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Collision avoidance is one of the high-level safety objectives and requires a complete and reliable description of the maritime traffic situation. The radar is specified by the IMO as the primary sensor for collision avoidance. In this paper we study the performance of multi-target tracking based on radar imagery to refine the maritime traffic situation awareness. In order to achieve this we simulate synthetic radar images and evaluate the tracking performance of different Bayesian multi-target trackers (MTTs), such as particle and JPDA filters. For the simulated tracks, the target state estimates in position, speed and course over ground will be compared to the reference data. The performance of the MTTs will be assessed via the OSPA metric by comparing the estimated multi-object state vector to the reference. This approach allows a fair performance analysis of different tracking algorithms based on radar images for a simulated maritime scenario.
Rocznik
Tom
Strony
511--518
Opis fizyczny
Bibliogr. 28 poz., rys.
Twórcy
autor
- German Aerospace Centre (DLR), Neustrelitz, Germany
autor
- German Aerospace Centre (DLR), Neustrelitz, Germany
autor
- German Aerospace Centre (DLR), Neustrelitz, Germany
autor
- German Aerospace Centre (DLR), Neustrelitz, Germany
Bibliografia
- 1 Bar‐Shalom, Y., Daum, F., & Huang, J. (2009, December). The Probabilistic Data Association Filter. IEEE Control Systems Magazine
- 2 Bar‐Shalom, Y., & Li, X.‐R. (1995). Multitarget‐Multisensor Tracking: Principles and Techniques.
- 3 Blom, H. A. P., & Bar‐Shalom, Y. (1988). The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients. IEEE Transactions on Automatic Control, 33
- 4. Braca, P., Vespe, M., Maresca, S., & Horstmann, J. (2012). A Novel Approach to High Frequency Radar Ship Tracking Exploiting Aspect Diversity. Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, 6895‐6898.
- 5. Doucet, A., Godsill, S., & Andrieu, C. (2000, July). On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing, 10(3), 197–208. Retrieved from http://dx.doi.org/10.1023/A: 1008935410038 doi: 10.1023/A:1008935410038
- 6. Doucet, A., Smith, A., de Freitas, N., & Gordon, N. (2001). Sequential Monte Carlo methods in practice. Springer New York. Retrieved from https://books.google.de/ books?id=uxX‐koqKtMMC
- 7. Fortmann, T., Bar‐Shalom, Y., & Scheffe, M. (1983, Jul). Sonar tracking of multiple targets using joint probabilistic data association. IEEE Journal of Oceanic Engineering, 8(3), 173184. doi: 10.1109/JOE.1983.1145560
- 8. Glass, J. D., Blair, W. D., & Bar‐Shalom, Y. (2013). IMM Estimators with Unbiased Mixing for Tracking Targets Performing Coordinated Turns. Proceedings IEEE Aerospace Conference.
- 9. Gordon, N., Salmond, D., & Smith, A. (1993, April). Novel approach to nonlinear/non‐gaussian bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), 140, 107113(6)
- 10. Guerriero, M., Willett, P., Coraluppi, S., & Carthel, C. (2008). Radar/AIS Data Fusion and SAR tasking for Maritime Surveillance. In International Conference on Information Fusion (Vol. 11th).
- 11 Heymann F., Banyś P., Sáez‐Martínez C. (2015). Radar Image Processing and AIS Target Fusion. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 9, No. 3, pp. 443‐448.
- 12. Hue, C., & Le Cadre, J.‐P., & Pérez, P. (2002, July). Tracking multiple objects with particle filtering. IEEE Transactions on Aerospace and Electronic Systems, 38(3).
- 13 Isard, M., & Blake, A. (1998, August). Condensation conditional density propagation for visual tracking. In (Vol. 28, p. 5‐28).
- 14 Isard, M., & MacCormick, J. (2001). BraMBLe: A Bayesian multiple‐blob tracker. In Eighth IEEE International Conference on Computer Vision (Vol. 2, p. 34‐41).
- 15 Julier, S. J., & Uhlmann, J. K. (1997). A New Extension of the Kalman Filter to Nonlinear Systems. In Proc. of AeroSense: The 11th Int. Symp. on Aerospace/Defence Sensing, Simulation and Controls. (pp. 182–193)
- 16 Kazimierski, W., & Stateczny, A. (2015). Radar and Automatic Identification System Track Fusion in an Electronic Chart Display and Information System. The Journal of Navigation(68), 1141‐1154.
- 17 Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state‐ofthe‐art. Information Fusion, 14(1), 28 44. doi: http://dx.doi.org/ 10.1016/j.inffus.2011.08.00
- 18 Kim, C., Li, F., Ciptadi, A., & Rehg, J. M. (2015, Dec). Multiple hypothesis tracking revisited. In 2015 IEEE International Conference on Computer Vision (ICCV) (p. 4696‐4704). doi: 10.1109/ICCV.2015.533
- 19 Kitagawa, G. (1996). Monte carlo filter and smoother for nongaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5(1), 1‐25.
- 20 Mahler, R. (2015, Oct). A brief survey of advances in randomset fusion. In Control, automation and information sciences (iccais), 2015 international conference on (p. 62‐67). doi: 10.1109/ICCAIS.2015.7338726
- 21 Mazzarella, F., & Vespe, M. (2015, April). SAR Ship Detection and Self‐Reporting Data Fusion Based on Traffic Knowledge. IEEE Geoscience and Remote Sensing Letters.
- 22 Perera, L. P., Ferrari, V., Santos, F. P., Hinostroza, M. A., & Soares, C. G. (2015, APRIL). Experimental Evaluations on Ship Autonomous Navigation and Collision Avoidance by Intelligent Guidance. IEEE Journal of Oceanic Engineering, 40.
- 23 Pulford, G. W. (2005, October). Taxonomy of multiple target tracking methods. IEEE Proceedings Radar, Sonar and Navigation, 152(5), 291‐304. doi: 10.1049/ip‐rsn:20045064
- 24 Schuhmacher, D., Vo, B. T., & Vo, B. N. (2008, June). On performance evaluation of multi‐object filters. In Information fusion, 2008 11th international conference on (p. 1‐8)
- 25 Siegert, G., Banyś, P., & Heymann, F. (2016, July). Improving the Maritime Traffic Situation Assessment for a Single Target in a Multisensor Environment. In Maritime knowledge discovery and anomaly detection workshop proceedings (p. 7882). Ispra, Italy: European Commission Joint Research Center. doi: 10.2788/025881
- 26 Siegert, G., Banyś, P., Hoth, J., & Heymann, F. (2017, February). Counteracting the Effects of GNSS Jamming in a Maritime Multi‐Target Scenario by Fusing AIS with Radar Data. In ION International Technical Meeting. Monterrey, CA, USA: International Organization of Navigation
- 27 Siegert, G., Banyś, P., Martínez, C. S., & Heymann, F. (2016, April). EKF Based Trajectory Tracking and Integrity Monitoring of AIS Data. In IEEE/ION Position, Location and Navigation Symposium PLANS (p. 887 897). Savannah, GA: IEEE.
- 28 Tugnait, J. K. (2003, June). Tracking of multiple maneuvering targets in clutter using multiple sensors, imm and jpda coupled filtering. In American control conference, 2003. proceedings of the 2003 (Vol. 2, p. 1248‐1253). doi: 10.1109/ ACC.2003.1239759
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Bibliografia
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