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Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Mechatronics - Foundations and Applications
Position Measurement in Inertial Systems
JASS 2006, St.Petersburg
Christian Wimmer
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Content
1. Motivation
2. Basic principles of position measurement
3. Sensor technology
4. Improvement: Kalman filtering
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Motivation
Johnnie: A biped walking machine OrientationStabilizationNavigation
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Motivation
Automotive Applications: Drive dynamics AnalysisAnalysis of test route topologyDriver assistance systems
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Motivation
Aeronautics and Space Industry: Autopilot systemsHelicoptersAirplaneSpace Shuttle
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
MotivationMilitary Applications: ICBM, CMDrones (UAV)TorpedoesJets
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Motivation
Maritime Systems: Helicopter PlatformsNaval NavigationSubmarines
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Motivation
Industrial robotic Systems: MaintenanceProduction
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Basic Principles
Measurement by inertia and integration:
AccelerationVelocityPosition
Newton‘s 2. Axiom:
F = m x a BASIC PRINCIPLE OF DYNAMICS
Measurement system with 3 sensitive axes
3 Accelerometers3 Gyroscope
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Basic Principles
Gimballed Platform Technology: 3 accelerometers3 gyroscopescardanic Platform
ISOLATED FROM ROTATIONAL MOTIONTORQUE MOTORS TO MAINTAINE DIRECTIONROLL, PITCH AND YAW DEDUCED FROMRELATIVE GIMBAL POSITIONGEOMETRIC SYSTEM
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Basic Principles
Strapdown Technology: Body fixed3 Accelerometers3 Gyroscopes
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Basic Principles
Strapdown Technology: The measurement principle
SENSORS FASTENED DIRECTLY ON THE VEHICLEBODY FIXED COORDINATE SYSTEMANALYTIC SYSTEM
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Basic Principles
Reference Frames: i-framee-framen-frameb-frame
Also normed: WGS 84
15.041 /eie h
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Vehicle‘s acceleration in inertial axes (1.Newton):
Problem: All quantities are obtained in vehicle’s frame (local)Euler Derivatives!
Basic Principles
2
2( )i p i OP i i ie e e
d dv r f g A f g
dt dt
Interlude: relative kinematics
Differentiation:
2 2
2 22i p i OP ie e OP ie e OP e ie e OP e ie e ie e OPe
d d d dv r A r r r r
dt dt dt dt
trans corrot cent
Inertial system: i
Moving system: eP = CoM
O
P
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Frame Mechanisation I: i-Frame
Vehicle‘s velocity (ground speed) and Coriolis Equation:
abbreviated:
Differentiation: Applying Coriolis Equation (earth‘s turn rate is constant):
subscipt: with respect to; superscript: denotes the axis set; slash: resolved in axis set
Basic Principles
( ) ( )ie
i e
d dr r r
dt dt
( )e
e
dr v
dt
2
2( ) ( )( )
e iei ii
d d dr v r
dt dt dt
2
2( )( )
e ie e ie ieii
d dr v v r
dt dt
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Frame Mechanisation II: i-Frame
Newton’s 2nd axiom:
abbreviated:
Recombination: i-frame axes: Substitution:
subscipt: with respect to; superscript: denotes the axis set; slash: resolved in axis set
Basic Principles
2
2
( )
e ie e ie ie
i
dv f v r g
dt l ie ieg g r
2
2
( )
e ie e l
i
dv f v g
dt i i i i i
e ie e lv f v g
i b i i ie ib ie e lv A f v g
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Basic Principles
Frame Mechanisation III: Implementation
BODYMOUNTEDGYROSCOPES
ATTITUDECOMPUTER
RESOLUTION OF SPECIFIC FORCEMEASUREMENTS
BODYMOUNTEDACCELEROMETERS NAVIGATION
COMPUTER
CORIOLISCORRECTION
GRAVITYCOMPUTER
INITIAL ESTIMATES OFVELOVITY AND POSITION
INITIAL ESTIMATES OFATTITUDE
POSITIONINFORMATION
bib
ibA
bf if
ig
POSITION ANDVELOVITY ESTIMATES
iev
POSSIBILITY FOR KALMAN FILTER INSTALLATION
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Basic Principles
Strapdown Attitude Representation:
Direction cosine matrix
Quaternions
Euler angles
No singularities, perfect for internal computations
singularities, good physical appreciation
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Basic Principles
Strapdown Attitude Representation: Direction Cosine Matrix
11 12 13
21 22 23
31 32 33
nb
c c c
A c c c
c c c
1 313 cos ;n b
c n n
For Instance:
Simple Derivative:Axis projection:
bnb nb nbA A
0
0
0
z ybnb z x
y x
With skew symmetric matrix:
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Basic Principles
Strapdown Attitude Representation: Quaternions
Idea: Transformation is single rotation about one axis
cos / 2
( / )sin / 2
( / )sin / 2
( / )sin / 2
x
y
z
a
bp
c
d
, ,x y z
Components of angle Vector, defined with respect to reference frame
Magnitude of rotation:
Operations analogous to 2 Parameter Complex number
p a ib jc kd
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Basic Principles
Strapdown Attitude Representation: Euler Angles
Rotation about reference z axis through angleRotation about new y axis through angleRotation about new z axis through angle
cos cos cos sin sin sin cos sin sin cos sin cos
cos sin cos sin sin sin sin sin cos cos sin sin
sin sin cos cos cosnbA
1 Tnb bn bnA A A 90 Singularity:
Gimbal angle pick-off!
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Sensor Technology
Accelerometers
Physical principles:
PotentiometricLVDT (linear voltage differential transformer) Piezoelectric
F ma mg Newton’s 2nd axiom:
gravitational part: Compensation
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Sensor Technology
Accelerometers
Potentiometric
+
-
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Sensor Technology
Accelerometers
LVDT (linear voltage differential transformer)
Uses Induction
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Sensor Technology
Accelerometers
Piezoelectric
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Sensor Technology
Accelerometers
Servo principle (Force Feedback)
Intern closed loop feedbackBetter linearityNull seeking instead of displacement measurement
1 - seismic mass2 - position sensing device3 - servo mechanism4 - damper5 - case
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Sensor Technology
Gyroscopes
Vibratory GyroscopesOptical Gyroscopes
Historical definition:
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Sensor Technology
Gyroscopes: Vibratory Gyroscopes
Coriolis principle: 1. axis velocity caused by harmonic oscillation (piezoelectric)2. axis rotation3. axis acceleration measurement
Problems:High noiseTemperature driftsTranslational accelerationvibration
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Sensor Technology
Gyroscopes: Vibratory Gyroscopes
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Sensor Technology
Gyroscopes: Optical Gyroscopes
Sagnac Effect:
Super Luminiszenz DiodeBeam splitterFiber optic cable coilEffective path length difference
LASER
INTERFERENCEDETECTOR
MODULATOR
Beamsplitter
Beamsplitter
8 A
c
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
The Kalman Filter – A stochastic filter method
Motivation:
Uncertainty of measurement System noise Bounding gyroscope’s drift (e.g. analytic systems) Higher accuracy
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
The Kalman Filter – what is it?
Definition:Optimal recursive data processing algorithm.
Optimal, can be any criteria that makes sense.
Combining information: Knowledge of the system and measurement device dynamics Statistical description of the systems noise, measurement errors and uncertainty in
the dynamic models Any available information about the initial conditions of the variables of interest
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
The Kalman Filter – Modelization of noise
Deviation:Bias: Offset in measurement provided by a sensor, caused by imperfections
Noise: disturbing value of large unspecific frequency range
Assumption in Modelization:White Noise: Noise with constant amplitude (spectral density) on frequency domain (infinite energy);
zero mean
Gaussian (normally) distributed: probability density function 2
1
21( )
2
x
f x e
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Basic Idea:
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Combination of independent estimates: stochastic Basics (1-D)
Mean value:
Variance:
Estimates:
Mean of 2 Estimates(with weighting factors):
1
1( )
n
ii
x E x xn
2
2
1
1
1
n
ii
x xn
1 2,x x
1 1 2 2x w x w x
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Combination of independent estimates: stochastic Basics (1-D)
Weighted mean:
Variance of weightedmean:
Not correlated:
Variance of weighted mean:
1 1 2 2( ) ( )E x w E x w E x
2 22 2 21 1 1 2 2 2 1 2 1 1 2 2( ) ( ) 2 ( ) ( )E w x E x w x E x w w x E x x E x
1 1 2 2( ) ( ) 0E x E x x E x
2 22 2 2 2 21 1 1 2 2 2 1 1 2 2( ) ( )w E x E x w E x E x w w
Quantiles are independent!
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Combination of independent estimates: stochastic Basics (1-D)
Weighting factors:
Substitution:
Optimization (Differentiation):
Optimum weight:
1 2 1w w
2 2 2 2 21 2(1 )w w
2 2 2 2 21 22(1 ) 2 0
dw w
dw
21
2 21 2
w
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Combination of independent estimates: stochastic Basics (1-D)
Mean value:
Variance:
Multidimensional case:
Covariance matrix:
2 22 1 1 2
1 1 22 22 1
( )x x
x x w x x
2 22 21 2
12 21 2
(1 )w
( )( )TP E x x x x
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Interlude: the covariance matrix
1-D: Variance – 2nd central momentN-D: Covariance – diagonal elements are variances, off-diagonal elements encode the correlations
Covariance of a vector:
n x n matrix, which can be modal transformed, such that are only diagonal elements with decoupled error contribution;Symmetric and quadratic
cov( ) ( )T
xxx P x E x x x x P
cov( , ) ( , ) ( ) ( ) xyx y E x y E x E y P
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Interlude: the covariance matrix applied to equations
Equation structure:
x, y are gaussian distributed, c is constant:
Covariance of z:
Linear difference equation:
Covariance:
with:
Kalman Filter
z Ax By c
T T T Tzz xx xy yx yyP AP A AP B BP A BP B
1 1( ) ( , ) ( ) ( ) ( ) ( )i i i i i i ix t t t x t B t u t w t
1 1 1( ) ( , ) ( ) ( , )Ti i i i i i dP x t t t P x t t t Q
( ) ( )Td i iQ E w t w t Diagonal structure: since white gaussian noise
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Combination of independent estimates: (n-D)
Mean value:
measurement:
Mean value:
Covariancewith:
1 2 1 1 2( ) ( )x I W x Wx x W x x
2 2y Hx
1 1 2 1 1 2 1 2( ) ( ) ( )x x KH x x x K Hx y I KH x Ky
1 2 1 1 2 1 2 1 2( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )T
P x E I KH x Ky I KH E x KE x KE y I KH x Ky I KH E x KE y
W KH
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Combination of independent estimates: (n-D)
Covariance:
Covariance:
Minimisation of Variance matrix‘sDiagonal elements (Kalman Gain):
1
1 1T TK PH HPH R
1 1 1 1 2 2 2 2( ) ( ) ( ) ( ) ( ) ( )T TT TP I KH E x E x x E x I KH KE y E y y E y K
1( ) ( )T TP I KH P I KH KRK
For further information please also read:P.S. Maybeck: ‘Stochastic Models, Estimation and Control Volume 1’,Academic Press, New York San Francisco London
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Combination of independent estimates: (n-D)
Mean value:
Variance:
1 1 2x x K Hx y
1 1P P KHP
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Interlude: time continuous system to discrete system
Continuous solution:
Substitution:
Conclusion:
Sampling time:
0
0
( )( )( ) ( ) ( )t
A t tA t
t
x t e Bu d e x t
( ) ; ;ku t u t d d
0
( ) ( ) ( )t t kT
kT
t d B d B H t kT
1k k kx x Hu
( ) ( ) ( ) ( ) kx t t kT x kT H t kT u
(( 1) ) ( ) ( ) ( ) kx k T T x kT H T u
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
The Kalman Filter: Iteration Principle
INITIAL ESTIMATION OF STATES AND QUALITY OF STATE
PREDICTION OF STATES (SOLUTION) BETWEEN TWO ITERATIONS
PREDICTION OF ERROR COVARIANCE BETWEEN TWO ITERATIONS
CALCULATION OFKALMAN GAIN (WEIGHTING OF MEASUREMENT AND PREDICTION)
DETERMINATION OF NEW SOLUTION (ESTIMATION)
CORRECTION OF THE STOCHASTIC MODELLS TO NEW QUALITY VALUE OF SOLUTIONPREDICTION
CORRECTION
NEXT ITERATION
1k k
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Linear Systems – the Kalman Filter:
Discrete State Model:
Sensor Model:
( 1) ( ) ( ) ( )x k x k Bu k w k
( ) ( ) ( )z k Hx k r k
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Linear Systems – the Kalman Filter: 1. Step Prediction
Prediction:
State Prediction Covariance:
Observation Prediction:
( 1| ) ( | ) TP k k P k k Q
( 1| ) ( | ) ( )x k k x k k Bu k
( 1| ) ( 1| )z k k H x k k
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Linear Systems – the Kalman Filter: 2. Step Correction
Corrected state estimate:
Corrected State Covariance:
Innovation Covariance:
Innovation:
( 1| 1) ( 1| ) ( 1) ( 1)x k k x k k K k v k
( 1| 1) ( 1| ) ( 1) ( 1) ( 1)TP k k P k k K k S k K k
( 1) ( 1) ( 1| ) ( 1) ( 1)TS k H k P k k H k R k
( 1) ( 1) ( 1| )v k z k z k k
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
The Kalman Filter: Kalman Gain
Kalman Gain:( 1| ) ( 1)
( 1)( 1)
TP k k H kK k
S k
State Prediction Covariance
Innovation Covariance
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
( )u k ( | )x k k
( 1| 1)x k k
The Kalman Filter: System Model
G
( 1) ( )x k x k
( 1)z k
( 1| )x k k
( 1| )z k k
( 1| )x k k
( 1)v k ( 1)K k
H
Memory+
-
+ +
++
For linear systems: System matrices are timeinvariant
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Non-Linear Systems – the extended Kalman Filter:
Nonlinear dynamics equation:Nonlinear observation equation:
Solution strategy: Linearize Problem around predicted state: (Taylor Series tuncation)
( , )x f x u
, ,
( , ) ( ) ( , ) ( )x u x u
x f x u x x f x u u ux u
Error Deviation from Prediction stateNecessary for Kalman Gain and Covariance Calculation
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Non-Linear Systems – the extended Kalman Filter:
Prediction:
Correction:
( 1| ) , ( | )x k k f k x k k
( 1| ) ( ) ( | ) ( )Tx xP k k f k P k k f k
1( 1) ( 1) ( 1) ( 1)xK k P k h k S k
( 1| 1) ( 1| ) ( 1) ( 1) , ( 1| )x k k x k k K k z k h k x k k ( 1| 1) ( 1| ) ( 1) ( 1) ( 1)TP k k P k k K k S k K k
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Example: Aiding the missile
MISSILE WITH ON-BOARD INERTIAL NAVIGATION SYSTEM (REPLACING THE PHYSICAL PROCESS MODEL; 1 ESTIMATE) AND NAVIGATION AID (GROUND TRACKER MEASUREMENT; 2 ESTIMATE)
MISSILE SURFACE SENSORS
KALMAN GAINS
INS MEASUREMENT MODEL
Missile Motion
MeasurementNoise
True Position
Measurement Innovations
Estimated INS Error
System Noise
INS Indicated Position
Estimated Range, Elevation and Bearing
+
_
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Example: Aiding the missile
Nine State Kalman Filter: 3 attitude, 3 velocity, 3 position errorsBounding Gyroscope’s and accelerometers drifts by long term signal of surface sensor on launch platform (complementary error characteristics)
Extended Kalman Filter: Attention: All Matrices are vector derivatives! Linearisation around trajectory)
Error Model: (truncated Taylor series)
Discrete Representation: (System Equation)
Attention: All Matrices are vector derivatives matrices!
x F x Dw
1k k k kx x w
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Example: Aiding the missile
Measurement Equations with respect to radar, providing measurements in polar coordinates, i.e. Range (R), elevation ( ) and bearing ( ).
Expressed in Cartesian coordinates (x,y,z):
Radar Measurements:
2 2 2 2R x y z
2 2arctan
z
x y
arctan
y
x
, ,T
z R z v
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Example: Aiding the missile
Estimates of the radar measurements, z, obtained from the inertial navigation system:
Innovation: (Measurement Equation)
2 2 2
2 2arctan
arctan
x y z
Rz
zx y
y
x
z z z H x
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Example: Aiding the missile
H-Matrix (Jacobian):
Best Estimate of the errors after update:
Covariance Prediction:Initial setup: diagonal structure
2 2
22 2 2 2 2 2
2 2 2 2
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0 0
x y z
R R R
x yxz yzH
RR x y R x y
y x
x y x y
1/ 0k kx
1/ /T
k k k k k kP P Q
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Example: Aiding the missile
Filter update:
Estimates of error:
Covariance update:(R measurement noise, diagonal structure)
1/ 1 1 1k k k kx K z
1/ 1 1 1 1/k k k k k kP I K H P
1/ 1 11
1 1/ 1
Tk k k
k Tk k k k
P HK
H P H R
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
Kalman Filter
Example: Aiding the missile
Velocity and Position Correction:
Attitude Correction:(direction cosine matrix)
x x x
C I C
3 2
3 1
2 1
0
0
0
Lecture: Position Measurement in Inertial SystemsChristian WimmerTechnical University of Munich
thank you for your attention