1© 2012 The MathWorks, Inc.

Inflight Alignment Simulation using Matlab Simulink

RCI/DRDO Hyderabad

Authors,

K. Chandana,

Soumi Chakraborty,

Saumya Shanker,

R.S. Chandra Sekhar,

G. Satheesh Reddy.RCI /DRDO.

Hyderabad.

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Agenda with Challenging Issues as:

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How Weapons are Categorized

What is an INS and why an INS is used

How to align INS inflight

Need for Inflight Alignment

Bias Estimation during Maneuvring

Need for Simulation Model

Modules used in Simulation Testbed

Simulation Test Results

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Surface-to-Surface

Air-to-Surface (ballistic, cruise,

antiship, anti-tank)

Surface-to-Air (anti-aircraft and

antiballistic),

Air-to-Air, and

Anti-satellite Weapon.

Weapon come in types adapted

for different purposes:

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Inertial Navigation System:INS

Inertial navigation is the process of obtaining Position, Velocity and

Attitude (PVA) by continuously integrating system acceleration and

rotation rates measured by an orthogonal triad of accelerometers and

gyroscope.

How to align INS when its in motion?

Methods to initialization

Ground based Alignment In Motion Alignment/Inflight Alignment

• In this method, initial velocity is

taken to be zero without considering the

environmental disturbances

• Initial position is provided by some

external aiding such as GPS or

surveyed positions.

• In this case, as the system

undergoes an arbitrary movement

during initialization, it is not possible to

assign initial values without an aid of

some external reference source.

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In master/slave configurations,

Master INS refers to the system

which provides reference navigation

values such as attitude, velocity,

position.

Slave INS refers to the system to

be aligned and calibrated.

Master INS

(INS in Aircraft)Slave INS

(INS in Weapon)

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Process of transfer of navigation

information from a reference source

to the navigation system when they

are in arbitrary movement

SINS is initialized with MINS

Position

Velocity

Attitude

Inflight alignment In Brief

MASTER

INSSLAVE

INS

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Need for Inflight Alignment

Alignment and initialization of INS in flight

Estimate roll, pitch and azimuth in wing

vibratory environment.

Unaffordability of other forms of alignment

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Weapon INS:SINS

Undergoing Wing

Vibrations

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Critical areas in Inflight Alignment

Frame of navigation

Gyro bias estimation

Duration for alignment

Specific Maneuver requirement

Instant & Duration Of Maneuver

Time Taken For Convergence

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zbyb

xb

Maneuvering MINS NED Frame

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Small accelerometer biases

Minimal modeling errors

No lever arm

Ability to estimate gyro biases

Advantages of Attitude Matching

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Inflight Alignment can be done using different techniques,

one of the best possible way found to be Attitude Matching.

Attitude of the MINS is obtained along with the velocity information.

In order to assess the effect of attitude measurements in estimation

performance, a Kalman filter which uses only attitude information as

measurement is constructed.

The filter is thoroughly analyzed in simulation test bed.

MINS SINS

PVA

How to Validate Attitude Matching Technique

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Need for Simulation Model

All the possible test conditions can be simulated and

expected output can be seen.

Easy flow of analyzing and understanding.

Simulating the possible conditions which is not

feasible in real time test environment.

Real-time tests are done through van trials which

would be difficult to test too many conditions having

Petrol Hike.

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Simulation test bed

6DOF Trajectory

generator

Master INS PVA

Induced errors

Slave INS

Kalman Filter

Misalignments and Gyro Bias

estimates

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Inflight Alignment Schematic

MINS

Kalman Filter

Master Attitude

Slave Attitude

gyro bias

estimates

ˆ M

SC

b

ib

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KALMAN FILTER

1k k k kX A X w

The discrete observations on the process are related to the state via a linear

transformation with usual nomenclature.

k k k kz H X v

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A nine state Kalman Filter is designed to find the estimates of attitude

misalignment between MINS and SINS, and the sensor biases.

STATE AND ITS DYNAMICS

3 3

3 3

3 3 3 33 3

3 33 3 0

0 00

0

0

0

e ecefie xb

x

x xx

xx

X

C

X

X AX

MEASUREMENT PROCESS

ecef S Mobs S M ecefZ C C C

MEASUREMENT RELATION TO STATE (MATRIX FORM)

( )ecef S Mobs S M ecefZ I C S C C

MEASUREMENT RELATION TO STATE (VECTOR FORM)

(2,3)

(3,1)

(1,2)

obs

ecefmeas obs S

obs

Z

Z Z C

Z

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6DOF Model

Bank-angle to ailerons PI control

Airspeed to elevator PID control

Out 12

12Out 11

11Out 10

10Out 9

9Out 8

8Out 7

7Out 6

6Out 5

5

Ang Acc

4 Out 3

3

sensors

2states

1

Stop Simulation

when A /C on the ground

STOP

Sideslip

R2D

R2D

R2D

Pitch angle

Heading

Demux

Demux

Bank-angle -to-Aileron

Proportional

-K-

Bank-angle -to-Aileron

Integral

-K-

Bank angle

Integrator

1

s

Bank angle

Airspeed -to-Elevator

Proportional

-K-

Airspeed -to-Elevator

Integral

-K-

Airspeed -to-Elevator

Derivative

-K-

Airspeed error

Integrator

1

s

Airspeed error

Derivative

du /dt

Airspeed

Command

25

Airspeed

Aerosonde UAV

(Geodetic -frame EOM )

Controls

Winds

RST

States

Sensors

VelW

Mach

Ang Acc

Euler

AeroCoeff

PropCoeff

EngCoeff

Mass

ECEF

MSL

AGL

REarth

AConGnd

AOA

In7

7

In6

6In5

5In4

4In3

3In2

2

In1

1

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MASTER INS MODULE

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SLAVE INS MODULE

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KALMAN OBSERVATION EXTRACTOR

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KALMAN ESTIMATOR

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Type/Time Of Maneuver

Bias 10, 15, 20 deg/hr Pitch man@10sec

SIMULATION TEST RESULTS

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Type/Time Of Maneuver

Misalignment 1,1,1 Pitch man@10sec

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Type/Time Of Maneuver

Bias 10, 15, 20 deg/hr S man@40sec

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Type/Time Of Maneuver

Misalignment 1,1,1 S man@40sec

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Summary

INS aligned inflight using Attitude Matching Technique,

where misalignment estimated is found to be consistent.

A Known Misalignment is fed and Kalman Filter

estimated back to its correct value.

Easy way of analyzing module to module parameter flow.

Simulation testbed created using Matlab Simulink,

Aerospace Toolbox.

Thus this Testbed has brought out a clear vision of each

module to examine.

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REFERENCES1 [Yuksel] YİĞİTER YÜKSEL, Design and Analysis of Inflight Alignment Algorithms,

Middle East Technical University, 2005.

2 [Ogata] K Ogata, Modern Control Engineering, Prentice Hall India, 1996.

3 [Brown-Hwang] Brown & Hwang, Introduction to Random Signal Analysis and Applied Kalman

filtering, Wiley, 1997.

4 [Jazwinski] A Jazwinski, Stochastic Processes and Filtering

Theory, Dover, 2007.

5 [Shortelle] Shortelle K. J., Graham W. R.,F-16 Flight Tests of a Rapid Inflight Alignment

Procedure, IEEE PLANS 1998, pp379-386, 1998.

6 [Spalding] Spalding K., An Efficient Rapid Inflight Alignment

Filter, Proceedings of AIAA Guidance, Navigation and Control Conference,

pp1276-1286, 1992

7 [Stovall] Stovall S. H., Inflight Alignment, Naval Air Warfare Center – Weapons Division,

China Lake, 1996.

8 [Rogers] Rogers R. M., Weapon IMU Inflight Alignment Using Aircraft Position from Actual

Flight Tests, IEEE PLANS, pp328-335, 1996

9 [Savage] Savage P. G., Strapdown System Algorithms,

Advances in Strapdown Inertial Systems,

NATO AGARD Lecture Series, No:133, 1984

10 [Titterton] Titterton D.H. and Weston J.L. Strapdown Inertial Navigation Technology,

IEE Press, Peter Pergrinus Ltd., London, 1997

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THANK YOU

RCI/DRDO Hyderabad