrated Algorithms

PositionController

External/InternalDisturbance

Desired

Azimuth

Actual

Azimuth

IMU& GPS integrated to Antenna Position Control Loop




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IMU AccuracyInertial error sources can be divided into stationary errors like therandom constant part of the gyro drift, and on-stationary errors likethe accelerometer scale factor.

Kalman FilterThe estimation accuracy depends on the a priori information of thesystem and measurement models, as well as the noise statistics.

A well-designed Kalman filter will attenuate the initial state errors, andsmooth the effects of system and measurement errors through theaveraging process.

Factors Influence the Measurements Accuracy

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19/20

The ACU has the following operational modes:

Shutdown (no power to motors, brakes set)

Standby (motor power on, brakes set)

Velocity (rate loop driving of axes from local handset)

Encoder (drive so encoders equal commanded position)

Autonomous (drive so bore -sight equals commanded position)

Preset (same as Autonomous with limited velocity and acceleration)

Stow (drive to stow position).

To Achieve Reliable and Repeatable positioning data



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Dr.Ahmed Bassyouni IEEE Meeting-Syracuse 20

Questions ?

Thank You

imu ant az ahmed

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