Multiple Hypothesis Tracking: Basic Idea€¦ · Multiple Hypothesis Tracking: Basic Idea Iterative updating of conditional probability densities! kinematic target state xk at time

Multiple Hypothesis Tracking: Basic IdeaIterative updating of conditional probability densities!

kinematic target state xk

at time t

k

, accumulated sensor data Zk

a priori knowledge: target dynamics models, sensor model, other context

• prediction: p(xk�1|Zk�1

)

dynamics model��!context

p(xk

|Zk�1)

• filtering: p(xk

|Zk�1)

sensor data Z

k��!sensor model

p(xk

|Zk

)

• retrodiction: p(xl�1|Zk

)

filtering output ��dynamics model

p(xl

|Zk

)

� finite mixture: inherent ambiguity (data, model, road network )� optimal estimators: e.g. minimum mean squared error (MMSE)� initiation of pdf iteration: multiple hypothesis track extraction

Sensor Data Fusion - Methods and Applications, 4th Lecture on May 2, 2018

Kalman filter: xk

= (r>k

, r>k

)

>, Zk

= {zk

,Zk�1}

initiation: p(x0

) = N�

x0

; x0|0, P0|0

�

, initial ignorance: P0|0 ‘large’

prediction: N�

xk�1; x

k�1|k�1, Pk�1|k�1�

dynamics model��!F

k|k�1,Dk|k�1

N�

xk

; xk|k�1, Pk|k�1

�

xk|k�1 = F

k|k�1xk�1|k�1

Pk|k�1 = F

k|k�1Pk�1|k�1Fk|k�1>+D

k|k�1

filtering: N�

xk

; xk|k�1, Pk|k�1

�

current measurement zk��!

sensor model: Hk

,Rk

N�

xk

; xk|k, Pk|k

�

xk|k = x

k|k�1 +Wk|k�1⌫k|k�1, ⌫

k|k�1 = zk

�Hk

xk|k�1

Pk|k = P

k|k�1 �Wk|k�1Sk|k�1Wk|k�1

>, S

k|k�1 = Hk

Pk|k�1Hk

>+R

k

Wk|k�1 = P

k|k�1Hk

>Sk|k�1

�1‘KALMAN gain matrix’


Stationary object position from noisy measurements

position: x 2 R, measurements: z

i

with “likelihood”: p(z

i

|x) = N (z

i

;x,�

i

)

each measurement can have its own measurement error �

i

(standard deviation)

Initial knowledge: p(x) = N (x;x

0

,⌃

0

), ⌃

0

� �

1

, flat.

Impact of the first measurement: p(x|z1

) = N (x; z

1

,�

1

)


p(x|z1

) = p(z

1

|x) p(x) /R

dx p(z

1

|x) p(x) Bayes


p(x|z1

) = p(z

1

|x) p(x) /R

dx p(z

1

|x) p(x) Bayes

/ N (z

1

;x,�

1

)N (x;x

0

,⌃

0

) up to normalization


p(x|z1

) = p(z

1

|x) p(x) /R

dx p(z

1

|x) p(x) Bayes

/ N (z

1

;x,�

1

)N (x;x

0

,⌃

0


/ exp

n

�1

2

⇣

(x� x

0

)

2

/⌃

2

0

+ (z

1

� x)

2

/�

2

1

⌘o

Ignore constants!


p(x|z1

) = p(z

1

|x) p(x) /R

dx p(z

1

|x) p(x) Bayes

/ N (z

1

;x,�

1

)N (x;x

0

,⌃

0


/ exp

n

�1

2

⇣

(x� x

0

)

2

/⌃

2

0

+ (z

1

� x)

2

/�

2

1

⌘o

Ignore constants!

= exp

n

�1

2

⇣

(x

2 � 2xx

0

+ x

2

0

)/⌃

2

0

+ (z

2

1

� 2z

1

x+ x

2

)/�

2

1

⌘o


p(x|z1

) = p(z

1

|x) p(x) /R

dx p(z

1

|x) p(x) Bayes

/ N (z

1

;x,�

1

)N (x;x

0

,⌃

0


/ exp

n

�1

2

⇣

(x� x

0

)

2

/⌃

2

0

+ (z

1

� x)

2

/�

2

1

⌘o

Ignore constants!

= exp

n

�1

2

⇣

(x

2 � 2xx

0

+ x

2

0

)/⌃

2

0

+ (z

2

1

� 2z

1

x+ x

2

)/�

2

1

⌘o

/ exp

n

�1

2

⇣

(x

2 � 2xx

0

)/⌃

2

0

+ (�2z1

x+ x

2

)/�

2

1

⌘o


p(x|z1

) = p(z

1

|x) p(x) /R

dx p(z

1

|x) p(x) Bayes

/ N (z

1

;x,�

1

)N (x;x

0

,⌃

0


/ exp

n

�1

2

⇣

(x� x

0

)

2

/⌃

2

0

+ (z

1

� x)

2

/�

2

1

⌘o

Ignore constants!

= exp

n

�1

2

⇣

(x

2 � 2xx

0

+ x

2

0

)/⌃

2

0

+ (z

2

1

� 2z

1

x+ x

2

)/�

2

1

⌘o

/ exp

n

�1

2

⇣

(x

2 � 2xx

0

)/⌃

2

0

+ (�2z1

x+ x

2

)/�

2

1

⌘o

= exp

n

�1

2

⇣

x

2

(1/⌃

2

0

+ 1/�

2

1

)� 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)

⌘o


p(x|z1

) = p(z

1

|x) p(x) /R

dx p(z

1

|x) p(x) Bayes

/ N (z

1

;x,�

1

)N (x;x

0

,⌃

0


/ exp

n

�1

2

⇣

(x� x

0

)

2

/⌃

2

0

+ (z

1

� x)

2

/�

2

1

⌘o

Ignore constants!

= exp

n

�1

2

⇣

(x

2 � 2xx

0

+ x

2

0

)/⌃

2

0

+ (z

2

1

� 2z

1

x+ x

2

)/�

2

1

⌘o

/ exp

n

�1

2

⇣

(x

2 � 2xx

0

)/⌃

2

0

+ (�2z1

x+ x

2

)/�

2

1

⌘o

= exp

n

�1

2

⇣

x

2

(1/⌃

2

0

+ 1/�

2

1

)� 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)

⌘o

= exp

n

�1

2

⇣

x

2

/⌃

2

1

� 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)

⌘o

, 1/⌃

2

1

= 1/⌃

2

0

+ 1/�

2

1


p(x|z1

) = p(z

1

|x) p(x) /R

dx p(z

1

|x) p(x) Bayes

/ N (z

1

;x,�

1

)N (x;x

0

,⌃

0


/ exp

n

�1

2

⇣

(x� x

0

)

2

/⌃

2

0

+ (z

1

� x)

2

/�

2

1

⌘o

Ignore constants!

= exp

n

�1

2

⇣

(x

2 � 2xx

0

+ x

2

0

)/⌃

2

0

+ (z

2

1

� 2z

1

x+ x

2

)/�

2

1

⌘o

/ exp

n

�1

2

⇣

(x

2 � 2xx

0

)/⌃

2

0

+ (�2z1

x+ x

2

)/�

2

1

⌘o

= exp

n

�1

2

⇣

x

2

(1/⌃

2

0

+ 1/�

2

1

)� 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)

⌘o

= exp

n

�1

2

⇣

x

2

/⌃

2

1

� 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)

⌘o

, 1/⌃

2

1

= 1/⌃

2

0

+ 1/�

2

1

= exp

n

�1

2

⇣

x

2 � 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)⌃

2

1

⌘

/⌃

2

1

o


p(x|z1

) = p(z

1

|x) p(x) /R

dx p(z

1

|x) p(x) Bayes

/ N (z

1

;x,�

1

)N (x;x

0

,⌃

0


/ exp

n

�1

2

⇣

(x� x

0

)

2

/⌃

2

0

+ (z

1

� x)

2

/�

2

1

⌘o

Ignore constants!

= exp

n

�1

2

⇣

(x

2 � 2xx

0

+ x

2

0

)/⌃

2

0

+ (z

2

1

� 2z

1

x+ x

2

)/�

2

1

⌘o

/ exp

n

�1

2

⇣

(x

2 � 2xx

0

)/⌃

2

0

+ (�2z1

x+ x

2

)/�

2

1

⌘o

= exp

n

�1

2

⇣

x

2

(1/⌃

2

0

+ 1/�

2

1

)� 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)

⌘o

= exp

n

�1

2

⇣

x

2

/⌃

2

1

� 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)

⌘o

, 1/⌃

2

1

= 1/⌃

2

0

+ 1/�

2

1

= exp

n

�1

2

⇣

x

2 � 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)⌃

2

1

⌘

/⌃

2

1

o

= exp

n

�1

2

⇣

x

2 � 2xx

1

⌘

/⌃

2

1

o

, x

1

= (x

0

/⌃

2

0

+ z

1

/�

2

1

)⌃

2

1


p(x|z1

) = p(z

1

|x) p(x) /R

dx p(z

1

|x) p(x) Bayes

/ N (z

1

;x,�

1

)N (x;x

0

,⌃

0


/ exp

n

�1

2

⇣

(x� x

0

)

2

/⌃

2

0

+ (z

1

� x)

2

/�

2

1

⌘o

Ignore constants!

= exp

n

�1

2

⇣

(x

2 � 2xx

0

+ x

2

0

)/⌃

2

0

+ (z

2

1

� 2z

1

x+ x

2

)/�

2

1

⌘o

/ exp

n

�1

2

⇣

(x

2 � 2xx

0

)/⌃

2

0

+ (�2z1

x+ x

2

)/�

2

1

⌘o

= exp

n

�1

2

⇣

x

2

(1/⌃

2

0

+ 1/�

2

1

)� 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)

⌘o

= exp

n

�1

2

⇣

x

2

/⌃

2

1

� 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)

⌘o

, 1/⌃

2

1

= 1/⌃

2

0

+ 1/�

2

1

= exp

n

�1

2

⇣

x

2 � 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)⌃

2

1

⌘

/⌃

2

1

o

= exp

n

�1

2

⇣

x

2 � 2xx

1

⌘

/⌃

2

1

o

, x

1

= (x

0

/⌃

2

0

+ z

1

/�

2

1

)⌃

2

1

= exp

n

�1

2

⇣

x

2 � 2xx

1

+ x

2

1

� x

2

1

⌘

/⌃

2

1

o

Add Zero!


p(x|z1

) = p(z

1

|x) p(x) /R

dx p(z

1

|x) p(x) Bayes

/ N (z

1

;x,�

1

)N (x;x

0

,⌃

0


/ exp

n

�1

2

⇣

(x� x

0

)

2

/⌃

2

0

+ (z

1

� x)

2

/�

2

1

⌘o

Ignore constants!

= exp

n

�1

2

⇣

(x

2 � 2xx

0

+ x

2

0

)/⌃

2

0

+ (z

2

1

� 2z

1

x+ x

2

)/�

2

1

⌘o

/ exp

n

�1

2

⇣

(x

2 � 2xx

0

)/⌃

2

0

+ (�2z1

x+ x

2

)/�

2

1

⌘o

= exp

n

�1

2

⇣

x

2

(1/⌃

2

0

+ 1/�

2

1

)� 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)

⌘o

= exp

n

�1

2

⇣

x

2

/⌃

2

1

� 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)

⌘o

, 1/⌃

2

1

= 1/⌃

2

0

+ 1/�

2

1

= exp

n

�1

2

⇣

x

2 � 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)⌃

2

1

⌘

/⌃

2

1

o

= exp

n

�1

2

⇣

x

2 � 2xx

1

⌘

/⌃

2

1

o

, x

1

= (x

0

/⌃

2

0

+ z

1

/�

2

1

)⌃

2

1

= exp

n

�1

2

⇣

x

2 � 2xx

1

+ x

2

1

� x

2

1

⌘

/⌃

2

1

o

Add Zero!

/ exp

n

�1

2

(

x� x

1

)

2

/⌃

2

1

o


p(x|z1

) = p(z

1

|x) p(x) /R

dx p(z

1

|x) p(x) Bayes

/ N (z

1

;x,�

1

)N (x;x

0

,⌃

0


/ exp

n

�1

2

⇣

(x� x

0

)

2

/⌃

2

0

+ (z

1

� x)

2

/�

2

1

⌘o

Ignore constants!

= exp

n

�1

2

⇣

(x

2 � 2xx

0

+ x

2

0

)/⌃

2

0

+ (z

2

1

� 2z

1

x+ x

2

)/�

2

1

⌘o

/ exp

n

�1

2

⇣

(x

2 � 2xx

0

)/⌃

2

0

+ (�2z1

x+ x

2

)/�

2

1

⌘o

= exp

n

�1

2

⇣

x

2

(1/⌃

2

0

+ 1/�

2

1

)� 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)

⌘o

= exp

n

�1

2

⇣

x

2

/⌃

2

1

� 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)

⌘o

, 1/⌃

2

1

= 1/⌃

2

0

+ 1/�

2

1

= exp

n

�1

2

⇣

x

2 � 2x(x

0

/⌃

2

0

+ z

1

/�

2

1

)⌃

2

1

⌘

/⌃

2

1

o

= exp

n

�1

2

⇣

x

2 � 2xx

1

⌘

/⌃

2

1

o

, x

1

= (x

0

/⌃

2

0

+ z

1

/�

2

1

)⌃

2

1

= exp

n

�1

2

⇣

x

2 � 2xx

1

+ x

2

1

� x

2

1

⌘

/⌃

2

1

o

Add Zero!

/ exp

n

�1

2

(

x� x

1

)

2

/⌃

2

1

o

/ N (x;x

1

,⌃

1

)

with: ⌃

1

=

q

1/(1/⌃

2

0

+ 1/�

2

1

) ! �

1

(⌃

0

� �

1

)

x

1

= ⌃

2

1

(x

0

/⌃

2

0

+ z

1

/�

2

1

) ! z

1




i


i

|x) = N (z

i

;x,�

i

)


i



0

,⌃

0

), ⌃

0

� �

1

, flat.


) = N (x; z

1

,�

1

)

Impact of k measurement: p(x|zk

, . . . , z

1

) = N (x;x

k

,⌃

k

)

with: 1/⌃

2

k

=

k

X

i=1

1/�

2

i

harmonic mean, x

k

= ⌃

2

k

0

@

k

X

i=1

z

i

/�

2

i

1

A


p(x|zk

, . . . , k

1

) / p(z

k

|x) p(x|zk�1, . . . , z1) Bayes


p(x|zk

, . . . , k

1

) / p(z

k

|x) p(x|zk�1, . . . , z1) Bayes

/ N (z

k

;x,�

k

)N (x;x

k�1,⌃k�1) induction assumption


p(x|zk

, . . . , k

1

) / p(z

k

|x) p(x|zk�1, . . . , z1) Bayes

/ N (z

k

;x,�

k

)N (x;x


/ exp

n

�1

2

⇣

(x� x

k�1)2

/⌃

2

k�1 + (z

k

� x)

2

/�

2

k

⌘o


p(x|zk

, . . . , k

1

) / p(z

k

|x) p(x|zk�1, . . . , z1) Bayes

/ N (z

k

;x,�

k

)N (x;x


/ exp

n

�1

2

⇣

(x� x

k�1)2

/⌃

2

k�1 + (z

k

� x)

2

/�

2

k

⌘o

/ exp

n

�1

2

⇣

(x

2 � 2xx

k�1)/⌃2

k�1 + (�2zk

x+ x

2

)/�

2

k

⌘o


p(x|zk

, . . . , k

1

) / p(z

k

|x) p(x|zk�1, . . . , z1) Bayes

/ N (z

k

;x,�

k

)N (x;x


/ exp

n

�1

2

⇣

(x� x

k�1)2

/⌃

2

k�1 + (z

k

� x)

2

/�

2

k

⌘o

/ exp

n

�1

2

⇣

(x

2 � 2xx

k�1)/⌃2

k�1 + (�2zk

x+ x

2

)/�

2

k

⌘o

= exp

n

�1

2

⇣

x

2

(1/⌃

2

k�1 + 1/�

2

k

)� 2x(x

k�1/⌃2

k�1 + z

k

/�

2

k

)

⌘o


p(x|zk

, . . . , k

1

) / p(z

k

|x) p(x|zk�1, . . . , z1) Bayes

/ N (z

k

;x,�

k

)N (x;x


/ exp

n

�1

2

⇣

(x� x

k�1)2

/⌃

2

k�1 + (z

k

� x)

2

/�

2

k

⌘o

/ exp

n

�1

2

⇣

(x

2 � 2xx

k�1)/⌃2

k�1 + (�2zk

x+ x

2

)/�

2

k

⌘o

= exp

n

�1

2

⇣

x

2

(1/⌃

2

k�1 + 1/�

2

k

)� 2x(x

k�1/⌃2

k�1 + z

k

/�

2

k

)

⌘o

= exp

n

�1

2

⇣

x

2

/⌃

2

k

� 2x(x

k�1/⌃2

k�1 + z

k

/�

2

k

)

⌘o

, ⌃

�2k

= ⌃

�2k�1 + �

�2k


p(x|zk

, . . . , k

1

) / p(z

k

|x) p(x|zk�1, . . . , z1) Bayes

/ N (z

k

;x,�

k

)N (x;x


/ exp

n

�1

2

⇣

(x� x

k�1)2

/⌃

2

k�1 + (z

k

� x)

2

/�

2

k

⌘o

/ exp

n

�1

2

⇣

(x

2 � 2xx

k�1)/⌃2

k�1 + (�2zk

x+ x

2

)/�

2

k

⌘o

= exp

n

�1

2

⇣

x

2

(1/⌃

2

k�1 + 1/�

2

k

)� 2x(x

k�1/⌃2

k�1 + z

k

/�

2

k

)

⌘o

= exp

n

�1

2

⇣

x

2

/⌃

2

k

� 2x(x

k�1/⌃2

k�1 + z

k

/�

2

k

)

⌘o

, ⌃

�2k

= ⌃

�2k�1 + �

�2k

= exp

n

�1

2

⇣

x

2 � 2xx

k

⌘

/⌃

2

k

o

, x

k

= (x

k�1/⌃2

k�1 + z

k

/�

2

k

)⌃

2

k


p(x|zk

, . . . , k

1

) / p(z

k

|x) p(x|zk�1, . . . , z1) Bayes

/ N (z

k

;x,�

k

)N (x;x


/ exp

n

�1

2

⇣

(x� x

k�1)2

/⌃

2

k�1 + (z

k

� x)

2

/�

2

k

⌘o

/ exp

n

�1

2

⇣

(x

2 � 2xx

k�1)/⌃2

k�1 + (�2zk

x+ x

2

)/�

2

k

⌘o

= exp

n

�1

2

⇣

x

2

(1/⌃

2

k�1 + 1/�

2

k

)� 2x(x

k�1/⌃2

k�1 + z

k

/�

2

k

)

⌘o

= exp

n

�1

2

⇣

x

2

/⌃

2

k

� 2x(x

k�1/⌃2

k�1 + z

k

/�

2

k

)

⌘o

, ⌃

�2k

= ⌃

�2k�1 + �

�2k

= exp

n

�1

2

⇣

x

2 � 2xx

k

⌘

/⌃

2

k

o

, x

k

= (x

k�1/⌃2

k�1 + z

k

/�

2

k

)⌃

2

k

= exp

n

�1

2

⇣

x

2 � 2xx

k

+ x

2

k

� x

2

k

⌘

/⌃

2

k

o


p(x|zk

, . . . , k

1

) / p(z

k

|x) p(x|zk�1, . . . , z1) Bayes

/ N (z

k

;x,�

k

)N (x;x


/ exp

n

�1

2

⇣

(x� x

k�1)2

/⌃

2

k�1 + (z

k

� x)

2

/�

2

k

⌘o

/ exp

n

�1

2

⇣

(x

2 � 2xx

k�1)/⌃2

k�1 + (�2zk

x+ x

2

)/�

2

k

⌘o

= exp

n

�1

2

⇣

x

2

(1/⌃

2

k�1 + 1/�

2

k

)� 2x(x

k�1/⌃2

k�1 + z

k

/�

2

k

)

⌘o

= exp

n

�1

2

⇣

x

2

/⌃

2

k

� 2x(x

k�1/⌃2

k�1 + z

k

/�

2

k

)

⌘o

, ⌃

�2k

= ⌃

�2k�1 + �

�2k

= exp

n

�1

2

⇣

x

2 � 2xx

k

⌘

/⌃

2

k

o

, x

k

= (x

k�1/⌃2

k�1 + z

k

/�

2

k

)⌃

2

k

= exp

n

�1

2

⇣

x

2 � 2xx

k

+ x

2

k

� x

2

k

⌘

/⌃

2

k

o

/ exp

n

�1

2

(

x� x

k

)

2

/⌃

2

k

o


p(x|zk

, . . . , k

1

) / p(z

k

|x) p(x|zk�1, . . . , z1) Bayes

/ N (z

k

;x,�

k

)N (x;x


/ exp

n

�1

2

⇣

(x� x

k�1)2

/⌃

2

k�1 + (z

k

� x)

2

/�

2

k

⌘o

/ exp

n

�1

2

⇣

(x

2 � 2xx

k�1)/⌃2

k�1 + (�2zk

x+ x

2

)/�

2

k

⌘o

= exp

n

�1

2

⇣

x

2

(1/⌃

2

k�1 + 1/�

2

k

)� 2x(x

k�1/⌃2

k�1 + z

k

/�

2

k

)

⌘o

= exp

n

�1

2

⇣

x

2

/⌃

2

k

� 2x(x

k�1/⌃2

k�1 + z

k

/�

2

k

)

⌘o

, ⌃

�2k

= ⌃

�2k�1 + �

�2k

= exp

n

�1

2

⇣

x

2 � 2xx

k

⌘

/⌃

2

k

o

, x

k

= (x

k�1/⌃2

k�1 + z

k

/�

2

k

)⌃

2

k

= exp

n

�1

2

⇣

x

2 � 2xx

k

+ x

2

k

� x

2

k

⌘

/⌃

2

k

o

/ exp

n

�1

2

(

x� x

k

)

2

/⌃

2

k

o

/ N (x;x

k

,⌃

k

)

with: ⌃

k

= 1/

v

u

u

u

t

k

X

i=1

1/�

2

i

harmonic mean

x

k

= ⌃

2

k

0

@

k

X

i=1

z

i

/�

2

i

1

A

weighted arithmetic mean




i


i

|x) = N (z

i

;x,�

i

)


i



0

,⌃

0

), ⌃

0

� �

1

, flat.


) = N (x; z

1

,�

1

)

Impact of k measurement: p(x|zk

, . . . , z

1

) = N (x;x

k

,⌃

k

)

with: 1/⌃

2

k

=

k

X

i=1

1/�

2

i

harmonic mean, x

k

= ⌃

2

k

0

@

k

X

i=1

z

i

/�

2

i

1

A

For � = �

i

8i: ⌃

k

= �/

pk “square-root law”

x

k

=

1

k

k

X

i=1

z

i

arithmetic mean


The Multivariate GAUSSian Pdf

� wanted: probabilities ‘concentrated’ around a center x

� quadratic distance: q(x) =

1

2

(x� x)P�1(x� x)>

q(x) defines an ellipsoid around x, its volume and orienta-

tion being determined by a matrix P (symmetric: P> = P,

positively definite: all eigenvalues > 0).

� first attempt: p(x) = e

�q(x)/

R

dx e

�q(x)(normalized!)

p(x) = N (x; x, P) =

1

q

|2⇡P|e

�1

2

(x�x)>P�1(x�x)

Exercise 4.1 Show:

R

dx e

�q(x)=

p

|2⇡P|, E[x] = x, E[(x� x)(x� x)>] = P

Trick: Symmetric, positively definite matrices can be diagonalized by an orthogonal coordinate transform.





1

2

(x� x)P�1(x� x)>





�q(x)/

R

dx e


p(x) = N (x; x, P) =

1

q

|2⇡P|e

�1

2

(x�x)>P�1(x�x)

E[x] = x, E[(x� x)(x� x)>] = P (covariance)





1

2

(x� x)P�1(x� x)>





�q(x)/

R

dx e


p(x) = N (x; x, P) =

1

q

|2⇡P|e

�1

2

(x�x)>P�1(x�x)

E[x] = x, E[(x� x)(x� x)>] = P (covariance)

� GAUSSian Mixtures: p(x) =

P

i

p

i

N (x; xi

, Pi

) (weighted sums)


A Useful Product Formula for GAUSSians

N�

z; Hx, R�

N�

x; y, P�

= N�

z; Hy, S�

| {z }

independent of x

⇥

(

N�

x; y+W⌫, P�WSW>�

N�

x; Q�1(P�1x+H>R�1z), Q�

⌫ = z�Hy, S = HPH>+R, W = PH>S�1, Q�1 = P�1 +H>R�1H.

Sketch of the proof:

• Interpret N�

z; Hx, R�

N�

x; y, P�

as a joint pdf p(z|x)p(x) = p(z,x).

• Show that p(z,x) is a GAUSSian: p(z,x) = N��

zx

�

;

�

Hyy

�

,

�

S HPPH> P

��

.

• Calculate from p(z,x) the marginal and conditional pdfs p(z) and p(x|z).

• From p(z,x) = p(z|x)p(x) = p(x|z)p(z) = p(x, z) we obtain the result.


Affine Transforms of GAUSSian Random Variables

N⇣

x; E[x], C[x]⌘ y=t+Tx��! N

⇣

y; t+TE[x], TC[x]T>⌘



N⇣

x; E[x], C[x]⌘ y=t+Tx��! N

⇣


p(y) =

Z

dx p(x,y) =

Z

dx p(y|x) p(x) =

Z

dx �(y � t�Tx) p(x)



N⇣

x; E[x], C[x]⌘ y=t+Tx��! N

⇣


p(y) =

Z

dx p(x,y) =

Z

dx p(y|x) p(x) =

Z


A possible representation: �(x� y) = N⇣

x; y, D⌘

with D! O!

p(y) =

Z

dxN (y; t+Tx,D)N (x;E[x],C[x]) for D! 0



N⇣

x; E[x], C[x]⌘ y=t+Tx��! N

⇣


p(y) =

Z

dx p(x,y) =

Z

dx p(y|x) p(x) =

Z


A possible representation: �(x� y) = N⇣

x; y, D⌘

with D! O!

p(y) =

Z

dxN (y; t+Tx,D)N (x;E[x],C[x]) for D! 0

= N⇣

y; t+TE[x], TC[x]T>+D⌘

for D! 0; product formula!

Also true if dim(x) 6= dim(y)!Sensor Data Fusion - Methods and Applications, 4th Lecture on May 2, 2018

A popular model for object evolutions

Piecewise Constant White Acceleration Model

Consider state vectors: xk

= (r>k

, r>k

)

>(position, velocity)

For known xk�1 and without external influences we have with �T

k

= t

k

� t

k�1:

xk

=

I �T

k

IO I

!

rk�1rk�1

!

=: Fk|k�1xk�1, see blackboard!

Assume during the interval �T

k

a constant acceleration ak

causing the state evolution:

1

2

�T

2

k

I�T

k

I

!

ak

=: Gk

ak

, linear transform!

Let ak

be a Gaussian RV with pdf: p(ak

) = N⇣

ak

; o, ⌃2

k

I⌘

, we therefore have:

p(Gk

ak

) = N⇣

Gk

ak

; o, ⌃2

k

Gk

G>k

⌘

.


Therefore: p(xk

|xk�1) = N

⇣

xk

; Fk|k�1xk�1, Dk|k�1

⌘

with

Fk|k�1 =

I �T

k

IO I

!

, Dk|k�1 = ⌃

2

k

1

4

�T

4

k

I 1

2

�T

3 I1

2

�T

3

k

I �T

2

k

I

!


Therefore: p(xk

|xk�1) = N

⇣

xk

; Fk|k�1xk�1, Dk|k�1

⌘

with

Fk|k�1 =

I �T

k

IO I

!

, Dk|k�1 = ⌃

2

k

1

4

�T

4

k

I 1

2

�T

3

k

I1

2

�T

3

k

I �T

2

k

I

!

Exercise 4.2 Consider xk

= (r>k

, r>k

, r>k

)

>(position, velocity, acceleration)

Show that Fk|k�1 and D

k|k�1 = ⌃

2

k

Gk

G>k

(constant acceleration rates) are given by:

Fk|k�1 =

0

@

I �T

k

I 1

2

�T

2

k

IO I �T

k

IO I I

1

A

, Dk|k�1 = ⌃

2

k

0

@

1

4

�T

4

k

I 1

2

�T

3

k

I 1

2

�T

2

k

I1

2

�T

3

k

I �T

2

k

I �T

k

I1

2

�T

2

k

I �T

k

I I

1

A

with �T

k

= t

k

� t

k�1. Reasonable choice:

1

2

q

max

⌃

k

q

max


Sensor Fusion: Gain in Localization Accuracy

If a stationary target is observed by N sensors, we na¨ıvely

expect an improvement in accuracy / 1/

pN .


Sensor Fusion: Gain in Localization Accuracy

If a stationary target is observed by N sensors, we na¨ıvely

expect an improvement in accuracy / 1/

pN .

a closer look: The error of each measurement zi

is described by a related

measurement error covariance matrix Ri

(‘error ellipsoids’). In 2 dimensions:

s1 s2

s3

Ri

can strongly depend on the underlying senor-to-target geometry!


Simplified: Range, Azimuth Measurements

• measurements in polar coordinates:

zk

= (r

k

,'

k

)

>, measurement error: R =

⇣

�

2

r

0

0 �

2

'

⌘

, r, ' independent




zk

= (r

k

,'

k

)


⇣

�

2

r

0

0 �

2

'

⌘

, r, ' independent

Likelihood function in polar coordinates:

p(zk

|xk

) = N (zk

; xpk

, Rp

)




zk

= (r

k

,'

k

)


⇣

�

2

r

0

0 �

2

'

⌘

, r, ' independent

Likelihood function in polar coordinates:

p(zk

|xk

) = N (zk

; xpk

, Rp

)

• What is the likelihood function in Cartesian coordinates?

t[zk

] = r

k

⇣

cos'

k

sin'

k

⌘




zk

= (r

k

,'

k

)


⇣

�

2

r

0

0 �

2

'

⌘

, r, ' independent

• in Cartesian coord.: expand around rk|k�1 = (r

k|k�1, 'k|k�1)

>:

t[zk

] = r

k

�

cos'

k

sin'

k

�

⇡ t[rk|k�1] +T (z

k

� rk|k�1)

constant and linear term of a Taylor series only, blackboard!




zk

= (r

k

,'

k

)


⇣

�

2

r

0

0 �

2

'

⌘

, r, ' independent


k|k�1, 'k|k�1)

>:

t[zk

] = r

k

�

cos'

k

sin'

k

�

⇡ t[rk|k�1] +T (z

k

� rk|k�1)

T =

@t[rk|k�1]

@rk|k�1

=

⇣

cos'

k|k�1 �rk|k�1 sin'

k|k�1sin'

k|k�1 r

k|k�1 cos'k|k�1

⌘

=

✓

cos' � sin'

sin' cos'

◆

| {z }

rotation D'

✓

1 0

0 r

◆

| {z }

dilation Sr




zk

= (r

k

,'

k

)


⇣

�

2

r

0

0 �

2

'

⌘

, r, ' independent


k|k�1, 'k|k�1)

>:

t[zk

] = r

k

�

cos'

k

sin'

k

�

⇡ t[rk|k�1] +T (z

k

� rk|k�1)

T =

@t[rk|k�1]

@rk|k�1

=

⇣

cos'


k|k�1sin'

k|k�1 r

k|k�1 cos'k|k�1

⌘

=

✓

cos' � sin'

sin' cos'

◆

| {z }

rotation D'

✓

1 0

0 r

◆

| {z }

dilation Sr

• affine transform of GAUSSian random variables:

N�

z; x, R� z0=t+Tz��! N

�

z0; t+Tx, TRT>�




zk

= (r

k

,'

k

)


⇣

�

2

r

0

0 �

2

'

⌘

, r, ' independent


k|k�1, 'k|k�1)

>:

t[zk

] = r

k

�

cos'

k

sin'

k

�

⇡ t[rk|k�1] +T (z

k

� rk|k�1)

T =

@t[rk|k�1]

@rk|k�1

=

⇣

cos'


k|k�1sin'

k|k�1 r

k|k�1 cos'k|k�1

⌘

=

✓

cos' � sin'

sin' cos'

◆

| {z }

rotation D'

✓

1 0

0 r

◆

| {z }

dilation Sr

• Cartesian error covariance (time dependent):

TRT> = D'

Sr

R Sr

D

>'

= D'

⇣

�

2

r

0

0 (r�

'

)

2

⌘

D>'

• sensor fusion: sensor-to-target-geometry enters into TRT>


s1 s2

s3

sensor fusion: sensor-to-target-geometry enters into TRT>


Filtering step: alternative formulation

p

�

xk

| Zk

�

= p

�

xk

| zk

,Zk�1�(current measurement)

=

p

�

zk

|xk

�

p

�

xk

| Zk�1�

R

dxk

p

�

zk

|xk

�

p

�

xk

| Zk�1�

(BAYES’ rule)

=

N�

zk

; Hk

xk

, Rk

�

N�

xk

; xk|k�1, Pk|k�1

�

R

dxk

N�

zk

; Hk

xk

, Rk

�

| {z }

likelihood function

N�

xk

; xk|k�1, Pk|k�1

�

| {z }

prediction for t

k

= N�

xk

; xk|k, Pk|k

�

(product formula: 2. version!)

xk|k = P�1

k|k�

P�1k|k�1xk|k�1 +H>

k

R�1k

zk

�

P�1k|k = P�1

k|k�1 +H>k

R�1k

H

inverse covariance matrices are called information matrices.


ϕ π ϕ−

S1 S2 R

r r

X


Special case: stationary object

Example: different sensors

F = I D = OH = I R

k

time dependent!

Initiation: x1|1 = z

1

, P1|1 = R

1

Prediction: xk|k�1 = F

k|k�1xk�1|k�1, Pk|k�1 = F

k|k�1Pk�1|k�1F>k|k�1 +D

k|k�1

Filtering: xk|k = P�1

k|k�

P�1k|k�1xk|k�1 +H>

k

R�1k

zk

�

(2. formulation)

P�1k|k = P�1

k|k�1 +H>k

R�1k

H




F = I D = OH = I R

k

time dependent!


1

, P1|1 = R

1

Prediction: xk|k�1 = x

k�1|k�1, Pk|k�1 = P

k�1|k�1


k|k�

P�1k�1|k�1xk�1|k�1 +R�1

k

zk

�

P�1k|k = P�1

k�1|k�1 +R�1k




F = I D = OH = I R

k

time dependent!


1

, P1|1 = R

1

Prediction: xk|k�1 = x

k�1|k�1, Pk|k�1 = P

k�1|k�1


k|k�

P�1k�1|k�1xk�1|k�1 +R�1

k

zk

�

= P�1k|k

k

X

i=1

R�1i

zi

P�1k|k = P�1

k�1|k�1 +R�1k

=

k

X

i=1

R�1i




F = I D = OH = I R

k

time dependent!


1

, P1|1 = R

1

Filtering: xk|k = P

k|k

k

X

i=1

R�1i

zi

, Pk|k =

⇣

k

X

i=1

R�1i

⌘�1

Kalman filter! weighted, recursive, arithmetic mean

estimation error covariance matrix: harmonic mean of measurement error matrices!

ϕ π ϕ−

S1 S2 R

r r

X


Discussion: stationary objects

• If all measurement error covariances R

i

, i = 1, . . . , k are identical,

we observe the statistical “square-root effect”: P

k|k = R/k

• If the corresponding error ellipses are significantly different in their

geometric extension, we can observe a much larger effect.

• statistical “intersection” of error ellipses: harmonic mean!

• In the limiting case of very eccentric error ellipses, we obtain

triangulation of a position from bearings (! multiple sensor data

fusion!).

• These considerations are valid also for 3D and more abstract mea-

surements. The corresponding intersections: not intuitively clear.


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Multiple Hypothesis Tracking: Basic Idea€¦ · Multiple Hypothesis Tracking: Basic Idea Iterative updating of conditional probability densities! kinematic target state xk at time