### ppt

```Introduction to Kalman Filters
Modified from source: http://users.cecs.anu.edu.au/~hartley/Vision-Reading-Course/Kalman-filters.ppt
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Overview
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What is a Kalman Filter?
Conceptual Overview
The Observer Problem
The Theory of Kalman Filter
Example – falling object
References
2
What is a Kalman Filter?
• Recursive data processing algorithm
• Generates optimal estimate of desired quantities
given the set of measurements
• Optimal?
– For linear system and white Gaussian errors, Kalman
filter is “best” estimate based on all previous
measurements
– For non-linear system optimality is ‘qualified’
• Recursive?
– Doesn’t need to store all previous measurements and
reprocess all data each time step
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Applications
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Tracking objects (e.g., missiles, faces, heads, hands)
Economics
Many computer vision applications
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Stabilizing depth measurements
Feature tracking
Cluster tracking
Fusing data from radar, laser scanner and stereo-cameras for
depth and velocity measurements
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Conceptual Overview
x
• Lost on the 1-dimensional line
• Position – x(t)
• Assume Gaussian distributed measurements
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Conceptual Overview
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Sextant Measurement at t1: Mean = z1 and Variance = 2z1
Optimal estimate of position is:  (t1) = z1
Variance of error in estimate: 2x (t1) = 2z1
Boat in same position at time t2 - Predicted position is z1
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Conceptual Overview
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prediction  - (t2)
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measurement
z(t2)
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So we have the prediction  -(t2)
GPS Measurement at t2: Mean = z2 and Variance = 2z2
Need to correct the prediction due to measurement to get  (t2)
Closer to more trusted measurement
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Conceptual Overview
prediction  -(t2)
Bayes rule
0.16
corrected optimal
estimate (t2)
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px z 
p z x p x
pz
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 px z 
0.1
0.08
1
N   z , z   N   x , x
measurement
z(t2)
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p m easurem ent  p prior
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  N   , 
2
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• Corrected mean is the new optimal estimate of position
• New variance is smaller than either of the previous two variances
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Conceptual Overview
z
x   x 

x 
x z
2
2
z
2
x
2
 z 
 


2
 z


x
2

2
z
2
x
x z
x 

2
2
z
2
 z 


 x 
2
x

 x  x
2

x z
2

x   x   z    x  z  x
2
2
x
2
2

2

x z
2

 x 
x
2
 
2
x
2
z  x   x

2
z


K zx
− : prediction (a priori estimate)
: update (a posteriori estimate)
K: Kalman gain


x
2
K 
x z
2
2
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Conceptual Overview
• Lessons so far:
Make prediction based on previous data -  -, -
Take measurement – zk, z
Optimal estimate (ŷ) = Prediction + (Kalman Gain) * (Measurement - Prediction)
Variance of estimate = Variance of prediction * (1 – Kalman Gain)
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Conceptual Overview
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(t2)
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Naïve Prediction
-(t3)
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• At time t3, boat moves with velocity dx/dt=u
• Naïve approach: Shift probability to the right to predict
• This would work if we knew the velocity exactly (perfect model)
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Conceptual Overview
Naïve Prediction
-(t3)
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(t2)
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Prediction  -(t3)
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• Better to assume imperfect model by adding Gaussian noise
• dx/dt = u + w
• Distribution for prediction moves and spreads out
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Conceptual Overview
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Conceptual Overview
0.16
Corrected optimal estimate  (t3)
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Measurement z(t3)
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Prediction  -(t3)
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• Now we take a measurement at t3
• Need to once again correct the prediction
• Same as before
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Conceptual Overview
• Lessons learnt from conceptual overview:
– Initial conditions ( k-1 and k-1)
– Prediction ( -k , -k)
• Use initial conditions and model (eg. constant velocity) to
make prediction
– Measurement (zk)
• Take measurement
– Correction ( k , k)
• Use measurement to correct prediction by ‘blending’
prediction and residual – always a case of merging only two
Gaussians
• Optimal estimate with smaller variance
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Conceptual Overview
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The Observer Problem
Black Box
System
Error Sources
External
Controls
System
System State
(desired but not
known)
Measuring
Devices
Optimal
Estimate of
System State
Observed
Measurements
Estimator
Measurement
Error Sources
• System state cannot be measured directly
• Need to estimate “optimally” from measurements
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Theoretical Basis
• Process to be estimated: (state space)
xk = Axk-1 + Buk + wk-1
zk = Hxk + vk
Process Noise (w) with covariance Q
Measurement Noise (v) with covariance R
• Kalman Filter
Prediction:  - is estimate based on measurements at previous time-steps
-k = Axk-1 + Buk
P-k = APk-1AT + Q
Correction: ŷk has additional information – the measurement at time k
k =  -k + K(zk - H  -k )
K = P-kHT(HP-kHT + R)-1

Pk = (I - KH)P-k

Pk H

H Pk H
T
T
R
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Kalman gain / Blending factor, K
• If we are sure about measurements:
– Measurement error covariance (R) decreases to zero
– K decreases and weights residual more heavily than prediction
lim K  H
1
R0
• If we are sure about prediction
– Prediction error covariance P-k decreases to zero
– K increases and weights prediction more heavily than residual
lim
K 0

Pk  0
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Theoretical Basis
Parameter
Udacity
Welch & Bishop
State
x
x
Measurement
z
z
Control input / driving function
u
u
State transition model
F
A
Control-input model
-
B
Observation model / Measure function
H
H
Measure error/noise covariance
R
R
-
Q
A priori estimate error covariance
/ uncertainty matrix
P
P-
A posteriori estimate error covariance
-
P
Kalman gain / Blending factor
K
K
- covariance matrix of process noise, zk
Process noise covariance
- covariance matrix of process noise, wk
20
Theoretical Basis
Parameter
Udacity
Welch & Bishop
State
x
x
Measurement
z
z
Control input / driving function
u
u
State transition model
F
A
Control-input model
-
B
Observation model / Measure function
H
H
Measure error/noise covariance
R
R
-
Q
A priori estimate error covariance
/ uncertainty matrix
P
P-
A posteriori estimate error covariance
-
P
Kalman gain / Blending factor
K
K
- covariance matrix of process noise, zk
Process noise covariance
- covariance matrix of process noise, wk
21
Theoretical Basis
Correction (Measurement Update)
Prediction (Time Update)
(1) Compute the Kalman Gain
(1) Project the state ahead
K = P-kHT(HP-kHT + R)-1
- k = Axk-1 + Buk
(2) Project the error covariance ahead
P-k = APk-1AT + Q
(2) Update estimate with measurement zk
k =  - k + K(zk - H  - k )
(3) Update Error Covariance
Pk = (I - KH)P-k
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Basic steps – prediction and update
= P=-
Source: http://en.wikipedia.org/wiki/File:Basic_concept_of_Kalman_filtering.svg
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Assumptions behind Kalman
Filter
• The model you use to predict the ‘state’ needs to
be a LINEAR function of the measurement
• Non-linear model linearize about nominal point
(EKF - Extended Kalman Filter)
• The model error and the measurement error
(noise) must be Gaussian with zero mean
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Example – falling object
• Consider an object falling under a constant
gravitational field. Let y(t) denote the height of
the object, then:
y t    g
 y  t   y  t0   g   t  t0 
 y  t   y  t0   y  t0    t  t0  
g
2
  t  t0 
2
D iscrete tim e system w ith  t  t  t 0  1
y  k   y  k  1  g

y  k   y  k  1  y  k  1 
g
2
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Example – falling object
• Exercise: construct state space model from equations,
when we are able to perform measurements, zk, of the
height.
y  k   y  k  1  g

y  k   y  k  1  y  k  1 
g
2
that is, find A, B, uk and H in:
xk = Axk-1 + Buk
zk = Hxk
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Example – falling object
• Exercise: construct state space model from equations,
when we are able to perform measurements, zk, of the
height.
y  k   y  k  1  g
Solution:
x k  A x k 1  B u
zk  Hxk
y  k   y  k  1  y  k  1 

Xk

A
 yk  1
  
 yk  0
B
2
u
1   y k 1   0.5 


 g 
1   y k 1   1 
 yk 
0  
 yk 
z k  1
zk
Xk-1
g
H
Xk
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Example – falling object
MATLAB demo
”kalman_demo.m”
References
1.
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4.
5.
Kalman, R. E. 1960. “A New Approach to Linear Filtering and Prediction
Problems”, Transaction of the ASME--Journal of Basic Engineering, pp. 35-45
(March 1960).
Maybeck, P. S. 1979. “Stochastic Models, Estimation, and Control, Volume 1”,