ISSN 2083-6473
ISSN 2083-6481 (electronic version)




Associate Editor
Tomasz Neumann

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
www http://www.transnav.eu
e-mail transnav@am.gdynia.pl
Validation of Radar Image Tracking Algorithms with Simulated Data
1 German Aerospace Centre (DLR), Neustrelitz, Germany
ABSTRACT: 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.
Bar-Shalom, Y., Daum, F., & Huang, J. (2009, December). The Probabilistic Data Association Filter. IEEE Control Systems Magazine.
Bar-Shalom, Y., & Li, X.-R. (1995). Multitarget-Multisensor Tracking: Principles and Techniques.
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.
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.
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
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
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
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.
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).
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).
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).
Isard, M., & Blake, A. (1998, August). Condensation conditional density propagation for visual tracking. In (Vol. 28, p. 5-28).
Isard, M., & MacCormick, J. (2001). BraMBLe: A Bayesian multiple-blob tracker. In Eighth IEEE International Conference on Computer Vision (Vol. 2, p. 34-41).
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).
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.
Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28 44. doi: http://dx.doi.org/ 10.1016/j.inffus.2011.08.001
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
Kitagawa, G. (1996). Monte carlo filter and smoother for nongaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5(1), 1-25.
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
Mazzarella, F., & Vespe, M. (2015, April). SAR Ship Detection and Self-Reporting Data Fusion Based on Traffic Knowledge. IEEE Geoscience and Remote Sensing Letters.
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.
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
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).
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
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.
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.
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
Citation note:
Heymann F., Hoth J., Banyś P., Siegert G.: Validation of Radar Image Tracking Algorithms with Simulated Data. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 11, No. 3, doi:10.12716/1001.11.03.18, pp. 511-518, 2017

Other publications of authors:

File downloaded 255 times

Important: TransNav.eu cookie usage
The TransNav.eu website uses certain cookies. A cookie is a text-only string of information that the TransNav.EU website transfers to the cookie file of the browser on your computer. Cookies allow the TransNav.eu website to perform properly and remember your browsing history. Cookies also help a website to arrange content to match your preferred interests more quickly. Cookies alone cannot be used to identify you.
Akceptuję pliki cookies z tej strony