ChSTU repository
ChSTU repository preserves and enables easy and open access to all types of digital content including text, images, moving images, mpegs and data sets
Learn More
Please use this identifier to cite or link to this item:
https://er.chdtu.edu.ua/handle/ChSTU/8356Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Трембовецька, Руслана Володимирівна | - |
| dc.contributor.author | Гузьман, Іван Іванович | - |
| dc.date.accessioned | 2026-03-14T12:10:20Z | - |
| dc.date.available | 2026-03-14T12:10:20Z | - |
| dc.date.issued | 2025-12-15 | - |
| dc.identifier.uri | https://er.chdtu.edu.ua/handle/ChSTU/8356 | - |
| dc.description.abstract | У роботі досліджено методи мінімізації навігаційних помилок безплатформної інерціальної навігаційної системи шляхом її комплексування з оптико-електронними системами орієнтації та навігації та застосування алгоритмів корекції точнісних характеристик. | uk_UA |
| dc.description.abstract | The work investigates methods for minimizing navigation errors of a strapdown inertial navigation system by integrating it with optical-electronic orientation and navigation systems and applying algorithms for accuracy correction. | uk_UA |
| dc.language.iso | uk | uk_UA |
| dc.subject | безплатформна інерціальна навігаційна система | uk_UA |
| dc.subject | оптико-електронні системи орієнтації та навігації | uk_UA |
| dc.subject | мінімізація навігаційних помилок | uk_UA |
| dc.subject | комплексування навігаційних систем | uk_UA |
| dc.subject | фільтр Калмана | uk_UA |
| dc.subject | корекція точнісних характеристик | uk_UA |
| dc.title | Дослідження інерціальної навігаційної системи | uk_UA |
| dc.type | Master Thesis | uk_UA |
| Appears in Collections: | 174 Автоматизація, комп'ютерно-інтегровані технології та робототехніка (Робототехнічні системи та автоматизація) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Диплом-магистр_Гузьман І.pdf Restricted Access | КРМ Гузьман І. | 7.63 MB | Adobe PDF | View/Open Request a copy |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
Extracted text
UEPKACGkiii, EPKABHHÜ TENHOJOTHHÜS HBEPCITET
DAKY IbTETE EKTPOHHU TEXHO.JOTT0
ABTOTPAICHOPTS TA MAIIHOEYJIYBAHIA
KAOEPA IIPUJAIOEYJV BAHHA, MEXATPOHIKH TA
KOMIIIOTEPIBOBAHUX TEXHOJJOr0
JoryuieHO IO 3axHCTY
3asilyBay Kahe1pu IIMKT
MakcuM 5OHJAPEHKO
2025 p.
IIOACHIOBAJILHA3 AIIMCKA
A0 KBAJTiþikauiHnoï poõoTH
MaricTpa
Ha TeMy «lociiIKeHHA 0HepujaIbHOÈ HaBirauiGHOÈ CHCTeMM »
KBaioikauiHHa pooora Marictpa MiCTHTb pe3yJIbTaTH BJIaCHHX I0CJIKEHb.
BKopeTaHHA ineH, pesyabraris i TeKCriB iHIHX asTopiB MaroTb nocHJaHHA Ha
BIII1OBIJHe KEpeio IBaH IY35MAH
BuKOHaB 3/106y Bay BHIOi OCB0TH OCB0THboro
CTYIIeHA (MaricTp» 2 Kypcy, rpynuM PCA-47
3a cneuianbH0CTKO 174 «ABTOMaTH3auis,
KOMIT IOTepHO-0HTerpOBaHi TeXHOJOriï ra
poooTOTeXH0Ka», 3a oCBÍTHbOO IIDorpamOO
«PoõoTOTeXHI4HI CHCTeMM Ta aBTOMaTH3au>
IBan Iy3bMAH
KepisMK Pycnana TPEMBOBEL|bKA
PeueH3eHT BiKTop AHTOHIOK
Yepkacn- 2025 poKy
4EPKACLKHG IEPKABHH TEXHOJIOTIYHII YHIBEPCHTET
(nOBHC HalixeryBaAHA BHNtoro FaBTATbHOro 3AKIa)
DakyIbTeT eneKmpoHHux mexHO.I02iù, aGmompaHCNopmy ma uauUHO6vdv6aHHA
Kapeapa npuiadobyoysaHHA, MexampoH0KUM a KOMN TOmepu306AHUX mexHO102iù
OcBiTH0ú piBeHb MaACmp
CneriantbH0CTL 174 «AemoMamusayia, Koun' omepHO-iHIMe•pOGani mexHa02ii ma poomomerHIKO »
OcBiTH iporpaMa «PobomomexHI4HÈ CUCmeMu ma a6mOMamu3ayiA)
(uuipp i HaIsa)
3ATBEPJIKYIO
3asiuyBa4 Kajexpu IIMKT
MakCHM BOHLAPEHKO
(2 2025 poKy
3A B I AH HA
HA KBAJIIOIKAIIÄHY POBOTY MATICTPA
Ty3bMaHa lsaHa lsaHOGUHa
(npi3BuIme, iM'A, Ilo 6aTbKOB0)
1. TeMa poboTH: JocniKeHHA 0HepLiaibHOÏ HaB0raiYHHoi cHCTeMH
HayKoBH KepiBHHK po6oTH TpemÑoBeII.KA PycIaA Bo.oMINMIp0BHA, A-D TexH, HaVK, poVecop kadbepa
IIMKT
(pisBKne, ix 'a, no 6aTbKOB0, Hay KORHÍÍc TVtb, ByeHe 3BAHHS)
3aTBepIKEHI HaKa3OM BHIMOrO HaB4aJIbHoro 3akJiany Bin "I5° BepeCHA 2025 poky No 261/03-03
2. CTpOK NO1aHHs 3BOp oÑ0TH 15 rpyHA 2025 poKy
3. Mera 10cainKeHHA; po3poQka MareMaTHYHHX MOIEIeH Ta anropuTM0B VHKL|OHYBaHHA
GesuuaTdopMHOi 0HepuianB HOÈ HABiraui~Hoi cHCTeMH Ta 10cniKEHHA ejexTHBHOcTi i) KopeKUï 3a
noOMOroo oTHKO-eJIEKTpOHHOi CHCTeMU opi¬HTaii l HaBirauii 3 BHKOPACTaHHAM HiJbTpa KaMaHa.
06 'eKm docnidICeHHA esJIaTopMHa iHepuiaTbHa HaBiraui}Ha CHCTeMa, nooyIOBAHa Ha 6a3i
Ja3epHHX TipocKOI0B.
IIpedMem dociiònceHHA: MeTONH KOpeKii HaBirauuHHHX napameTp0B SIHC Ta aJ1ropuTMH
KOMILIeKcVBaHHA bIHC3 ONTHKO-¬NEKTDOHHOO CHCTeMOKO Op1¬HTauiï Ta HaBiraui
Memody docaiðceHb y poÑoTi BMKOPHCTAHO aHaiTH9HH orIAA iCHyoYMX CHCTeM 3 OnTHYHOIO
KODeKUiCHO, MaTeMaTH4HHH 0HC OITHHHX CHCTEM Ta aropuTMIB ix po00TH. orHc nDHHUHnia
oniewrauii Ta HaBirauii Ha õasi onTHKOeJEKTPOHHAX 3acoóiB, a Takox CHHTes pinbTpa KanMaHa LIA
OuiHHOBaHHA IIOXHÕOK IHTerpOBaHoro HaBIrali`HOro KOMJeKCV
4 CrpyeTYpa ä o6car po6oTH. KBamoikaui}Ha poóora Maricrpa CcKlanacTbcA 3i BCTyIY, 4OTHPLOx
po3IiJIIB, BHCHOBKIB, CIIHCKV BHKOPHCTaHHX IKepen.
5. IlpexenTauiï Ha 13 cIañaax.
6. Koncy.b TaHTHp ouiiB KBaipikauiinoi po6oTH Maricrpa
Ilinnc, naTa
Po3un IIpi3BMLle, iHisjaIH Ta nocaFa 3aByaHHA
3aBAaHHA
KOHCYMbTaHTa npuDHAB
BH1aB
Teoperu4HIi0
MerouuHI0 TpemQOBeUbKa P.B., 1-p TeXH. Hayk,
npoecop kajenpu IIMKT
AocaianHLbKHÄ
THYKOB B.B., K-T TeXH. Hayk, 10.,
HopMokoHTPO.Tb
NOL. Kahexpu IIMKT
7. Mara BHJaYi 3aBlaHHa "15® BepeCHa 2025 poKy
KAJIEHIAPHM IIJIAH
No CTpoK BHKOHAHHA eTaIIIB
HasBa erariB KBaNioikauiHHoi poboTH Marictpa Ilpumina
3/1 poboTH
TeopeTHuHMÄ pO3ain 15.09.25 -05.10.25 BHK
2 TexHoJIOriHHÄ po3AiJI 06.10.25 -26.10.25 BHK
3 locaiNHHIbKHÄ P031iI 27.10.25-23.11.25 BHK
4 OopMIEHHA IOACHIOBaJTbHOÏ 3anuCKH 24.11.25 -07.12.25 BHK
O¢opMIeHHA CYipOB0JHO0 IOKY MeHTaLI0i 01.12.25 - 15.12.25 BHK
6 OdopmneHHA npesEHTaLii 08.12.25 15.12,25 BHK
7 Poo0Ta Han noOBIIIIO 08.12.25 - 15.12.25 BHK
MarictpauT IBaH TY3LMAH
TIiIHC (opi3B4Êe Ta iHiian)
KepisHHK poóoTH Pycnana TPEMBOBEIILKA
(up13BHUle Ta iHIIjaIn)
3
.
.................................................................................. 2
5
1.
8
1.1. 8
1.2. '
13
1.3.
15
1 20
2.
21
2.1. '
21
2.2.
30
2.3. 41
2 46
3. - 48
3.1. -
48
3.2. - 50
3.3.
- 52
3 56
4.
58
4.1. 60
4
4.2. 63
4 73
75
77
5
,
XX ,
- .
,
’ .
1955 ,
( ).
( ) —
.
, , ’
( ).
, .
- .
—
( ).
,
.
, .
.
. , ,
. , ,
.
6
, « - »
.
-
( ).
.
—
. ,
.
, ( )
, .
( ) .
,
’ ( )
, - .
,
, ,
.
,
, .
,
.
:
•
;
• ;
7
•
;
•
- ;
•
;
•
.
’
, .
.
.
,
, -
,
.
8
1
1.1 .
. :
( )
,
’ ( ) ,
.
—
.
’ .
.
,
,
’ .
, .
(
), .
.
9
, : ,
.
, :
• :
.
:
( ) 3D-
( ' ).
• :
,
.
• :
.
• :
, .
• ' :
,
.
,
—
.
’ : , , , ( ,
), .
:
• ( ).
• ,
( , )
’ .
10
• ,
.
’
,
.
( )
, ,
, .
’
( ) ( , )
,
.
( )
( ), ' .
:
1. :
, .
2. ( ):
’ ,
.
:
• : — ,
, ,
.
.
• :
( ).
.
11
• :
.
' .
-
- ( )
, '
(" ") . -
.
, ,
.
, , ,
' . '
.
:
1.
- .
2.
, .
3.
.
,
.
.
, 50
, 5 7
.
12
, :
,
.
( ) ’
.
(GPS/GLONASS) ( ).
- .
MEMS- ( )
,
.
,
:
• GPS ,
( , ' )
.
• ( ),
,
.
•
.
(GPS )
:
1. : (50
), GPS ( 1 ),
' .
2. : GPS,
.
13
3. :
,
.
,
,
,
.
1.2 '
,
, ,
.
’
, .
, —
- —
.
( ).
, . ,
( ) :
,
3–9 .
, ’
.
14
40 100 ,
( 10 ) ,
.
:
,
.
— '
,
.
( ),
.
- (
« »).
.
.
( , , ),
.
1 5 .
,
.
,
.
, ' ,
. ' ,
,
.
15
1.3
'
,
' . (
Visual-Inertial Odometry — VIO)
,
.
,
' ( )
( ).
: ’ (Loose Coupling), ’
(Tight Coupling) (Ultra-Tight Coupling).
’ (Loose Coupling)
. ,
,
(Pose Estimation).
- ( ),
2.
• : , (
),
.
• : ;
, '
( , ).
16
’ (Tight Coupling)
’ ' " "
.
,
(2D- ) .
'
(landmarks).
• : .
,
( , 2–3 ),
.
• : ,
( ),
.
'
VIO, MSCKF (Multi-State Constraint Kalman
Filter) .
:
' ,
, " " .
(Filtering-based methods)
(EKF).
. EKF
.
MSCKF (Multi-State
Constraint Kalman Filter), Mourikis Roumeliotis.
, 3D-
. '
17
(poses)
" ".
, ' .
:
• (Real-time performance).
• (
).
• .
(Optimization-based / Smoothing methods)
,
' , ,
Bundle Adjustment (BA) Graph SLAM.
,
(Factor Graph), ,
— , (IMU
factors) (Reprojection errors).
" " (Sliding Window
Optimization).
:
• OKVIS (Open Keyframe-based Visual-Inertial SLAM)4:
'
IMU.
• VINS-Mono5: ,
,
(Loop Closure).
• ORB-SLAM36: ,
, RGB-D
, ORB.
:
18
• " " (re-linearization) ,
.
• .
• ( ,
).
+
, :
1. (Scale Ambiguity):
( )
.
,
( / 2).
(bias) ( ) '
.
2. (Time Synchronization):
(100–1000 ),
(30–60 ).
IMU
. (time offset)
.
3. (Initialization):
( VINS) .
(biases) .
" " (In-motion alignment),
' .
19
4. " " (Lever Arm):
,
(translation) (rotation). (Extrinsic
parameters) .
,
" " 9.
: Deep Learning
, 2018-2020
, (Deep Learning)
.
" " MEMS- ,
.
, InertialNet AI-IMU
.
' ( , ),
' ( , ), VIO.
,
— —
.
(Graph SLAM)
, ,
. (
EKF Error-State Kalman Filter)
,
. , MEMS-
,
.
20
1
,
:
1.
- .
2.
( ). ,
,
.
3. - ( )
.
, ,
,
.
4. ( )
, —
.
5.
( ) ,
.
,
.
21
2
2.1 '
( ) : « » « » (
« »). « »
,
’ .
’
, .
.
(
). , ,
, ( ),
.
« » ’
« ».
,
.
,
. (
- , - ),
22
: ,
( ) . ,
, ,
.
( ),
( ,
- , ).
,
’ O X1 Y1 Z1
O .
:
, ϑ0 γ 0 .
. 2.1 ’
23
g ’ .
:
gx1 = g cosϑ0 sin γ 0
g y1 = −g sinϑ0 (2.1)
gz1 = −g sinϑ0 cosγ 0
(2.1),
:
g
ϑ = arcsin − y1
0 g
g (2.2)
γ 0 = arctg − x1
gz1
,
O :
ωξ = 0
ωη = Ω cosϕ (2.3)
ωζ = Ω sinϕ
Ω = 7.29 ⋅10−5 / — , —
. , ( )
:
(2.4)
ψ 0 ,
( )
ϑ0 :
24
ω −ω sinϑ
Ψ0 = arccos y1 ζ 0 (2.5)
ωη cosϑ0
(2.5) .
’
ϑ0 γ 0 :
c gx1
31 = cosϑ0 sin γ 0 = −
g
g
c y1
32 = sinϑ0 = − (2.6)
g
c33 = cosϑ0 cosγ gz1
0 = −
g
±0.5° c31,c32 ,c33 , (2.4)
:
(2.7)
, :
c12 = −cosϑ0 sin Ψ0 = c23 ⋅c31 − c21 ⋅c33 (2.8)
(2.6)–(2.8)
:
25
Ψ = arctg c12 = arctg ωz1gx1 −ωx1gz1
0 (2.9)
c22 ωy1g −ωζ g y1
(2.9) ’
. ψ 0 ,ϑ0 ,γ 0
, .
, .
.
(2.2) (2.9). Δnx1,Δny1,Δnz1 — , Δωx1,Δωy1,Δωz1 —
. ,
:
∂Fϑ g
ϑ 0
0 = g = − y1
∂g y1
g 2
y1
g 1+ y1
g
∂Fγ 0 ∂F
γ = g + γ 0 gx1 gx gz1
0 g = − +
∂g x1 ∂g z1 2 2 2
x1 z1 g g
x1 z1 + gx1
g 1+ (2.10)
z1 gz1
Ψ = ∂FΨ0 c + ∂FΨ0 c = − c12 − c12 c22
0 ∂c 12 ∂c 22 2 2
12 22
c 1+ c12 2
z1 c 1+ c12
c 22
22 c22
(2.10)
Δϑ0 Δγ 0 .
,
26
. , ,
10−2 g 0.
. 2.2 ,
,
. 2.3 , X1 ,
Δγ 0
27
Δψ 0 ,
,
. ,
.
( ) :
(cos Ψ ω + sin Ψ ω )
Ψ0 = − 0 x1 0 y1 (2.11)
Ωcosϕ
Ω — , ϕ = 57.27° — , Δω —
, ψ 0 — .
. 2.4 Δψ 0
:
Ψ 0 = − tgϕ ⋅(cos Ψ0 + sin Ψ0 ) ⋅ ga + ga cos Ψ 0 sin Ψ 0 (2.12)
28
Δgx1 = Δgy1 = Δgz1 = Δga — .
. 2.5 Δψ 0
( Δga = const , Δω = const ):
(cos Ψ0 + sin Ψ0 ) ω
+ tgϕ ⋅(cos Ψ + sin Ψ )⋅ g
Ψ = − Ωcosϕ 0 0 a (2.13)
+ ga cos Ψ0 sin Ψ0
29
in
ar
cs
ec
E rr
or
of
. 2.6 Δψ 0
'
in
arc
se
c
E rr
or
of
. 2.7 Δψ 0
ψ 0
(2.10)
30
.
, .
, ,
0.005 ÷ 0.01 / ,
( 3 ).
,
.
2.2
« »
’
,
.
, . 2.8.
. 2.8
31
,
:
.
1. ( )
.
’ ,
.
2. ( ) ,
, , .
’
( ) ,
.
,
.
.
,
.
’
( ),
.
’ .
’ ,
.
-
( - ),
.
.
, . 2.9.
32
. 2.9 –
ζ .
, ω
’ :
⋅ ⋅ ⋅
ω = Ψ+θ + γ (2.14)
ω ’ :
⋅ ⋅
ω = − Ψ cosθ sin γ +θ cosγ
⋅ ⋅
ω = γ + Ψsinθ (2.15)
⋅ ⋅
ω = Ψ cosθ cosγ +θ sinγ
’ ψ ,ϑ,γ .
:
⋅ ⋅
γ = ωy − Ψ sinθ (2.16)
33
. cosγ , sinγ ,
:
⋅ ⋅ ⋅ ⋅ ⋅
ωx cosγ +ωz sinγ = −Ψcosθ sinγ cosγ +θ cos2 γ +Ψcosθ cosγ sinγ +θ sin2 γ =θ (2.17)
, −sinγ , cosγ
, :
(2.18)
:
⋅ ⋅
γ = ωy − Ψ sinθ
⋅
θ = ωx cosγ + ωz sin γ (2.19)
⋅
Ψ cosθ = −ωx sin γ +ωz cosγ
:
⋅
Ψ = (ωz cosγ −ωx sin γ ) 1 (2.20)
cosθ
, :
⋅ ⋅
γ =ωy −Ψsinθ =ωy −(ωz cosγ −ωx sinγ ) tgθ (2.21)
- :
34
(2.22)
(2.22)
MATLAB - .
, :
(2.23)
ω — , A— , ε — .
. 2.10
(2.22)
35
.
( ) ϑ = ±90° . «
», . ,
.
( )
( ).
:
(2.24)
da — , ω p — .
dt
:
(C SP )t dAs = dAs + (ω PS
P ×) ⋅ Ap (2.25)
dt dt
CSP — S P .
S ξηζ , P xyz ,
Ωx :
0 −ω ps
3 p ω ps
2 p
(ω ps x) = ω ps ps
p 3 p 0 −ω1p (2.26)
−ω ps ps
2 p ω1p 0
(2.25), :
(2.27)
36
:
dC SP
= C SP ⋅ (ω PS
p x) (2.28)
dt
, :
dAs dA
= C SP ⋅ p + (ω PS
p ×) ⋅ As (2.29)
dt dt
0 −ω ps ps
3 p ω2s
(ω ps
s x) = ω ps ps
3s 0 −ω1s (2.30)
−ω ps
2s ω ps
1s 0
:
SP
(ω PS
p x) = dC t
⋅ (C SP ) (2.31)
dt
dC SP
= (ω PS
s ×) ⋅C SP (2.32)
dt
(2.23).
37
. 2.11
(2.32)
,
’ W F :
W = F (2.33)
m
F F ( , )
G :
W = F + G (2.34)
m
( ) a = F .
m
G g (R,ϕ)
m
-
R :
2
W = d R
2 (2.35)
dt
38
(2.33),
:
d 2R
2 = a + g (R,ϕ ) (2.36)
dt
,
a .
:
dV = a + g (R,ϕ ) (2.37)
dt
dV = V , (2.38)
dt
V — . ,
Ω ,
:
(2.39)
(2.40)
:
(2.41)
39
u — , λ — .
:
(2.42)
’ , :
Wξ = Ve −VN (u sinϕ +ωζ ) + u cosϕ + V sin Ψ V
R ζ
3
V V
Wη = V 2 N ζ
N −Ve (u sinϕ +ωζ ) + Ru cosϕ sinϕ + (2.43)
R3
(Vcos Ψ)2 + (Vsin Ψ 2
Wζ = V − )
ζ − 2Vu cosϕ sin Ψ − Ru2 cos2 ϕ
R3
, ’ :
Wx = ax + gx
Wy = ay + g y (2.44)
Wz = az + gz
’ ,
.
Wξ = (ax + gx )(cosγ cos Ψ − sin γ sinϑ sin Ψ) − (ay + gy )cosϑ sin Ψ + (az + gz )
(sin γ cos Ψ − cosγ sinϑ sin Ψ)
Wη = (ax + gx ) (cosγ sin Ψ +sinγ sinϑ cosΨ) +(ay + gy )cosϑ cosΨ +(az + gz )(sinγ sin Ψ + cosγ sinϑ cosΨ) (2.45)
Wζ = (ax + gx )sin γ cosϑ + (ay + g y )sinϑ + (az + gz )cosγ cosϑ
40
(2.45) ,
( ϕ,λ ) :
(2.46)
:
• Tk = 600 .
• : - 1- , h = 0.005 .
1: V = 200 / (720 / ),
(ϕ = 50.21° ,λ = 30.3° ), ψ 0 = 0° .
. 2.12 (2.44)
(2.46) ’ , ψ °
0 = 0
41
2: , ψ 0 = 30° .
. 2.13 (2.44)
(2.46) ’ , ψ = 30°
0
2.3
,
, .
.
.
, : ( ), ,
.
.
. ’ ,
42
.
’
.
, .
( ).
, (
) , .
,
.
,
Δa , —
:
δ p = badtdt = batdt = 1 bat
2 (2.47)
2
(2.47) ,
.
: ’
200 / 3 (10800 ).
10−4 g . g = 9.8 / 2 R = 6400000 ,
0.01786 , 1.023°.
Δω
, :
43
δθ = bg dt = bgt (2.48)
.
g ⋅Δα .
:
δ p = gbgtdtdt = 1 gb t 2dt = 1
g gb t3
g (2.49)
2 6
, ,
, ,
.
: 0.005 / ( 2.42 ⋅10−8 / )
3 0.007793 ,
0.45°.
, ,
.
N , h ,
( , ).
:
δΨ = hN Ψmϑmγ ω N +1
m sin(ε − N π ) (2.50)
2
, .
44
. 2.14
. 2.15
. 2.16
45
. 2.17
:
X = Fx + Bu (2.51)
Y = Hx (2.52)
x = [Δλ,Δϕ,Δh,ΔvN ,ΔvE ,ΔvD ,Δα ,Δβ ,Δγ ]T —
( , , ), F — , B
— , u — (
).
F ’
.
.
’ , 200 / 4 ,
10−4 g 0.01 / ,
.
46
. 2.18
2
1.
.
.
2. ( ,
)
, .
3.
( ), .
.
47
4. '
, .
5.
( ) (
).
.
6.
,
.
48
3
–
– .
,
, ( ),
.
3.1 -
- :
1. ,
- .
2. ( ) ,
’ .
.
.
,
.
.
,
49
.
( . 3.1):
• 1- : .
• 2- : (2D-
).
• 3- : (3D- ,
« » ).
• 4- : .
. 3.1
,
, .
50
« » .
, ’ ,
.
,
. ,
, .
,
.
( ), ’ .
3.2 -
- :
1. ,
- .
2. ( ) ,
’ .
.
.
,
.
.
,
51
.
( . 3.1):
• 1- : .
• 2- : (2D-
).
• 3- : (3D- ,
« » ).
• 4- : .
. 3.1
,
, .
« » .
52
, ’ ,
.
,
. ,
, .
,
.
( ), ’ .
3.3 -
,
.
,
,
’ .
( . 3.9).
. 3.9 « »
53
A :
A = D (3.1)
2 ⋅h ⋅ tan γ
2
h — ( ), γ —
( ) ( . 3.9 ).
, D
h ( . 3.9 ), (3.1) :
A = 1 (3.2)
2 ⋅h ⋅ tan γ
2
, ,
i , ( . 3.9 ),
.
, ( .
. 3.3), .
, (
α1,α2 ,α3 ) ( β1, β2 , β3 )
.
, ’
:
(Bx cosγ cosϑ + x0 )sin β1 = (Bx sinϑ + y0 ) cosα1 cos β1
(− Bx sinψ cosϑ + z0 )sin β1 = (Bx sinϑ + y0 ) sinα1 cos β1
(By (sin γ sinψ − cosγ cosψ sinϑ) + x0 )sin β2 = (By cosϑ cosγ + y0 ) cosα2 cos β2 (3.3)
(By (sinγ cosψ + cosγ sinψ sinϑ) + z0 ) sin β2 = (By cosϑ cosγ + y0 )sinα2 cos β2
(Bz (cosγ sinψ + sinγ cosψ sinϑ) + x0 )sin β3 = (− Bz cosϑ sin γ + y0 ) cosα3 cos β3
(Bz (cosγ cosψ − sinγ sinψ sinϑ) + z0 )sin β3 = (− Bz cosϑ sin γ + y0 )sinα2 cos β3
54
Bx , By , Bz — ’ ( );
ψ ,ϑ,γ — ; Bx0 , By0 , Bz0 —
’ ; α i — .
, (3.3) :
((c33 + c12 )Bx + x0 + z0 )sin β1 = (cosα1 + sinα1)(Bx c32 + y0 )cos β1
((c23 + c13)By + x0 + z0 )sin β2 = (cosα2 + sinα2 )(By c32 + y0 )cos β2 (3.4)
((c21 + c11)Bz + x0 + z0 )sin β3 = (cosα3 + sinα3)(Bz c32 + y0 )cos β3
Cij — ( ).
(3.4) ,
.
q . ’
r d :
(q ⋅ (dˆ
T T
i i − rˆ)q)x (q ⋅ (dˆ − rˆ)q) F i
i y x F i
h y
cam = ,
(q ⋅ (dˆ − rˆ)q) (q ⋅ (dˆ
=
− rˆ)q) F i ,
F i (3.5)
i z i z z z
i = 0,1,…, N f −1, N f — ,
.
(3.5)
:
−2r ∂Λ + 2r ∂Λ − 2v ∂Λ + 2v ∂Λ − q ∂Λ ∂Λ
y x y x z + qy − q ∂Λ + q ∂Λ
∂r ∂r ∂v ∂v ∂q ∂q x ∂q t ∂q (3.6)
x y x y t x y z
55
’
Λ(rx , ry , rz ,Vx ,Vy ,Vz ,qt ,qx ,qy ,qz ) , ’ ,
, . ’
, :
q q + q q
ψ = arctan 2 t z x y
1− 2(q2 2
y + qz )
ϑ = arctan (2(qtqy − qzqx )) (3.7)
q q + q q
γ = arctan 2 t x y z
1− 2(q2 2
y + qx )
C :
2 ti +1
Ri = d + vi Δt + Δt
i 0 ai − C (τ ) A(τ ) dτ dt C
2 (3.8)
ti
Δt — i - ; ai —
’ ; V0 — .
-
. 3.10.
56
. 3.10
3
1. -
' ,
.
2.
: , - ,
57
,
.
3.
,
,
.
4.
,
,
' .
5. - ,
,
.
58
4
, , -
,
.
( ) '
.
—
,
.
,
( ), - ( ).
-
,
. . 4.1.
. 4.1
, .
59
,
( )
( ) .
,
.
. 4.2.
. 4.2
,
.
' :
( ),
.
(
) .
, ,
60
' .
4.1
, ( )
' - .
, ( ),
, ,
.
.
. -
' ,
, ,
.
,
: ( )
( ).
k
:
• Φk — ( );
• Hk — ( );
• Qk — (
);
• Rk — (
);
61
• Bk — (
).
(k −1) k :
xk = Φk xk−1 + Bkuk + wk (4.1)
xk — , uk — , wk —
,
Qk .
' yk xk
:
yk = Hk xk + vk (4.2)
vk — ( )
R .
x0
wk vk .
,
: ( )
( ).
, (4.1).
'
.
62
:
P (t ) = E{[xk − xˆk ][xk − xˆ T
k ] } (4.3)
E{⋅} — .
:
x (t0 ) = xˆ0 (4.4)
P0 = E{[xˆ(t0 ) − x(t0 )][xˆ(t0 ) − x(t0 )]T } (4.5)
( ):
xˆ− +
k = Φk −1xˆk −1 + Bk −1uk −1 (4.6)
P−
k = Φ + T
k −1Pk −1Φk −1Qk −1 (4.7)
( ) Kk ,
yk :
K = P−H T H − T −1
k k k k Pk Hk + Rk (4.8)
xˆ+
k = xˆ−
k + K −
k yk − H k xˆk (4.9)
P+
k = [I − Kk H k ] P−
k (4.10)
63
. ,
, —
(Extended Kalman Filter — EKF).
,
.
4.2
.
( )
:
Ce e b
b = Cb (ωeb×)
re e
eb = veb (4.11)
ve e b e e e
eb = Cb f − 2ωei ×veb + g
δr,δV —
; ψ — ; f —
; ωie — ; δ g —
; ε —
.
- ( ).
. 4.3.
64
. 4.3
, '
, :
r c
cp = C c
e (r e e
k cp − rec )
k (4.12)
r e
p — - ;
k
r e
c — ; C e
c — ,
.
:
rc
cpk x uk − u0
rc
cp y = λ v − v
k k 0 (4.13)
rc − f
cpk z
65
u,v — ; f —
' .
ηk ,
:
rc
− f cpk x
u η rc + u0
z k uk cp z η
k u
= + = + k
v η rc η (4.14)
k vk − f cpk y vk
rc + v0
cpk z
’
( "lever arm").
(IMU)
. 4.4.
. 4.4 '
. :
66
φ = −ω e
ie ×φ − Ce
bbg
δ re
eb = δ vb
eb (4.15)
δ vb e
eb = (Cb f b )×φ − 2ω e
ie ×δ vb
eb +δ ge + Ce
bba
∇,ε — (
).
:
rc = re + Cerb
ec eb b bc (4.16)
'
Cb
c :
rc
cp = Cc
b Cb
e (re
ep − rb
k k bc ) (4.17)
(4.15, 4.16)
' (4.17) ,
:
r c c
cp = Cb (Cb
e (r e b
ep − r ) − rb )
k k bc bc (4.18)
.
:
67
f ⋅ rc
cpk x
− f 0 c 2
δ z = rc (rcpk z )
c c
k cp z − f ⋅δ r
k f ⋅ rc cp = H1δ r
k cpk
0 rc cp y (4.19)
cpk z k
(rc
cp z )2
k
' ( )
:
b b
δ rc = −C c b c e b δ reb δ reb
cpk b Cn Cb Cb (reb ×) = H
φ 2 φ 4.20)
:
δ δ rb
zk = H eb
1H2 φ (4.21)
' .
:
• : V = 200 / (720 / ).
• : . (ϕ = 50.21° , λ = 30.3° ).
• : 10−4 g .
• : 0.05° / ( ≈ 2.42 ⋅10−7 / ).
.
68
. 4.5 ’
( / ), ,
( / ), .
. 4.6
69
.
, .
. 4.7
.
1.5
1 yaw unaided
yaw SINS vs OESON
0.5
0
-0.5
-1
. 4.8
yaw error in degrees
70
( ) ,
P .
. 4.9 ’
. 4.10 ’
71
. 4.11
’
. 4.12
( - ),
.
72
. 4.13 (
)
. 4.14 ( )
73
. 4.15
( )
4
1.
.
.
2.
.
- ,
.
3.
,
74
,
.
4. -
,
.
5.
:
,
’ .
6. :
5
±0.5° .
180
.
7. ' ,
, :
2 / ,
10 .
75
-
. :
1.
.
, ' . ,
.
2.
,
.
3.
.
( , ).
4. MATLAB/Simulink
,
.
5. -
( ) .
.
6.
.
.
76
7. ’
. ,
. 25
: 4
/ , 2 ,
180 .
'
.
77
1. Groves P. D. Principles of GNSS, Inertial, and Multisensor Integrated
Navigation Systems. 2nd ed. Boston : Artech House, 2013. 780 p.
2. Jekeli C. Inertial Navigation Systems with Geodetic Applications. Berlin
: Walter de Gruyter, 2001. 360 p.
3. Titterton D. H., Weston J. L. Strapdown Inertial Navigation Technology.
2nd ed. Stevenage : IET, 2004. 584 p.
4. Grewal M. S., Andrews A. P. Global Positioning Systems, Inertial
Navigation, and Integration. 2nd ed. Hoboken, NJ : Wiley & Sons, 2007. 552 p.
5. Farrell J. A. Aided Navigation: GPS with High Rate Sensors. New York
: McGraw-Hill, 2008. 448 p.
6. Woodman O. J. An introduction to inertial navigation. Technical Report.
University of Cambridge, Computer Laboratory. 2007. UCAM-CL-TR-696. 38 p.
7. Rogers R. M. Applied Mathematics in Integrated Navigation Systems.
3rd ed. Reston, VA : AIAA, 2007. 331 p.
8. Zhang J., Li Y., Dai H. Review of MEMS inertial sensor error modeling
and compensation techniques // Sensors. 2021. Vol. 21, No. 24. P. 8348.
9. Noureldin A., Karamat T. B., Georgy J. Fundamentals of Inertial
Navigation, Satellite-based Positioning and their Integration. Berlin, Heidelberg :
Springer, 2013. 396 p.
10. Forster C., Carlone L., Dellaert F., Scaramuzza D. On-Manifold
Preintegration for Real-Time Visual-Inertial Odometry // IEEE Transactions on
Robotics. 2017. Vol. 33, No. 1. P. 1–21.
11. Chiang K. W., Duong T. T., Liao J. K. The Performance Analysis of a
Low-Cost MEMS-Based IMU/GPS Integration System for Land Vehicle Navigation
// Sensors. 2013. Vol. 13, No. 8. P. 10134–10153.
78
12. Liu Y., Li X., Zhang G., Guo Z. A review on deep learning-based
methods for inertial navigation // IET Radar, Sonar & Navigation. 2022. Vol. 16, No.
9. P. 1385–1403.
13. El-Sheimy N., Youssef A. Inertial Navigation Systems (INS). In:
Encyclopedia of Geodesy. Cham : Springer, 2016. P. 1–9.
14. Qian S., Wang H., Che Y., Xu X. A Novel Calibration and
Compensation Method for MEMS IMU Used in Land-Vehicle Navigation // IEEE
Sensors Journal. 2020. Vol. 20, No. 12. P. 6632–6643.
15. . ., . . -
// , ,
. 2018. 1. . 60–67.
16. . ., . . '
// « ».
« . ». 2012. 50. . 133–139.
17. . ., . ., . .
: . : , 2010. 236 .
18. . ., . .
: . :
" ", 2011. 176 .
19. c . ., . .
// . 2017. 3-
4. . 143–147.
20. . ., ’ . .
’ //
. 2014. . 1 (117). . 121–123.
21. . ., . .
- : . :
, 2015. 248 .
79
22. . .
- : . . ... . .
: 05.12.13. , 2019. 20 .
23. . .
- :
. . ... . . : 05.11.13. , 2017. 22 .
24. . .
: . . ... . . : 05.11.13.
, 2018. 21 .
25. Tedjini S., Fourati H., Al-Attar M. A. A comparison of nonlinear
filtering techniques for MEMS-based inertial navigation system // 2013 IEEE 8th
International Conference on Intelligent Sensors, Sensor Networks and Information
Processing (ISSNIP). 2013. P. 110–115.
26. Aggarwal P., Syed Z., Niu X., El-Sheimy N. A standard testing and
calibration procedure for low cost MEMS inertial sensors // The Journal of
Navigation. 2008. Vol. 61, No. 2. P. 323–336.
27. Skog I., Nilsson J. O., Händel P. A low-cost 6-DoF inertial navigation
system: calibration and performance // 2016 IEEE/ION Position, Location and
Navigation Symposium (PLANS). 2016. P. 444–450.
28. Brzozowski M., Krawinkel S. Calibration of MEMS IMUs for
automotive applications // 2018 22nd International Microwave and Radar Conference
(MIKON). 2018. P. 562–565.
29. Lai J., Wu J., Liu K., Wang H., Zhou Z. A Novel Calibration Method for
the Scale Factor and Cross-Axis Sensitivity of a Triaxial Accelerometer // IEEE
Sensors Journal. 2021. Vol. 21, No. 1. P. 466–477.
30. Fang K., Wang S., Chen B., Zhang H. An efficient calibration method
for inertial measurement units (IMUs) based on fixed-point iteration // Measurement
Science and Technology. 2019. Vol. 30, No. 12. P. 125103.
80
31. Leutenegger S., Lynen S., Bosse M., Siegwart R., Furgale P. Keyframe-
based visual–inertial odometry using nonlinear optimization // The International
Journal of Robotics Research. 2015. Vol. 34, No. 3. P. 314–334.
32. Mur-Artal R., Tardós J. D. Visual-Inertial Monocular SLAM with Map
Reuse // IEEE Robotics and Automation Letters. 2017. Vol. 2, No. 2. P. 796–803.
33. Campos C., Elvira R., Rodríguez J., Montiel J., Tardós J. D. ORB-
SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-
Map SLAM // IEEE Transactions on Robotics. 2021. Vol. 37, No. 6. P. 1874–1890.
34. Gui J., Gu D., Wang S. A review of visual inertial odometry //
International Journal of Automation and Computing. 2015. Vol. 12, No. 6. P. 583–
592.
35. Qin T., Li P., Shen S. VINS-Mono: A Robust and Versatile Monocular
Visual-Inertial State Estimator // IEEE Transactions on Robotics. 2018. Vol. 34, No.
4. P. 1004–1020.
36. Delmerico J., Scaramuzza D. A Benchmark Comparison of Monocular
Visual-Inertial Odometry Algorithms for Flying Robots // 2018 IEEE International
Conference on Robotics and Automation (ICRA). 2018. P. 2511–2518.
37. Niu X., Ban Y., Zhang T., Wang H., El-Sheimy N. Gyroscope error time
series modeling based on Allan variance // 2013 IEEE International Conference on
Mechatronics and Automation (ICMA). 2013. P. 1029–1034.
38. IEEE Standard Specification Format Guide and Test Procedure for
Single-Axis Interferometric Fiber Optic Gyros // IEEE Std 952-1997(R2008). 1998.
P. 1–85.
39. Brossard M., Bonnabel S., Barrau A. Denoising IMU Gyroscopes with
Deep Learning for Open-Loop Attitude Estimation // IEEE Robotics and Automation
Letters. 2020. Vol. 5, No. 3. P. 4684–4691.
40. Zhang Z., Liu J., Wu Y. A Novel Gyroscope Drift Suppression Method
for IMU Based on Recurrent Neural Networks // IEEE Access. 2019. Vol. 7. P.
177028–177036.
81
41. El-Diasty M., El-Rabbany A. Modeling systematic and stochastic errors
of MEMS-based inertial sensors // Journal of Applied Geodesy. 2015. Vol. 9, No. 1.
P. 19–29.
42. Al-Habaibeh A., Pandyan A. A review of the developments of MEMS
gyroscopes and their applications // International Journal of Mechatronics and
Manufacturing Systems. 2012. Vol. 5, No. 5/6. P. 403.
43. . ., . .
// « ».
« ». 2014. . 48. . 60–66.
44. . ., . .
MEMS- // ,
' . 2017. . 4(44). . 110–113.
45. . ., . .
- //
' . 2016. 23(99). . 132–137.
46. . ., . .
- // « ».
« ». 2013. . 45. . 129–135.
47. . ., . ., . .
// . 2011. . 10, 4. . 455–
460.
48. 2695 – XII 14.10.1992 « »
48. „ ” 17 1993 .
49. „ ’
, ” 23 1999 .
50. “
” 8 2000 .
82
51. . ., . .
.
. .: « », 2011. – 135 .
52. . .
. "
", VIII ,
" , , -
", « », 2011.
53. . ., . .
. . – : – ,
2011. - 172 .
54. “
”
-
“ ” ” ”. / . . . – . : ” ”, 2013, - 64 .
55. «
» / . . , . .
, . . , . . . – . : , 1988. - 48 .
56. 255 15.11.04 «
». .
57. . .
INERTIAL LABS, USA., VIII
, " , ,
- ", « », 2011.
58. : / . . , . .
, . . , . . . – : ,
2009. – 264 .
59. : /
. ., . ., . . – , , 2000 – 352 .
83
60. . ., . ., . .
. -
. – .: , 2004. – 120 .
62. Bo Xu, Yang Liu, Wei Shan, Yi Zhang, and Guochen Wang. Error
Analysis and Compensation of Gyrocompass Alignment for SINS on Moving Base.
Mathematical Problem in Engineering, Harbin Engineering University, Harbin China,
Hindawi, 2014.
63. Bennett K. MATLAB applications for the practical engineer. 2014.
64. Corke P., Lobo J., Dias J., “An introduction to inertial and vision
sensing”, Int. J. Rob. Res. 26(6): 519 – 535, 2007.
65. Crassidis J.L. and Markley F.L. New algorithm for attitude
determination using global positioning system signals. Journal of Guidance, Control,
and Dynamics, 20(5):891–896, 1997.
66. Crassidis J.L. and Markley F.L. Predictive filtering for attitude
estimation without rate sensors. Journal of Guidance, Control, and Dynamics,
20(3):522– 527, 1997.
67. Du W.Y., and Dickerson S.L. Passive component inspection using
machine vision. International Conference in Multichip Modules and High Density
Packaging (1998), p. 74-79.
68. Dusha Damien, Walker Rodney, Boles Wageeh. Fixed – wing attitude
estimation using vision based horizon detection // 22nd International Unmanned Air
Vehicle Systems Conference 2007.
69. Ejiri M. Machine vision technonlgy: Past, present and future. Intelligent
Robots and Systems (1990), XXIX-XXXX.
70. Faraz M. Mirzaei, Stergios I. Roumeliotis. A Kalman Filter – Based
Algorithm for IMU – Camera Calibration: Observability Analysis and Performance
Evaluation. IEEE TRANSACTIONS ON ROBOTICS, VOL. 24, NO. 5, OCTOBER
2008.
71. Farrell J.A. and Barth M. The Global Positioning and Inertial
Navigation. McGraw-Hill, New York, 1999.
84
72. Farrell J.A. AIDED NAVIGATION GPS with High Rate Sensors
McGrawHill, New York, 2008.
73. Fu-Jun Pei, Xuan Liu, Li Zhu. In – Flight Alignment Using Filter for
Strapdown INS on Aircraft. Beijing University of Technology, Beijing, China,
Hindawi, 2014.
74. Grewal M.S., Andrews A.P. Kalman filtering: theory and practice using
MATLAB. 3rd ed. J. Wiley & Sons, Inc. – 2008.
75. Grewal M.S., Weill L.R., Andrews A.P. Global Positioning Systems,
Inertial navigation and integration, 2nd ed., Wiley, New York, 2007.
76. Hol J. D. “Pose Estimation and Calibration Algorithms for Vision and
Inertial Sensors”, Lic. Thesis no 1379, Dept. Electr. Eng., Linkopings University,
Sweden, May 2008.
77. Huddle J. Inertial Navigation System Error Model Considerations in
Kalman Filtering Applications. In Control and Dynamic Systems. Vol. 20., Academic
Press, 1983.
78. Hung J.C. and White H.V. Self-alignment techniques for inertial
measurement units. IEEE Trans. on Aerospace and Electronic Systems, 11(6):1232–
1247, 1975.
79. Integrated Vision-Based Navigation System for Autonomous Vehicles in
GPS-denied Environments (Autonomous Weapons Summit and GNC Challenges for
Miniature Autonomous Systems Workshop, October 25-27, 2010)
80. Jarvis R.A. Application – oriented robotic vision. – a review // Robotica,
1984 N 2, P. 3 – 15.
81. Jekeli C. Inertial Navigation Systems with Geodetic Applications.
Walter de Gruyter, Berlin, 2001.
82. Jeroen D. Hol, Thamas B. Schon, Henk Luinge, Per J. Slycke, Frederik
Gustafsson. “Robust real – time tracking by fusing measurement from inertial and
vision sensors”, Journal of Real – Time Image Processing.
83. Jeroen D. Hol, Thamas B. Schon, Frederik Gustafsson “Relative Pose
Calibration of Spherical Camera and an IMU”
85
84. Johnson C., Ohlmeyer E.J. and Pepitone T.R., “Attitude Dilution of
Precision -A New Metric for Observability of In-flight Alignment Errors” AIAA
20004277.
85. Jose Guivant, Eduardo nebot, Stephan Baiker “Autonomous Navigation
and Map Building Using Laser Range Sensors in Outdoor Applications”, Journal of
Robotic Systems, Vol. 17 10, October 2000, pp. 565 – 583.
86. Karsenti S.M. A Study of IMU Alignment Transfer.
MASSACHUSETTS INSTITUTE OF TECHNOLOGY. MASTER’S THESIS of
SCIENCE in AERONAUTICS AND ASTRONAUTICS. 1989.
87. Kayton M., Fried W.R. Avionics Navigation Systems. Wiley, New York,
1997
88. Lefferts E.J., Markley F.L., and Shuster M.D., “Kalman Filtering for
Spacecraft Attitude Estimation,” Journal of Guidance, Control, and Dynamics, Vol.
5, No. 5, Sept.-Oct. 1982, pp. 417–429.
89. Lin, Ching-Fang. Modern Navigation, Guidance, and Control
Processing. Prentice Hall, Englewood Cliffs, New Jersey, 1991
90. Price Sean. A Simultaneous Position and Orientation Estimate Feature
Finder for Machine Vision. Electrical and Computer Engineering. A thesis submitted
to the Faculty of the WORCESTER POLYTECHNIC INSTITUTE. 2000.
91. Pue A. Integration of GPS with Inertial Navigation Systems. short
courses notes, Navtech Seminars, Springfield, VA, 2003.
92. Rogers R.M., “IMU In-motion Alignment Without Benefit of Attitude
Inialization,” Journal of the Institute of Navigation, Vol. 44, No. 3, 1997, pp. 301-
311.
93. Rogers R.M., “Comparison of Inertial Navigation System Error Models
in Application to IMU Transfer Alignment” AIAA 97-3599.
94. Rogers R. M. Applied Mathematics in Integrated Navigation Systems.
AIAA Educational Series. American Institute of Aeronautics and Astronautics, Inc,
Reston, VA, 2nd edition, 2003.
86
95. Savage P.G. Strapdown System Algorithms. Advances in Strapdown
Inertial Systems. AGARD Lecture Series 133, 1984.
96. Savage P. G. Strapdown Inertial Navigation Integration Algorithm
Design Part 2: Velocity and Position Algorithms., Journal of Guidance, Control, and
Dynamics, vol. 21, no. 2, pp. 208-211, 1998.
97. Siouris G. Aerospace Avionics Systems – A Modern Synthesis.
Academic Press, San Diego, 1993.
98. Titterton D.H., Weston J.L. Strapdown Inertial Navigation Technology,
— American Institute of Aeronautics and Astronautics & Institution of Electrical
Engineers, 2004.
99. Vision-Aided Inertial Navigation for Flight Control. AIAA Guidance,
Navigation, and Control Conference and Exhibit 15 - 18 August 2005, San Francisco,
California
100. Visual Navigation Aid for Planetary UAV Risk Reduction — The
Charles Stark Draper Laboratory, Inc. — USA Intelligent robots and computer vision
XXV 9-11 September, 2007, Boston, Massachusetts, USA
101. Waldmann Jacques. In – Flight Alignment in INS – Aiding with
Switched Feedforward/Feddback of Error Estimates.
102. Weiping Jiang, Li Wang, Xiaoji Niu, Quan Zhang, Hui Zhang, Min
Tang, Xiangyun Hu. High – Precision Image Aided Inertial Navigation with Known
Features: Observability Analysis and Performance Evaluation.Sensors 2014, 14(10),
19371-19401
103. Xiang Zhou, Zhihui Lei, Qifeng Yu, Hongliang Zhang, Yang Shang,
Jing Du, Yang Gui, Pengyu Guo. Videometric Terminal Guidance Method and
System for UAV Accurate Landing. Society of Photo-Optical Instrumentation
Engineers (SPIE).
104. Yang C., Lin C.F., Tarrant D., Roberts C. and Ruffin P., “Transfer
Alignment Design and Evaluation,” AIAA-93-3892-CP, pp. 1724-1733.
105. [ ]
http://www.ipi.uni-hannover.de/fileadmin/institut/pdf/Kurz_mueller_etal.pdf
87
106. [ ]
http://www2.geog.ucl.ac.uk/~mdisney/teaching/GEOGG141/papers/Baltsavias_Lidar
.pdf
107. http://www.ncbi.nlm.nih.gov/pubmed/20820078 - .
108. http://www.darpa.mil/ - .
109. http://www.ssci.com – .