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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.
2
Agenda with Challenging Issues as:
RCI/DRDO Hyderabad
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
3
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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4
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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5
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
6
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
7
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
8
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
9
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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10
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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12
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
13
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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Type/Time Of Maneuver
Bias 10, 15, 20 deg/hr Pitch man@10sec
SIMULATION TEST RESULTS
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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.
RCI/DRDO Hyderabad
23
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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