Literature

For a fairly complete overview of the target tracking problem, methods, and algorithm collected at one place, the flowing books are good entry points.

• S. Blackman and R. Popoli. Design and Analysis of Modern Tracking Systems. Archtech House, Norwood, MA. 1999.
• Y. Bar-Shalom, P. K. Willett, and X. Tian. Tracking and Data Fusion: A Handbook of Algorithms. Yaakov Bar-Shalom Publishing. 2011.

Articles used throughout the course:

• Bar-Shalom, Y., Blackman, S. S., and Fitzgerald, R. J. “Dimensionless score function for multiple hypothesis tracking“, in IEEE Transactions on Aerospace and Electronic Systems, 43(1):392-400, January 2007.
• Bar-Shalom, Y., Daum, F., and Huang, J. “The probabilistic data association filter“, in IEEE Control Systems Magazine, 29(6):82-100, Nov. 2009.
• Bertsekas, D. “Auction algorithms“.
• Blackman, S. “Multiple hypothesis tracking for multiple target tracking“, in IEEE Transactions on Aerospace and Electronic Systems, 19(1):5-18, 2004.
• Blom, Henk A. P. and Bar-Shalom, Yaakov. “The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients“, in IEEE Transactions on Automatic Control 33(8), August 1988.
• Boers, Y., Driessen, H., Torstensson, J., Trieb, M., Karlsson, R., and Gustafsson, F. “Track-before-detect algorithm for tracking extended targets“, in IEE Proc on Radar Sonar Navigation, 153(4):345-351, Aug. 2006.
• Chong, C., Mori, S., and Reid, D. “Forty years of multiple hypothesis tracking - A review of key developments“, in Proceedings of the 21st International Conference on Information Fusion, Cambridge, UK, July 2018.
• Fränken, D., Schmidt, M., and Ulmke, M. ”“Spooky action at a distance” in the cardinalized probability hypothesis density filter“, in IEEE Transactions on Aerospace and Electronic Systems 45(4):1657-1664, 2009.
• García-Fernández, Á. F., Williams, J. L., Granström, K., and Svensson, L. “Poisson multi-Bernoulli mixture filter: direct derivation and implementation“, in IEEE Transactions on Aerospace and Electronic Systems 54(4), Aug. 2018.
• Granström, K., Baum, M., and Reuter, S. “Extended object tracking: Introduction, overview and applications“, in Journal of Advances in Information Fusion 12(1), Dec. 2017.
• Granström, K., Svensson, L., Reuter, S. , Xia, Y., and Fatemi, M. “Likelihood-based data association for extended object tracking using sampling methods“, in IEEE Transactions on Intelligent Vehicles, 3(1), Mar. 2018.
• Guerriero, M., Svensson, L., Svensson, D. and Willett, P. “Shooting two birds with two bullets: How to find minimum mean OSPA estimates“, in Proceedings of the 13st International Conference on Information Fusion, Edinburgh, UK, July 2010.
• Kirubarajan, T. and Bar-Shalom, Y., “Kalman filter versus IMM estimator: when do we need the latter?“, in IEEE Transactions on Aerospace and Electronic Systems 39(4):1452-1457, Oct. 2003.
• Li, X. Rong and Jilkov, Vesslin P. “Survey of Maneuvering Target Tracking. Part I: Dynamic Models“, in IEEE Transactions on Aerospace and Electronic Systems 39(4), Oct 2003.
• Li, X. Rong and Jilkov, Vesslin P. “A Survey of Maneuvering Target Tracking. Part III: Measurement Models“, in Proceedings of SPIE — The International Society for Optical Engineering: Signal and Data Proceedings of Small Targets, Orlando, Juli 2001.
• Li, X. Rong and Jilkov, Vesslin P. “A Survey of Maneuvering Target Tracking. Part V: Multiple-Model Methods“, in IEEE Transactions on Aerospace and Electronic Systems 41(4), Oct 2005.
• Mahler, R. P. S. “Multitarget Bayes Filtering via First-Order Multitarget Moments“, in IEEE Transactions on Aerospace and Electronic Systems 39(4):1152-1178, 2003.
• Mahler, R. P. S. “PHD filters of higher order in target number“, in Proceedings of SPIE 6235, May 2006.
• Mahler, R. P. S. and Zajic, T. “Multitarget filtering using a multitarget first-order moments“, in Proceedings of SPIE 4380, Aug, 2001.
• Noack, B. Sijs, J. and Hanebeck, U. D. “Inverse covariance intersection: New insights and properties“, in proceedings of 20st International Conference on Information Fusion, Xi’an, China, July 2017.
• Nygårds, J., Deleskog, V., and Hendeby, G. “Safe fusion compared to established distributed fusion methods“, in Proceedings of IEEE lntemational Conference on Multisensor Fusion and Integration for Intelligent Systems, Baden-Baden, Germany, Sept. 2016.
• Olofsson, J. “On Multi-UAS Sea Ice Monitoring“. Phd thesis, Norwegian University of Science and Technology, 2019.
• Reid. D. “An algorithm for tracking multiple targets“, in IEEE Transactions on Automatic Control, 24(6):843-854, Dec. 1979.
• Reuter, S., Vo, B.-T., Vo, B.-N., and Dietmayer, K. “The Labeled Multi-Bernoulli Filter“, in IEEE Transactions on Signal Processing 62(12):3246-3260, 2014.
• Ristic, B. Vo, B.-T., Vo, B.-N., and Farina, A. “A Tutorial on Bernoulli Filters: Theory, Implementation and Applications” in IEEE Transactions Signal Processing 61(13):3406-3430, Jul. 2013.
• Singh, S. K., Premalatha, M., and Nair, G. “Ellipsoidal gating for an airborne track while scan radar“, in Proceedings of the International Radar Conference, pages 334-339, Alexandria, VA, May 8-11 1995.
• Uhlmann, J. K. “Covariance consistency methods for fault-tolerant distributed data fusion” in Information Fusion 4(3):201-215, 2003.
• Vo, B.-N., Mallick, M., Bar-Shalom, Y., Coraluppi, S., Osborne III, R., Mahler, R., and Vo, B.-T. “Multitarget Tracking” in Wiley Encyclopedia of Electrical and Electronics Engineering, 2015.
• Vo, B.-N., Pasha, A., and Tuan, H. D. A Gaussian Mixture PHD Filter for Nonlinear Jump Markov Models” in Proceedings of the 45th IEEE Conference on Decision and Control, 2006.
• Vo, B.-N. , Vo, B.-T., and Phung, D. “Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter” in IEEE Transactions on Signal Processing 62(24):6554-6567, 2014.
• Vo, B.-T. and Vo, B.-N. “Labeled Random Finite Sets and Multi-Object Conjugate Priors” in IEEE Transactions on Signal Processing 61(13):3460-3475, 2013.
• Williams., “Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based MeMBer“, in IEEE Transactions on Aerospace and Electronic Systems, 51(3):1664-1687, July 2015.

Programming frameworks

This is an incomplete list of programming frameworks that can be used to speed up the development and evaluation of multi-target tracking solutions. For your production codes solutions, you most likely should tailor a solution for your specific problem. However, studying the structure of these is probably still a good ideas.

MATLAB: Sensor Fusion and Tracking Toolbox

This MATLAB toolbox includes algorithms and tools for designing, simulating, and testing systems that fuse data from multiple sensors to maintain situational awareness and localization.

Python: Stone Soup

Stone Soup is a software project to provide the target tracking and state estimation community with a framework for the development and testing of tracking and state estimation algorithms. The project is lead by the DSTL in the UK, but involves a number of different partners.