A study on the implementation of nonlinear Kalman filter applying MMG model

1. Ministry of Land (2021) Infrastructure, transport and tourism.

White paper on land, infrastructure, transport and tourism in

Japan

2. Takai T (1990) Automatic berthing experiments using" shiojimaru" (in japanese). J Jpn Inst Navig 83:267–276

3. Maki A, Sakamoto N, Akimoto Y, Nishikawa H, Umeda N

(2020) Application of optimal control theory based on the evolution strategy (cma-es) to automatic berthing. J Mar Sci Technol 25(1):221–233

4. Rachman DM, Maki A, Miyauchi Y, Umeda N (2022) Warmstarted semionline trajectory planner for ship’s automatic docking (berthing). Ocean Eng 252:111127

5. Miyauchi Y, Sawada R, Akimoto Y, Umeda N, Maki A

(2022) Optimization on planning of trajectory and control of

Journal of Marine Science and Technology (2023) 28:733–745 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. autonomous berthing and unberthing for the realistic port geometry. Ocean Eng 245:110390

Jahren C, Jones R (1993) Ferry landing design phase i. final

report. Tech Rep

Roubos A, Groenewegen L, Peters DJ (2017) Berthing velocity

of large seagoing vessels in the port of rotterdam. Mar Struct

51:202–219

Association IMWP https://​www.​isemi​kawap​ilot.​jp/​wp-​conte​nt/​

uploa​ds/​2021/​12/​keiryu.​pdf

Kalman RE (1960) On the general theory of control systems. in

Proceedings First International Conference on Automatic Control, Moscow, USSR, pp. 481–492

Kalman RE, Bucy RS (1961) New results in linear filtering and

prediction theory

Humpherys J, Redd P, West J (2012) A fresh look at the kalman

filter. SIAM Rev 54(4):801–823

Yang C, Shi W, Chen W (2017) Comparison of unscented and

extended kalman filters with application in vehicle navigation. J

Navig 70(2):411–431

Gustafsson F, Gunnarsson F, Bergman N, Forssell U, Jansson J,

Karlsson R, Nordlund P-J (2002) Particle filters for positioning,

navigation, and tracking. IEEE Trans Signal Process 50(2):425–437

Giannitrapani A, Ceccarelli N, Scortecci F, Garulli A (2011)

Comparison of ekf and ukf for spacecraft localization via angle

measurements. IEEE Trans Aerosp Electron Syst 47(1):75–84

Farina A, Ristic B, Benvenuti D (2002) Tracking a ballistic target: comparison of several nonlinear filters. IEEE Trans Aerosp

Electron Syst 38(3):854–867

Ceccarelli N, Garulli A, Giannitrapani A, Leomanni M, Scortecci

F (2007) Spacecraft localization via angle measurements for

autonomous navigation in deep space missions. IFAC Proc

40(7):551–556

Larsen TD, Hansen KL, Andersen NA, and Ravn O (1999) Design

of kalman filters for mobile robots; evaluation of the kinematic

and odometric approach. In Proceedings of the 1999 IEEE international conference on control applications (Cat. No. 99CH36328),

vol. 2, pp. 1021–1026, IEEE

Battin RH (2002) Some funny things happened on the way to the

moon. J Guid Control Dyn 25(1):1–7

Balchen JG, Jenssen NA, Mathisen E, Sælid S (1980) A dynamic

positioning system based on kalman filtering and optimal control.

Model Identif Control Norweg Res Bull 1(3):135–163

Sørensen AJ, Sagatun SI, Fossen TI (1996) Design of a dynamic

positioning system using model-based control. Control Eng Pract

4(3):359–368

Abkowitz MA (1980) Measurement of hydrodynamic characteristics from ship maneuvering trials by system identification. Tech Rep

Fossen TI, Sagatun SI, Sørensen AJ (1996) Identification of

dynamically positioned ships. Control Eng Pract 4(3):369–376

Ochiai Y, Okazaki T, Ohtsu K (2006) Study on precise prediction of

ship’s position using ekf (in japanese). J Japan Inst Navig 115:1–10

Miyoshi S, Hara Y, Ohtsu K (2008) A study on optimum tracking

control with kalman filter for vessel (in japanese). J Japan Inst

Navig 118:47–53

Fossen S, and Fossen TI (2018) Extended kalman filter design

and motion prediction of ships using live automatic identification

system (ais) data. In 2018 2nd European Conference on Electrical

Engineering and Computer Science (EECS), pp. 464–470, IEEE

Fossen TI (2022) Line-of-sight path-following control utilizing

an extended kalman filter for estimation of speed and course over

ground from gnss positions. J Mar Sci Technol 27(1):806–813

Subchan S, Ismail RW, Asfihani T, and Adzkiya D (2019) Estimation of hydrodynamic coefficients using unscented kalman filter

and recursive least square. 2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA

2019 - Proceedings, pp. 9–13

745

28. Deng F, Yang HL, Wang LJ, Yang WM (2019) UKF based nonlinear offset-free model predictive control for ship dynamic positioning under stochastic disturbances. Int J Control Autom Syst

17:3079–3090

29. Konatowski S, Pieniżny AT (2007) A comparison of estimation

accuracy by the use of KF. EKF & UKF filters, Computational

Methods and Experimental Measurements XIII

30. Konatowski S, Kaniewski P, and Matuszewski J (2016) Comparison of estimation accuracy of ekf, ukf and pf filters. Ann Navig

31. Chen H, Chen G, Blasch E, and Pham K (2009) Comparison of

several space target tracking filters. In Sensors and Systems for

Space Applications III, vol. 7330, pp. 118–129, SPIE

32. Nishimura T (1970) Modeling errors in kalman filters. Tech Rep

33. Julier SJ, and Uhlmann JK (1997) A new extension of the kalman

filter to nonlinear systems. In Signal processing, sensor fusion,

and target recognition VI, vol. 3068, pp. 182–193, International

Society for Optics and Photonics

34. Julier S, Uhlmann J, Durrant-Whyte HF (2000) A new method for

the nonlinear transformation of means and covariances in filters

and estimators. IEEE Trans Autom Control 45(3):477–482

35. Julier SJ, Uhlmann JK (2004) Unscented filtering and nonlinear

estimation. Proc IEEE 92(3):401–422

36. Evensen G (1994) Sequential data assimilation with a nonlinear

quasi-geostrophic model using monte carlo methods to forecast

error statistics. J Geophys Res 99(C5):10143

37. Evensen G (2003) The ensemble kalman filter: Theoretical formulation and practical implementation. Ocean Dyn 53(4):343–367

38. Katzfuss M, Stroud JR, Wikle CK (2016) Understanding the

ensemble kalman filter. Am Stat 70(4):350–357

39. Yasukawa H, Yoshimura Y (2015) Introduction of mmg standard method for ship maneuvering predictions. J Mar Sci Technol

20(1):37–52

40. Yoshimura Y, Nakao I, and Ishibashi A (2009) Unified mathematical model for ocean and harbour manoeuvring. International Conference on Marine Simulation and Ship Maneuverability

41. Schneider R, Georgakis C (2013) How to not make the extended

kalman filter fail. Ind Eng Chem Res 52(9):3354–3362

42. Fujiwara T, Ueno M, Nimura T (1998) Estimation of wind

forces and moments acting on ships. J Soc Naval Arch Japan

1998(183):77–90

43. Araki M, Sadat-Hosseini H, Sanada Y, Tanimoto K, Umeda N,

Stern F (2012) Estimating maneuvering coefficients using system

identification methods with experimental, system-based, and cfd

free-running trial data. Ocean Eng 51:63–84

44. Jian C, Jiayuan Z, Feng X, Jianchuan Y, Zaojian Z, Hao Y, Tao

X, Luchun Y (2015) Parametric estimation of ship maneuvering motion with integral sample structure for identification. Appl

Ocean Res 52:212–221

45. Sutulo S, Soares CG (2014) An algorithm for offline identification of ship manoeuvring mathematical models from free-running

tests. Ocean Eng 79:10–25

46. Wakita K, Maki A, Umeda N, Miyauchi Y, Shimoji T, Rachman

DM, Akimoto Y (2022) On neural network identification for lowspeed ship maneuvering model. J Mar Sci Technol 27(1):772–785

47. Miyauchi Y, Maki A, Umeda N, Rachman DM, and Akimoto Y

(2021) System parameter exploration of ship maneuvering model

for automatic docking/berthing using cma-es. arXiv preprint

arXiv:​2111.​06124

48. Majeed M, Kar IN (2011) Multi sensor data fusion based

approach for the calibration of airdata systems. Aeronaut J

115(1164):113–122

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