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.

[B1]	S. Blackman and R. Popoli. Design and Analysis of Modern Tracking Systems. Archtech House, Norwood, MA. 1999.
[B2]	Y. Bar-Shalom, P. K. Willett, and X. Tian. Tracking and Data Fusion: A Handbook of Algorithms. Yaakov Bar-Shalom Publishing. 2011.
[B3]	B. Ristic, S. Arulampalam, and N. Gordon. Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House. 2004.

Articles used throughout the course:

[7]	Bar-Shalom, Y., Blackman, S. S., and Fitzgerald, R. J. Dimensionless score function for multiple hypothesis tracking. IEEE Transactions on Aerospace and Electronic Systems 43(1):392-400, January 2007.
[8]	Bar-Shalom, Y., Daum, F., and Huang, J. The probabilistic data association filter. IEEE Control Systems Magazine 29(6):82-100, November 2009.
[9]	Bertsekas, D. P. "Auction Algorithms," In Internal report MIT, Cambridge, MA, USA, .
[11]	Blackman, S. S. Multiple hypothesis tracking for multiple target tracking. IEEE Aerospace and Electronic Systems Magazine 19(1):5-18, January 2004.
[4]	Blom, Henk A. P. and Bar-Shalom, Yaakov. The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients. IEEE Transactions on Automatic Control 33(8):780-783, August 1988.
[13]	Chong, C.-Y., Mori, S., and Reid, D. B. "Forty Years of Multiple Hypothesis Tracking: A Review of Key Developments," In 21st International Conference on Information Fusion (FUSION), Cambridge, UK, 2018.
[16]	Forsling, R., Julier, S. and Hendeby, G. Matrix-Valued Measures and Wishart Statistics for Target Tracking Applications. IEEE Transactions on Aerospace and Electronic Systems 61(5):12234-12244, May 2025.
[18]	Forsling, R., Noack, B., and Hendeby, G. A quarter century of covariance intersection: Correlations still unknown?. IEEE Control Systems Magazine 44(2):81–105, April 2024.
[24]	Fränken, D. , Schmidt, M., and Ulmke, M. "Spooky action at a distance" in the cardinalized probability hypothesis density filter. IEEE Transactions on Aerospace and Electronic Systems, 45(4):, 2009.
[21]	Granström, K., Baum, M., and Reuter, S. Extended object tracking: Introduction, overview and applications. Journal of Advances in Information Fusion 12(2):139-174, December 2017.
[20]	Granström, K., Svensson, L., Reuter, S., Xia, Y., and Fatemi, M. Likelihood-based data association for extended object tracking using sampling methods. IEEE Transactions on Intelligent Vehicles 3(1):30-45, March 2018.
[28]	García-Fernández, Á. F., Williams, J. L., Granström, K., and Svensson, L.}, Poisson Multi-Bernoulli Mixture Filter: Direct Derivation and Implementation. IEEE Transactions on Aerospace and Electronic Systems 54(4):1883-1901, 2018.
[25]	Hendeby, G. and Karlsson, R. "Gaussian mixture PHD filtering with variable probability of detection," In 17th International Conference on Information Fusion, Salamanca, Spain, 2014.
[5]	Kirubarajan, T. and Bar-Shalom, Y. Kalman filter versus IMM estimator: when do we need the latter?. IEEE Transactions on Aerospace and Electronic Systems 39(4):1452-1457, October 2003.
[30]	Lewis, D., Reijers, W. and Pandit, H. "Ethics canvas manual," In Technical report, ADAPT Centre, Trinity College Dublin & Dublin City University, 2017.
[1]	Li, X. Rong and Jilkov, Vesslin P. Survey of Maneuvering Target Tracking. Part I: Dynamic Models. IEEE Transactions on Aerospace and Electronic Systems 39(4):1333-1364, October 2003.
[2]	Li, X. Rong and Jilkov, Vesslin P. "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.
[3]	Li, X. Rong and Jilkov, Vesslin P. A Survey of Maneuvering Target Tracking. Part V: Multiple-Model Methods. IEEE Transactions on Aerospace and Electronic Systems 41(4):1255-1321, October 2005.
[22]	Mahler, R. P. S. Multitarget Bayes Filtering via First-Order Multitarget Moments. IEEE Transactions on Aerospace and Electronic Systems 39(4):1152--1178}, 2003.
[15]	Rahmathullah, A. S., García-Fernández, Á. F., and Svensson, L. "Generalized optimal sub-pattern assignment metric," In 20th International Conference on Information Fusion (FUSION), Xi'an, China, July 10-13 2017.
[12]	Reid, D. B. An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control 24(6):843-854, December 1979.
[29]	Reijers, W., Koidl, K., Lewis, D. , Pandit, H. J. , and Gordijn, B. "Discussing ethical impacts in research and innovation: The Ethics Canvas," In This Changes Everything — ICT and Climate Change: What Can We Do?, Cham, Cham, 2018.
[26]	Reuter, S., Vo, B.-T., Vo, B.-N., and Dietmayer, K. The labeled multi-Bernoulli filter. IEEE Transactions on Signal Processing 62(12):3246–3260, 2014.
[19]	Ristic, B, Vo, B.-T., Vo, B.-N., and Farina, A. A tutorial on Bernoulli filters: Theory, implementation and applications. IEEE Transactions on Signal Processing 61(13):3406–3430, July 2013.
[6]	Singh, S. K., Premalatha, M., and Nair, G. "Ellipsoidal gating for an airborne track while scan radar," In Proceedings of the International Radar Conference, Alexandria, VA, May 8-11 1995.
[17]	Uhlmann, J. K. Covariance consistency methods for fault-tolerant distributed data fusion. Information Fusion 4(3):201-215, June 2003.
[23]	Vo, B.-N. and Ma, W.-K. The Gaussian mixture probability hypothesis density filter. IEEE Transactions on Signal Processing 54(11):4091–4104, 2006.
[10]	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, New York, NY, USA, 2015.
[27]	Vo, B.-N. , Vo, B.-T., and Phung, D. Labeled random finite sets and the Bayes multi-target tracking filter. IEEE Transactions on Signal Processing 62(24):6554–6567, 2014.
[14]	Williams, J. Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member. 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.