Hình 19 O»"j·pj"8じng hがc cてa robot
Zfiv"8k吋m C trùng v噂i tâm c栄c"tqdqv"mjk"8„"rj逢挨pi"vt·pj"8瓜ng c栄c"8k吋m 系:
崔 捲岌検岌寵寵 噺 捲岌噺 検岌
"""""""肯岌寵 噺 肯岌"""""""""""""""""""""""
(52)
Rj逢挨pi"vt·pj"8瓜ng l詠c c栄c"8k吋m 迎 (Reference) v噂i 懸眺 là v壱n t嘘c mong mu嘘n c栄a robot.
崔""""捲岌"""検岌追追 噺 懸噺 懸追追œÆº 肯˚æœ 肯追追
肯岌追 噺 降追"""""""
(53)
Rj逢挨pi"vt·pj"swcp"j羽 gi英a 懸, 降 và t嘘e"8瓜 góc c栄a 2 bánh robot:
峙降降追栂
鎮栂峩=釆な 堅な 堅 伐決 堅挽 峙決 堅 降峩懸 (54)
B瓜 8k隠u khi吋n s胤 8逢嬰c thi院t k院 8吋 8k吋m C c栄c"tqdqv"dƒo"vjgq"8k吋m R trên qu悦 8衣o
ejq"vt逢噂c v噂i t嘘e"8瓜懸眺. Sai s嘘gttqt"8逢嬰e"zƒe"8鵜pj"pj逢"ucw<
煩結結怠態 結戴晩 噺 " 煩 """˚æœ 砿 伐 œÆº 砿 ど """ œÆº 砿 ˚æœ 砿 ど """ ど ど な晩 煩 捲追 伐 検追 伐 砿追 伐"" 捲寵 検寵 砿寵晩 (55)
煩結結怠態岌岌 結戴岌 晩 噺 " 煩 懸追˚æœ 結戴 懸追œÆº 結戴 降追 晩 髪 煩 ""伐な ど ど "伐 穴" 結態 伐結怠 伐な"""晩 峙懸降峩 (56) N院w"tqdqv"8k"vjgq"j逢噂pi"pi逢嬰c l衣i thì thay d b茨ng -d. 4.2.2. Thi院t k院 b瓜8k隠u khi吋n:
V医p"8隠 theo dõi qu悦8衣o mong mu嘘n có th吋 hi吋u là:
V噂k" 8逢運ng reference 刺司(t) = ["捲追岫建岻""""検追岫建岻""""肯追岫建岻]T và v壱n t嘘c reference 子司 噺 岷士司""磁司峅T. V医p"8隠 bám qu悦 8衣o là tìm lu壱v"8k隠u khi吋n v壱n t嘘c: 子纂 噺 血鳥岫蚕 士司 察岻
sao cho ØÆŒ
痛蝦著刺岫嗣岻 噺 刺司岫嗣岻. V噂i e, zr và C l亥p"n逢嬰t là sai s嘘 v鵜 trí, v壱n t嘘c reference
x "8瓜 l嬰k"8k隠u khi吋n.
B瓜 8k隠u khi吋p"8逢嬰c thi院t k院 8吋 8衣v"8逢嬰e"g"s"2"mjk"v"s"ı="p„k"eƒej"mjƒe."8k吋m P
dƒo"vjgq"8k吋m tham chi院u R v噂i v壱n t嘘c mong mu嘘n. Ch丑n hàm Lyapunov: 撃怠 噺な に岫結怠態髪 結態態岻 髪 な 系態岫な 伐 ˚æœ 結戴岻 半 ど (57) A衣q"j o"X3"vc"8逢嬰c: 撃岌怠 噺 結怠結岌怠髪 結態結岌態髪 な 系態結岌戴œÆº 結戴 噺 結怠岫伐荒 髪 荒追˚æœ 結戴岻 髪系な 態œÆº 結戴岫降追 伐 降 髪 系態結態荒追岻 (58) Ch丑n lu壱v"8k隠u khi吋n: 四纂 噺 峙荒降峩 噺 釆降追 髪 系荒追˚æœ 結態荒追結戴態髪 系髪 系怠戴結œÆº 結怠 戴挽 (59) V噂i 系怠, 系態, 系戴 là các h茨ng s嘘f逢挨pi Thay 四纂 vào 撃岌怠vc"8逢嬰c:
撃岌怠 噺 結怠岫伐系怠結怠岻 髪寵怠
鉄œÆº 結戴岫伐系戴œÆº 結戴岻 噺 伐系怠結怠態伐寵典
寵鉄œÆº 結戴態 (60) Vì v噂i 系怠, 系態, 系戴 là các h徨ng s怎 ¸恊怦º̌ "©²"˚³˚"結怠, 結態, 結戴 b医t k 撃岌怠luôn nh臼 j挨p"
ho員c b茨pi" 2" fq" 8„ 結怠, 結態, 結戴s胤 ti院n v隠 0 khi 建 s" ı theo tiêu chu育n 鰻p" 8鵜nh Lyapunov.
4.3. K院t qu違 mô ph臼pi"8k隠u khi吋n robot bám qu悦8衣o khi k院t h嬰p c違m bi院n b茨ng EKF:
Hình 20: Control block diagram
Mô ph臼pi"8k隠u khi吋n robot bám qu悦 8衣o khi k院t h嬰p c違m bi院n b茨ng EKF v噂i các thông s嘘pj逢"ucw<
Chi隠u dài robot: hagv =0.230 m
Chi隠u r瓜ng robot: wagv=0.350 m
Kho違ng cách 2 bánh: b=0.3328 m
Bán kính bánh xe: r=0.0975 m
Kho違ng cách c違m bi院n và tr映c bánh xe: d=0.0 m
H羽 s嘘8k隠u khi吋n: 系怠=15; 系態=120; 系戴=20;
Chu k : 100 ms
Sai s嘘vjgq"rj逢挨pi"z"e栄a GPS: e_xGPS=150/1000; Sai s嘘vjgq"rj逢挨pi"z"e栄a Encoder: e_xodo=3/1000;
Sai s嘘vjgq"rj逢挨pi"{"e栄a GPS: e_yGPS=150/1000; Sai s嘘vjgq"rj逢挨pi"{"e栄a Encoder: e_yodo=3/1000; V壱n t嘘c dài ref c栄a robot là: v_ref = 1m/s;
Hình 21 Tín hiうu cＶm biｘn mô phぎng trong quá trình bám quぶ8Ｔo cてa robot vずi thぜi gian lＸy mｄu 100ms
Tín hi羽u c違m bi院n gi違 l壱p:
xGPS(i) = x(i) + rand. exGPS
yGPS(i) = y(i) + rand. eyGPS (61)
xodo(i)=xodo(i-1) + 捲岌(i)+rand. exodo
yodo(i)=yodo(i-1) + 検岌(i)+rand. eyodo
(62)
V噂i k院t qu違 mô ph臼ng k院t h嬰p c違m bi院n v噂i tín hi羽u c違m bi院n UWB và Odometry th詠c t院 荏 ph亥p"60306"vtqpi"ej逢挨pi"6."mjk"vj運i gian l医y m磯u th医p 違nh
j逢荏ng nhi隠w"8院n ch医v"n逢嬰ng c栄a gi違i thu壱t k院t h嬰r0"Fq"8„"vtqpi"rj亥n mô ph臼ng
th運i gian l医y m磯u là 100ms, k院t qu違 mô ph臼ng tín hi羽u c違m bi院n th吋 hi羽n 荏 Hình 24.
Hình 22: Kｘt quＶ mô phぎpi"8kｚu khiあn robot bám quぶ8Ｔo khi kｘt hぢp cＶm biｘn bｊng EKF vずi thぜi gian lＸy mｄu 100ms
T í n h i 羽 u m ô p h 臼 n
g c違m bi院n GPS bi院p"8瓜ng l噂p"pj逢pi"fcq"8瓜ng quanh qu悦 8衣o, còn Odometry thì
o逢嬰v"j挨p"pj逢pi"fq"違pj"j逢荏ng c栄a vi羽c sai s嘘 c瓜ng d欝n nên sai l羽ej"v<pi"f亥n. Sau khi áp d映ng lu壱v"8k隠u khi吋p"8瓜ng h丑c robot 荏 rj逢挨pi"vt·pj"*7;+"x "8鵜nh v鵜
b茨ng gi違i thu壱t k院t h嬰p c違m bi院n EKF thì nh壱p"8逢嬰c k院t qu違 bám qu悦8衣q"pj逢"J·pj"
22. Tuy v磯n còn sai l羽ch nhi隠u do 違pj"j逢荏ng c栄a vi羽c th運i gian l医y m磯u c栄a c違m bi院n l噂p"*322ou+"pj逢pi"tqdqv"x磯p"dƒo"8逢嬰c qu悦8衣o v噂i sai s嘘 tracking th吋 hi羽n 荏
Hình 23. Dk‒p"8瓜fcq"8瓜ng c栄a sai s嘘 v隠 v鵜 trí là x医p x雨 0.1 m.
A吋 8ƒpj"ikƒ"u詠 違pj"j逢荏ng c栄a ch医v"n逢嬰ng c違m bi院p"e pi"pj逢"vj運i gian l医y m磯u
8院n k院t qu違 8k隠u khi吋n bám qu悦 8衣o thì ta ti院n hành mô ph臼pi"8k隠u khi吋n bám qu悦 8衣o v噂i c違m bi院n GPS và Encoder v噂i th運i gian l医y m磯u 1 ms. Ta nh壱p"8逢嬰c k院t qu違 mô ph臼ng tín hi羽u c違m bi院n gi違 l壱p 荏 Hình 24. V噂i th運i gian l医y m磯w"v<pi"n‒p"
thì tín hi羽w"IRU"fcq"8瓜pi"f {"j挨p"e”p"8嘘i v噂i Odometry thì càng sai l羽ch l噂p"j挨p"
do c瓜ng d欝n nhi隠u l亥p"j挨p0"
Hình 24Tín hiうu cＶm biｘn mô phぎng trong quá trình bám quぶ8Ｔo cてa robot vずi thぜi gian lＸy mｄu 100ms
V噂i th運i gian l医y m磯w"pj逢"vj院 p {"ik¿r"ejq"swƒ"vt·pj"逢噂c n逢嬰ng c栄a gi違i thu壱t k院t h嬰p c違m bi院n tr荏 lên t嘘v"j挨p"x "mfiq"vjgq"8„"m院t qu違8k隠u khi吋n bám qu悦 8衣q"e pi" 8逢嬰c c違i thi羽p"8ƒpi"m吋. K院t qu違p {"8逢嬰c th吋 hi羽n 荏 Hình 25. Robot bám qu悦8衣o khá t嘘t m員c dù v磯p"e”p"fcq"8瓜ng 荏 m泳c nh臼, khi phóng to lên ta nh壱n th医y t "j挨p" 荏 Hình 26. Vì k院t qu違8k隠u khi吋n bám qu悦8衣o t嘘v"j挨p"p‒p"f磯p"8院n sai s嘘 tracking
e pi" v嘘v" j挨p" pjk隠u, sai s嘘 t嘘k" 8c" vj医r" j挨p" pi逢叡ng 0.01 m, gi違m 10 l亥n so v噂i
Hình 25 Kｘt quＶ mô phぎpi"8kｚu khiあn robot bám quぶ8Ｔo khi kｘt hぢp cＶm biｘn bｊng EKF vずi thぜi gian lＸy mｄu 1ms.
Hình 26 Kｘt quＶ mô phぎpi"8kｚu khiあn robot bám quぶ8Ｔo khi kｘt hぢp cＶm biｘn bｊng EKF vずi thぜi gian lＸy mｄu 1ms Î phóng to.
Hình 27Tracking error vずi thぜi gian lＸy mｄu 1ms
Ej逢挨pi"70 K蔭T LU一P"XÉ"J姶閏NG PHÁT TRI韻N:
5.1. K院t lu壱n:
V噂k"d k"vqƒp"8員t ra là thi院t k院 gi違i thu壱t k院t h嬰p d英 li羽u nhi隠u c違m bi院n trong v医n
8隠zƒe"8鵜nh v鵜vt "x "j逢噂ng c栄a robot t詠 hành, Lu壱p"x<p"8«"pijk‒p"e泳u, tìm hi吋u các
rj逢挨pi"rjƒr"mj違 thi liên quan nh茨m 泳ng d映ng gi違i quy院t bài toán. T瑛 8„"n詠a ch丑n
rj逢挨pi"ƒp"m院t h嬰p d英 li羽u c違m bi院n b茨ng b瓜 l丑c Kalman m荏 r瓜pi"8吋 áp d映ng cho vi羽c t詠j pj"tqdqv0"Eƒe"8„pi"i„r"e栄a lu壱p"x<p"i欝m:
‚ Th泳 nh医t, t鰻ng h嬰p, phân lo衣k"x "8ƒpj"ikƒ"逢w"pj逢嬰e"8k吋m c栄c"eƒe"rj逢挨pi"
pháp k院t h嬰p d英 li羽u.
‚ Th泳 hai, xây d詠ng mô hình t鰻ng quát cho vi羽c k院t h嬰p d英 li羽u c栄a nhi隠u lo衣i c違m bi院p"mjk"zƒe"8鵜nh v鵜 vt "x "j逢噂ng c栄a robot t詠 hành.
‚ Th泳 ba, thi院t k院 gi違i thu壱v"8k隠u khi吋n robot t詠 hành d詠a vào k院t h嬰p d詠 li羽u c栄a nhi隠u lo衣i c違m bi院n.
‚ Th泳 v逢."vk院n hành l壱p trình gi違i thu壱t và mô ph臼ng gi違i thu壱v"8«"8隠 xu医t trên n隠n t違ng Matlab.
5.2. J逢噂ng phát tri吋n:
Nhìn chung lu壱p"x<p"ej雨 m噂i d瑛ng l衣i 荏 m泳c áp d映pi"eƒe"rj逢挨pi"rjƒr"m院t h嬰p d英 li羽w"8«"8逢嬰c nghiên c泳u thành công nên có nh英pi"8k吋m c亥n ti院p t映c phát tri吋n
vj‒o"vtqpi"v逢挨pi"nck<
Nghiên c泳u thêm các gi違k"rjƒr"8吋 c違i thi羽n các thu壱t toán k院t h嬰p d英 li羽u nh茨m
v<pi"mj違 p<pi"j瓜i t映 c栄a các d詠 8qƒp"e pi"pj逢"ik違m kh嘘k"n逢嬰ng tính toán c栄a gi違i thu壱t.
Tài li羽u tham kh違o:
[1] A. Bosse, J. Roy, and D. Grenier, ÐData Fusion Concepts Applied to a Suite of Dissimilar Sensors,Ñ Canadian Conference on Electrical and Computer Engineering, Calgary, AB, Canada, May 1996, pp. 692Î695.
[2] A. M. Santana, ÐLocalization of a Mobile Robot based in Odometry and Natural Landmarks using Extended Kalman FilterÑ, in Proc. the Fifth International Conference on Informatics in Control, Automation and Robotics, Robotics and Automation 2, Funchal, Madeira, Portugal, May 11-15, 2008. [3] A. Schwaighofer, M. Grigoras, V. Tresp, and C. Hoffmann." ÐGPPS: A
Gaussian Process Positioning System for Cellular NetworksÑ. presented at NIPS, 2003.
[4] D0"E0"Nwq"cpf"O0"I0"Mc{."ÐFcvc"hwukqp"cpf"ugpuqt"kpvgitcvkqp<"uvcvg-of-the-art
3;;2u.Ñ"presented at Data Fusion in Robotics and Machine Intelligence, pp. 7Î
135, 1992.
[5] B. V. Dasarathy, ÐUgpuqt"hwukqp"rqvgpvkcn"gzrnqkvcvkqp-innovative architectures
cpf"knnwuvtcvkxg"crrnkecvkqpuÑ, in IEEE, vol. 85, no. 1, pp. 24Î38, 1997.
[6] B. Ferris, D. Fox, and N. D. Lawrence. ÐWiFi-SLAM Using Gaussian Process Latent Variable Models,Ñ in IJCAI, vol. 7, pp. 2480Î2485, 2007.
[7] B. Ferris, D. Haehnel, and D. Fox. ÐGaussian processes for signal strength- based location estimation,Ñ in Proc. of robotics science and systems, 2006. DOI:10.15607/RSS.2006.II.039
[8] C. Brown, H. Durrant-Yj{vg." L0" Ngqpctf." D0" Tcq." cpf" D0" Uvggt." ÐFkuvtkdwvgf" fcvc"hwukqp"wukpi"Mcnocp"hknvgtkpi<"c"tqdqvkeu"crrnkecvkqp.Ñ"in Data, Fusion in Robotics and Machine Intelligence, pp. 267Î309, 1992.
[9] C. Edwards, and S. Spurgeon, Sliding Mode Control: Theory and Applications London: Taylor & Francis, 1998.
[10] C. M. Wang. ÐLocation estimation and uncertainty analysis for mobile robots,Ñ
in Proc. IEEE International Conference on Robotics and Automation, 1988, pp. 1230-1235. DOI:10.1109/ROBOT.1988.12229
[11] F0"Etkucp"cpf"C0"Fqwegv."ÐC"uwtxg{"qh"eqpxgtigpeg"tguwnvu"qp"rctvkeng"hknvgtkpi"
methods for practitioners,Ñ"IEEE Transactions on Signal Processing, vol. 50, no. 3, pp. 736Î746, 2002.
[12] F0"N0"Jcnn"cpf"L0"Nnkpcu."ÐCp"kpvtqfwevkqp"vq"ownvkugpuqt"fcvc"hwukqpÑ, in Proc. IEEE, vol. 85, no. 1, pp. 6Î23, 1997.
[13] G0"R0"Dncuej"cpf"U0"Rncpq."ÐLFN"ngxgn"7"hwukqp"oqfgn"Ðwugt"tghkpgogpvÑ"kuuwgu"
and applications in group tracking,Ñ in Proc. Signal Processing, Sensor Fusion, and Target Recognition XI, pp. 270Î279, April 2002.
[14] G. T. McKee, ÐWhat can be fused? Multisensor Fusion for Computer Vision, Nato Advanced Studies Institute Series FÑ, 99:71Î84, 1993.
[15] H. F. Durrant-Yj{vg" cpf" O0" Uvgxgpu." ÐFcvc" hwukqp" kp" fgegpvtcnk¦gf" ugpukpi" pgvyqtmu.Ñ"kp" Rtqe. the 4th International Conference on Information Fusion, pp. 302Î307, Montreal, Canada, 2001.
[16] H. F. Durrant-Yj{vg." ÐUgpuqt" oqfgnu" cpf" ownvkugpuqt" kpvgitcvkqp.Ñ"
International Journal of Robotics Research, vol. 7, no. 6, pp. 97Î113, 1988. [17] H. K. Khalil, Nonlinear Systems, 3rd ed. Prentice Hall, 1996.
[18] J. Hamilton, The Kalman Filter, 1994.
[19] L0"M0"Wjnocpp."ÐEqxctkcpeg"eqpukuvgpe{"ogvjqfu"hqt"hcwnv tolerant distributed
[20] J. Llinas, C. Bowman, G. Rogova, A. Steinberg, E. Waltz, and F. White,
ÐTgxkukvkpi"vjg"LFN"fcvc"hwukqp"oqfgn"KK.Ñ"Technical Report, DTIC Document, 2004.
[21] J. Manyika and H. Durrant-Whyte, Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach. NJ, USA: Upper Saddle River, 1995.
[22] J. J. E. Slotine, Aplied Nonlinear Control. Prentine Hall, 1996.
[23] J. F. E. White, ÐData Fusion Lexicon.Ñ Technical Panel For C3, San Diego, Calif, USA, 1991.
[24] K. C. Chang, C. Y. Chong, and Y. Bar-Ujcnqo." ÐLqkpv" rtqdcdknkuvke" fcvc" cuuqekcvkqp"kp"fkuvtkdwvgf"ugpuqt"pgvyqtmu.Ñ"IEEE Transactions on Automatic Control, vol. 31, no. 10, pp. 889Î897,1986
[25] O0"Eqcvgu."ÐFkuvtkdwvgf"rctvkeng"hknvgtu"hqt"ugpuqt"pgvyqtmu.Ñ"kp"Rtqe. the 3rd International symposium on Information Processing in Sensor Networks (ACM
Ó26+."pp. 99Î107, New York, NY, USA, 2004.
[26] O0"Ocp¦q."V0"Tqquvc."cpf"U0"Ucuvt{."ÐVkog"u{pejtqpk¦cvkqp"kp"pgvyqtmu.Ñ"kp"
Proc. the 3rd ACM Workshop on Security of Ad Hoc and Sensor Networks
*UCUP"Ó27+, pp. 107Î116, November 2005.
[27] O0" Qtvqp" cpf" C0" Octtu." ÐC" Dc{gukcp" crrtqcej" vq" ownvk-target tracking and data fusion with Out-of-Ugswgpeg"Ogcuwtgogpvu.Ñ"IEE Colloquium, no. 174, pp. 15/1Î15/5, 2001.
[28] Mohinder S. Grewal and Angus P. Andrews, Kalman filtering: theory and practice using MATLAB. New York: John Wiley and Sons, 2001.
[29] P. J. Nahin and J. L. Pokoski. ÐNCTR Plus Sensor Fusion Equals IFFN or Can Two Plus Two Equal Five.Ñ"IEEE Transactions on Aerospace and Electronic Systems, vol. 16, iss. 3, pp. 320Î337, May, 1980.
]52̲"T0"E0"Nwq"cpf"O0"I0"Mc{."ÐFcvc"hwukqp"cpf"ugpuqt"kpvgitcvkqp<"uvcvg-of-the-art
3;;2u.Ñ" kp"Data Fusion in Robotics and Machine Intelligence, pp. 7Î135, 1992. doi:10.1016/ j.inffus.2011.08.001
[31] R. C. Luo, C.-E0" [kj." cpf" M0" N0" Uw." ÐOwnvkugpuqt" hwukqp" cpf" kpvgitcvkqp<" crrtqcejgu." crrnkecvkqpu." cpf" hwvwtg" tgugctej" fktgevkqpu.Ñ" IEEE Sensors Journal, vol. 2, no. 2, pp. 107Î119, 2002.
[32] S. Roweis, Z. Ijcjtcocpk." ÐC" wpkh{kpi" tgxkgy" qh" nkpgct" icwuukcp" oqfgnu,Ñ"
Neural computation, vol. 11, pp. 30Î45, 1999.
[33] U0" Nnq{f." ÐNgcuv" uswctgu" swcpvk¦cvkqp" kp" PCM,Ñ" IEEE Transactions on Information Theory, vol. 28, no. 2, pp. 129Î137, 1982.
[34] U0" U0" Dncemocp." ÐCuuqekcvkqp" cpf" hwukqp" qh" ownvkrng" ugpuqt" fcvc.Ñ" kp"
Multitarget-Multisensor: Tracking Advanced Applications, vol. 86, pp. 187Î
217, Artech House, 1990.
[35] S. N0"Ncwtkv¦gp0"ÐVkog"ugtkgu"cpcn{uku"kp"3::20"C"fkuewuukqp"qh"eqpvtkdwvkqpu"
made by T.N. Thiele,Ñ"International Statistical Review, vol. 49, no. 3, pp. 319Î
333, 1981.
[36] R. L. Stratonovich, ÐOptimum nonlinear systems which bring about a separation of a signal with constant parameters from noise,Ñ"Izvestiya vuzov SSSR, Seriya Radiofizika, in Russian, vol. 2, 1959, pp.862Î901.
[37] T. E. Fortmann, Y. Bar-Ujcnqo."cpf"O0"Uejghhg."ÐOwnvk-target tracking using
lqkpv" rtqdcdknkuvke" fcvc" cuuqekcvkqp.Ñ" kp" Rtqc. the 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, vol. 19, pp. 807Î 812, December 1980.
[38] V. I. Utkin et al., Sliding Mode Control in Electromechanical Systems. Taylor & Francis, 1999.
[39] Z. Wei, Y. Lang, F Yang, and S. Zhao, ÐA TOF Localization Algorithm Based
qp"Kortqxgf"Fqwdng佻ukfgf"Vyq"Yc{"Tcpikpi,Ñ DEStech Trans. Comput. Sci. Eng, 423:."47."529佻5370
[40] Welch and G. Bishop, ÐAn Introduction to the Kalman Filter,Ñ ACM SIC- CRAPH, 2001 Course Notes, 2001.
[41] Y. Bar-Shalom and E. Vug." ÐVtcemkpi" kp" c" enwvvgtgf" gpxktqpogpv" ykvj" rtqdcdknkuvke"fcvc"cuuqekcvkqp.Ñ"Automatica, vol. 11, no. 5, pp. 451Î460, 1975. [42] Y. Bar-Ujcnqo."ÐWrfcvg"ykvj"qwv-of-sequence measurements in tracking: exact
uqnwvkqp.Ñ"IEEE Transactions on Aerospace and Electronic Systems, vol. 38, no. 3, pp. 769Î778, 2002.
[43] [0" Ejqpi." U0" Oqtk." cpf" M0" E0" Ejcpi." ÐFkuvtkdwvgf" ownvkvctigv" ownvkugpuqt" vtcemkpi.Ñ"kp"Multitarget-Multisensor Tracking: Advanced Applications, vol. 1, pp. 247Î295, 1990.
[44] [0"Ejqpi."U0"Oqtk."cpf"M0"E0"Ejcpi."ÐKphqtocvkqp"nwukqp"kp"fkuvtkdwvgf"ugpuqt" pgvyqtmu.Ñ"kp"Rtqe. the 4th American Control Conference, Boston, Mass, USA, June 1985.
[45] Zinober, Variable Structure and Lyapunov Control. London: Springer - Verlag, 1994.
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