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min
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ang
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ne: 7
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ffice
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atio
n: F
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aue.
auc.
dk/~
ymzh
ang/
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ses/
ES
IF/in
dex.
htm
l
FP
8-4:
Est
imat
ion
an
d
Sen
sor
Info
rmat
ion
Fu
sio
n
FP
8-4:
Est
imat
ion
an
d
Sen
sor
Info
rmat
ion
Fu
sio
n
Lec
ture
1Re
view
on
Kalm
an F
ilter
s•C
ours
e ou
tlin
e•W
hat
is K
alm
anfi
lter
(KF)
and
app
licat
ions
of
KF?
•The
dis
cret
e-ti
me
Kalm
anfi
lter
(DKF
)•T
he c
onti
nuou
s-ti
me
Kalm
anfi
lter
(CKF
)•T
he K
alm
anfi
lter
s fo
r no
nlin
ear
syst
ems
3Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Co
urs
e O
utl
ine
Co
urs
e O
utl
ine
1.R
evie
w o
n K
alm
an fi
lter
and
its v
aria
nts
2.M
ultip
le-m
odel
bas
ed K
alm
an fi
lters
3.S
imul
tanu
ous
stat
e an
d pa
ram
eter
es
timat
ion:
Tw
o-st
age
Kal
man
filte
r
4.In
trod
uctio
n to
sen
sor
info
rmat
ion
fusi
on (
I)
5.In
trod
uctio
n to
sen
sor
info
rmat
ion
fusi
on (
II)
and
cour
se s
umm
ary
4Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Th
e P
rob
lem
T
he
Pro
ble
m ––
Wh
y d
o w
e n
eed
W
hy
do
we
nee
d K
alm
anK
alm
anF
ilter
s?F
ilter
s?
Mea
suri
ng
D
evic
esE
stim
ato
r
Mea
sure
men
tE
rror
Sou
rces
Sys
tem
Sta
te
(des
ired
but n
ot
know
n)
Ext
erna
l C
ontr
ols
Obs
erve
d M
easu
rem
ents
Opt
imal
E
stim
ate
of
Sys
tem
Sta
te
Sys
tem
Err
or S
ourc
es
Sys
tem
Bla
ck B
ox
•S
yste
m s
tate
can
not b
e m
easu
red
dire
ctly
•N
eed
to e
stim
ate
“opt
imal
ly”
from
mea
sure
men
ts
5Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
All
Ro
ads
Lea
d f
rom
Gau
ssA
ll R
oad
s L
ead
fro
m G
auss
1809
“ …
sin
ce a
ll ou
r m
easu
rem
ents
and
obs
erva
tions
are
no
thin
g m
ore
than
app
roxi
mat
ions
to th
e tr
uth,
the
sam
e m
ust b
e tr
ue o
f al
l cal
cula
tions
res
ting
upon
them
, and
the
high
est a
im o
f al
l com
puta
tions
mad
e co
ncer
ning
con
cret
e ph
enom
enon
mus
t be
to a
ppro
xim
ate,
as
near
ly a
s pr
actic
able
, to
the
trut
h. B
ut th
is c
an b
e ac
com
plis
hed
in
no o
ther
way
than
by
suita
ble
com
bina
tion
of m
ore
obse
rvat
ions
than
the
num
ber
abso
lute
ly r
equi
site
for
the
dete
rmin
atio
n of
the
unkn
own
quan
titie
s. T
his
prob
lem
ca
n on
ly b
e pr
oper
ly u
nder
take
n w
hen
an a
ppro
xim
ate
know
ledg
e of
the
orbi
t has
bee
n al
read
y at
tain
ed, w
hich
is
afte
rwar
ds to
be
corr
ecte
d so
as
to s
atis
fy a
ll th
e ob
serv
atio
ns in
the
mos
t acc
urat
e m
anne
r po
ssib
le.”
-F
rom
The
ory
of th
e M
otio
n of
the
Hea
venl
y B
odie
s M
ovin
g ab
out
the
Sun
in C
onic
Sec
tions
, Gau
ss, 1
809.
6Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Est
imat
ion
Bas
ics
Est
imat
ion
Bas
ics
•Pr
oble
m s
tate
men
t–
Obs
erva
tion
rand
om v
aria
ble
X
(Giv
en)
–T
arge
t ran
dom
var
iabl
e
Y
(U
nkno
wn)
–Jo
int p
roba
bilit
y de
nsity
f(
x,y)
(Giv
en)
•W
hat i
s th
e be
st e
stim
ate
yopt =
g(x)
whi
ch m
inim
izes
the
expe
cted
mea
n sq
uare
err
orex
pect
ed m
ean
squa
re e
rror
betw
een
yoptan
d y
?
•A
nsw
er: C
ondi
tiona
l Mea
ng(
x)=
E(Y
|X=
x)
•E
stim
ate
g(x)
can
be p
oten
tially
non
linea
r an
d un
avai
labl
e in
clo
sed-
form
.
•W
hen
X a
nd Y
are
join
tly G
auss
ian
g(x)
is li
near
.
•W
hat i
s th
e be
st li
near
est
imat
e yli
n =W
xw
hich
min
imiz
es
the
mea
n sq
uare
err
or ?
7Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Wie
ner
Filt
erW
ien
er F
ilter
1940
•W
iene
r-H
opf
Solu
tion:
W =
RY
X (
Rxx
)-1
•In
volv
es m
atri
x in
vers
ion
•A
pplie
s on
ly to
sta
tiona
ry p
roce
sses
•N
ot a
men
able
for
onl
ine
recu
rsiv
e im
plem
enta
tion
.
Sp
an
(X)
Y
Yli
n
8Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Kal
man
Kal
man
Filt
erF
ilter
1960
•T
he e
stim
ate
can
be o
btai
ned
recu
rsiv
ely.
•C
an b
e ap
plie
d to
non
-sta
tiona
ry p
roce
sses
.•
If m
easu
rem
ent n
oise
and
pro
cess
noi
se a
re w
hite
an
d G
auss
ian,
then
the
filte
r is
“op
timal
”.–
Min
imum
var
ianc
e un
bias
ed e
stim
ate
•In
the
gene
ral c
ase,
the
Kal
man
filte
r is
the
best
lin
ear
estim
ator
am
ong
all l
inea
r es
timat
ors.
•It
can
be
exte
nded
to n
onlin
ear
estim
atio
n –
exte
nded
Kal
man
filt
er (
EK
F)
1 11
11
kk
kk
kk
kk
xA
xw
yM
xv
+ ++
++
=+
=+
Pro
cess
Mod
el:
Mea
sure
men
t Mod
el:
ST
AT
E-S
PA
CE
MO
DE
L
9Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Wh
y D
o W
e N
eed
KF
?W
hy
Do
We
Nee
d K
F ?
A p
redi
ctio
n/co
rrec
tion
algo
rithm
use
d fo
r st
ate
estim
atio
nun
der
stoc
hast
icen
viro
nmen
t
KF
is u
sed
for:
Est
imat
ion
of a
val
ue w
here
mea
sure
men
ts a
re m
ade
in n
oisy
env
ironm
ents
(no
isy
inpu
t is
conv
erte
d to
less
no
isy
data
)
Pre
dict
ion
of ti
me
vary
ing
varia
ble
base
d on
a l
inea
r st
ate
mod
el (
base
d on
pre
viou
s m
easu
rem
ents
)
Dat
a fu
sion
(use
d fo
r ob
tain
ing
an e
stim
ate
of a
val
ue
who
se m
easu
rem
ent i
s ob
tain
ed fr
om d
iffer
ent s
ourc
es)
…
10Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Ho
w D
oes
It D
o It
?H
ow
Do
es It
Do
It ?
•T
he K
alm
anfil
ter
itera
tes
betw
een
two
step
s–
Tim
e U
pdat
e (P
redi
ct)
•P
roje
ct c
urre
nt (
k-1)
sta
te a
nd c
ovar
ianc
e fo
rwar
d to
the
next
tim
e st
ep (
k), t
hat i
s,
com
pute
the
next
a p
riori
estim
ates
.–
Mea
sure
men
t Upd
ate
(Cor
rect
)•
Upd
ate
the
a pr
iori
quan
titie
s us
ing
curr
ent
time
step
noi
sy m
easu
rem
ents
, tha
t is,
co
mpu
te th
e a
post
erio
ri es
timat
es.
•C
hoos
e K
k(K
alm
anga
in)
to m
inim
ize
erro
r co
varia
nce
()
ˆˆ
ˆk
kk
kk
kx
xK
zM
x−
−=
+−
11Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Co
mm
on
Ap
plic
atio
ns
Co
mm
on
Ap
plic
atio
ns
•T
rack
ing
mis
sile
s –
Tra
ckin
g m
ovin
g ob
ject
s•
GP
S•
Com
pute
r vi
sion
–E
xtra
ctin
g lip
mot
ion
from
vid
eo•
Dat
a fu
sion
/inte
grat
ion
–In
tegr
atio
n of
spa
tio-t
empo
ral v
ideo
seg
men
ts•
Rob
otic
s–
Rob
ust e
stim
atio
n an
d se
nsor
dat
a no
ise
redu
ctio
n•
Sta
te a
nd p
aram
eter
est
imat
ion
for
mon
itorin
g,
faul
t dia
gnos
is a
nd c
ontr
ol•
Dat
a sm
ooth
ing
and
curv
e fit
ting
•…
…
12Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Lin
ear
Lin
ear
Pla
nt
and
Mea
sure
men
t M
od
els
Pla
nt
and
Mea
sure
men
t M
od
els
13Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Est
imat
ion
Pro
ble
m
Est
imat
ion
Pro
ble
m --
Ob
ject
ive
Ob
ject
ive
Ob
ject
ive:
To
find
an e
stim
atio
n of
the
nst
ate
vect
or
rep
rese
nted
by
, a
line
ar fu
nctio
n of
th
e m
easu
rem
ents
th
at m
inim
izes
the
wei
ghte
d m
ean-
squa
red
erro
r
whe
re M
is a
ny s
ymm
etric
non
nega
tive-
defin
ite
wei
ghtin
g m
atrix
.
So
luti
on
?
ˆˆ
[]
[]
Tk
kk
kE
xx
xx
−−
kxˆ kx
,...,
,i
kz
z
14Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Th
e D
iscr
ete
Th
e D
iscr
ete
-- Tim
e T
ime
Kal
man
Kal
man
Filt
erF
ilter
A r
epre
sent
atio
n fr
om G
A01
15Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Th
e D
iscr
ete
Th
e D
iscr
ete
-- Tim
e T
ime
Kal
man
Kal
man
Filt
erF
ilter
A r
epre
sent
atio
n fr
om B
LK01
16Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Th
e D
iscr
ete
Th
e D
iscr
ete
-- Tim
e T
ime
Kal
man
Kal
man
Filt
erF
ilter
17Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Th
e T
he
Kal
man
Kal
man
Filt
er C
ycle
Filt
er C
ycle
Tim
e U
pdat
e(P
redi
ct)
Mea
sure
men
t Upd
ate
(Cor
rect
)
Adj
usts
the
curr
ent s
tate
est
imat
e
Pro
ject
s th
e cu
rren
t sta
te e
stim
ate
18Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Seq
uen
ce o
f O
per
atio
n o
f D
KF
Seq
uen
ce o
f O
per
atio
n o
f D
KF
19Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
A S
imp
le E
xam
ple
A
Sim
ple
Exa
mp
le --
Tra
ckin
gT
rack
ing
1k
kk
xF
xw
+=
+
kk
kz
Hx
v=
+
1
10
0
10
00
00
1
00
01
xx
xx
ky
y
yy
kk
pp
tp
pw
pp
t
pp
δ
δ
+
=+
10
00
00
10
x xx
ky
yk
yk
p pp
vp
p
p
=
+
X
Y(
,,
,)
kx
xy
yx
pp
pp
20Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Act
ual
Est
imat
e
A S
imp
le E
xam
ple
A
Sim
ple
Exa
mp
le ––
Est
imat
ed v
s. A
ctu
alE
stim
ated
vs.
Act
ual
21Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Th
e D
iscr
ete
Th
e D
iscr
ete
-- Tim
e T
ime
Kal
man
Kal
man
Filt
erF
ilter
An
Exa
mpl
e
22Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Th
e D
iscr
ete
Th
e D
iscr
ete
-- Tim
e T
ime
Kal
man
Kal
man
Filt
erF
ilter
An
Exa
mpl
e (c
ont)
–m
ore
exam
ples
in o
verh
eade
ran
d M
AT
LAB
23Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Th
e D
iscr
ete
Th
e D
iscr
ete
-- Tim
e T
ime
Kal
man
Kal
man
Filt
erF
ilter
Mea
s. u
pdat
e (c
orre
ctio
n)
Tim
e up
date
(p
redi
ctio
n)
24Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Kal
man
Kal
man
Filt
er v
s. R
LS
Filt
er v
s. R
LS
-R
LS(f
rom
F7-
1: S
yste
m Id
entif
icat
ion
cour
se)
25Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Kal
man
Kal
man
Filt
er v
s. R
LS
Filt
er v
s. R
LS
-K
F fo
r pa
ram
eter
est
imat
ion
(fro
m F
7-1:
Sys
tem
Iden
tific
atio
n)
26Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Th
e C
on
tin
uo
us
Th
e C
on
tin
uo
us --
Tim
e T
ime
Kal
man
Kal
man
Filt
erF
ilter
27Le
ctur
e 1
Lect
ure
1Le
ctur
e N
otes
on
Lect
ure
Not
es o
n E
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
nE
stim
atio
n a
nd
Sen
sor
Info
rmat
ion
Fu
sio
n, b
y Y
. M. Z
hang
(A
UE
), b
y Y
. M. Z
hang
(A
UE
)
Tex
tbo
oks
Rec
om
men
ded
tex
tbo
oks
Rec
om
men
ded
tex
tbo
oks
[BL
K01
] Yaa
kov
Bar
-Sha
lom
, X. R
ong
Li, T
hiag
alin
gam
K
iruba
raja
n, E
stim
atio
n w
ith A
pplic
atio
ns to
Tra
ckin
g an
d N
avig
atio
n, W
iley-
Inte
rsci
ence
, Jun
e 8,
200
1, IS
BN
: 04
7141
655X
.[H
M04
] Dav
id L
. Hal
l, S
onya
A. H
. McM
ulle
n, M
athe
mat
ical
T
echn
ique
s in
Mul
tisen
sor
Dat
a F
usio
n, A
rtec
hH
ouse
P
ublis
hers
; 2nd
edi
tion,
Mar
ch 1
, 200
4, IS
BN
: 158
0533
353.
Op
tio
nal
tex
tbo
oks
/ re
fere
nce
sO
pti
on
al t
extb
oo
ks /
refe
ren
ces
[GA
01]
Moh
inde
rS
. Gre
wal
, Ang
us P
. And
rew
s, K
alm
anF
ilter
ing:
The
ory
and
Pra
ctic
e U
sing
MA
TLA
B, 2
nd E
ditio
n,
Wile
y-In
ters
cien
ce, J
anua
ry 2
001,
ISB
N: 0
-471
-392
54-5
.[H
L01
] Dav
id L
. Hal
l, Ja
mes
Llin
as, H
andb
ook
of M
ultis
enso
r
Dat
a F
usio
n, C
RC
Pre
ss J
une
20, 2
001,
ISB
N: 0
8493
2379
7.28
Lect
ure
1Le
ctur
e 1
Lect
ure
Not
es o
n Le
ctur
e N
otes
on
Est
imat
ion
an
d S
enso
r In
form
atio
n F
usi
on
Est
imat
ion
an
d S
enso
r In
form
atio
n F
usi
on
, by
Y. M
. Zha
ng (
AU
E)
, by
Y. M
. Zha
ng (
AU
E)
Rea
din
gs
The
dis
cret
e-tim
e K
alm
anfil
ter
(DK
F):
•B
LK
01: S
ectio
ns 5
.1-5
.3
•G
A01
: Sec
tions
4.1
-4.2
The
con
tinuo
us-t
ime
Kal
man
filte
r (C
KF
):•
BL
K01
: Sec
tions
9.1
-9.2
•G
A01
: Sec
tions
4.3
The
ext
ende
d K
alm
anfil
ter:
•B
LK
01: S
ectio
ns 1
0.1-
10.3
•G
A01
: Sec
tions
5.1
-5.7
Intr
oduc
tion
to K
alm
anfil
ter:
•
Wel
ch &
Bis
hop:
Pap
er; S
lides
•M
aybe
ck’s
book
(19
79)_
Cha
pter
1
Recommended
