Multisensor Tracking of Marine Targets : Decentralized Fusion of Kalman and Neural Filters

• Autorzy: Stateczny A. , Kazimierski W.
• Języki publikacji: EN

Abstrakty

EN

This paper presents an algorithm of multisensor decentralized data fusion for radar tracking of maritime targets. The fusion is performed in the space of Kalman Filter and is done by finding weighted average of single state estimates provided be each of the sensors. The sensors use numerical or neural filters for tracking. The article presents two tracking methods - Kalman Filter and General Regression Neural Network, together with the fusion algorithm. The structural and measurement models of moving target are determined. Two approaches for data fusion are stated - centralized and decentralized - and the latter is thoroughly examined. Further, the discussion on main fusing process problems in complex radar systems is presented. This includes coordinates transformation, track association and measurements synchronization. The results of numerical experiment simulating tracking and fusion process are highlighted. The article is ended with a summary of the issues pointed out during the research.

Słowa kluczowe

EN

target tracking; sensor fusion; Kalman filter; neural filters

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2011
• Tom: Vol. 57, No. 1
• Strony: 65--70
• Opis fizyczny: Bibliogr. 15 poz., wykr.

Twórcy

autor

Stateczny A.

autor

Kazimierski W.

• Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland,

Bibliografia

• [1] W. Kazimierski, “Two-stage General Regression Neural Network for Radar Target Tracking,” Polish Journal of Environmental Studies, vol. 17, no. 3B, 2008.
• [2] P. Borkowski and A. Stateczny, “An Algorithm of Navigational Data Integration,” Problemy Eksploatacji, no. 1, pp. 181–188, 2008.
• [3] Y. Bar-Shalom and X. Li, Multitarget-multisensor Tracking: Principles and Techniques. CT, USA: Yaakov Bar-Shalom, 1995.
• [4] T. Kantak, A. Stateczny, and J. Urbaski, Basis of Automation of Navigation. Gdynia: AMW, 1988, (in polish).
• [5] A. G. Bole, W. O. Dineley, and A. Wall, Radar and ARPA Manual. Elsevier Science & Technology Book, 2005.
• [6] A. Stateczny, “Integration of RADAR, EGNOS/GALILEO, AIS and 3D ECDIS,” in Proceedings of International Radar Symposium, Warszawa, 2004.
• [7] A. Stateczny and W. Kazimierski, “Determining Manoeuvre Detection Threshold of GRNN Filter in the Process of Tracking in Marine Navigational Radars,” in Proceedings of International Radar Symposium, Wrocaw, 2008.
• [8] D. P. Mandic, D. Obradovic, A. Kuh, T. Adali, U. Trutschell, M. Golz, P. DeWilde, J. Barria, A. Constantinides, and J. Chambers, “Data Fusion for Modern Engineering Applications: An Overview,” in Proceedings of Interanational Conference on Artificial Neural Networks. Berlin: Springer Verlag, 2005.
• [9] A. Stateczny, A. Lisaj, and M. Chavan, “Navigational Data Fusion on the Base on Kalman Filter,” in Proceedings of Explo-Ship, Szczecin, 2006, (in polish).
• [10] IALA Recommendation V-128 – On Operational and Technical Performance Requirements for VTS Equipment, 3rd ed., IALA, 2007.
• [11] A. Stateczny, “AIS and Radar Data Fusion for Maritime Navigation,” Zeszyty Naukowe AM, 2004.
• [12] A. Stateczny and W. Kazimierski, “General Regression Neural Network (GRNN) in the Process of Tracking a Manoeuvring Target in ARPA Device,” in Proceedings of International Radar Symposium, Berlin, 2005.
• [13] A. Stateczny and W. Kazimierski, “A Comparison of the Target Tracking in Marine Navigational Radars by Means of GRNN Filter and Numerical Filter,” in Proceedings of IEEE Radar Conference, Rome, 2008.
• [14] A. Stateczny and W. Kazimierski, Marine Navigation and Safety of Sea Transportation. London, UK: Taylor & Francis Group, 2009, ch. Target Tracking in RIS.
• [15] A. Stateczny and W. Kazimierski, “Integration of Navigational Data in Vessel Traffic Control Systems,” Polish Journal of Environmental Studies, vol. 18, no. 5A, 2009.