Computational Framework for Target Tracking Information Fusion Problems

References

• Ankerst M, Breunig MM, Kriegel HP, Sander J (1999) Optics: Ordering points to identify the clustering structure. SIGMOD Rec. 28(2):49–60.Crossref, Google Scholar
• Bandelt HJ, Crama Y, Spieksma FC (1994) Approximation algorithms for multi-dimensional assignment problems with decomposable costs. Discrete Appl. Math. 49(1–3):25–50.Crossref, Google Scholar
• Bandelt HJ, Maas A, Spieksma FC (2004) Local search heuristics for multi-index assignment problems with decomposable costs. J. Oper. Res. Soc. 55(7):694–704.Crossref, Google Scholar
• Berclaz J, Fleuret F, Turetken E, Fua P (2011) Multiple object tracking using k-shortest paths optimization. IEEE Trans. Pattern Anal. Machine Intelligence 33(9):1806–1819.Crossref, Google Scholar
• Brandes U, Gaertler M, Wagner D (2003) Experiments on graph clustering algorithms. Proc. Eur. Sympos. Algorithms (Springer, Berlin), 568–579.Google Scholar
• Burkard RE, Rudolf R, Woeginger GJ (1996) Three-dimensional axial assignment problems with decomposable cost coefficients. Discrete Appl. Math. 65(1–3):123–139.Crossref, Google Scholar
• Chao G, Sun S, Bi J (2021) A survey on multiview clustering. IEEE Trans. Artificial Intelligence 2(2):146–168.Crossref, Google Scholar
• Crama Y, Spieksma FC (1992) Approximation algorithms for three-dimensional assignment problems with triangle inequalities. Eur. J. Oper. Res. 60(3):273–279.Crossref, Google Scholar
• Elhamifar E, Vidal R (2013) Sparse subspace clustering: Algorithm, theory, and applications. IEEE Trans. Pattern Anal. Machine Intelligence 35(11):2765–2781.Crossref, Google Scholar
• Gurobi Optimization LLC (2022) Gurobi Optimizer reference manual. (August 10), https://www.gurobi.com.Google Scholar
• He Z, Xie S, Zdunek R, Zhou G, Cichocki A (2011) Symmetric nonnegative matrix factorization: Algorithms and applications to probabilistic clustering. IEEE Trans. Neural Networks 22(12):2117–2131.Crossref, Google Scholar
• Huang K, Sidiropoulos ND, Swami A (2013) Non-negative matrix factorization revisited: Uniqueness and algorithm for symmetric decomposition. IEEE Trans. Signal Processing 62(1):211–224.Crossref, Google Scholar
• Karapetyan D, Gutin G (2011) Local search heuristics for the multidimensional assignment problem. J. Heuristics 17(3):201–249.Crossref, Google Scholar
• Khan K, Rehman SU, Aziz K, Fong S, Sarasvady S (2014) DBSCAN: Past, present and future. Proc. 5th Internat. Conf. Applications f Digital Inform. Web Tech. (IEEE, New York), 232–238.Google Scholar
• Kuang D, Ding C, Park H (2012) Symmetric nonnegative matrix factorization for graph clustering. Proc. SIAM Internat. Conf. Data Mining (SIAM, Philadelphia), 106–117.Google Scholar
• Kumar A, Daumé H (2011) A co-training approach for multi-view spectral clustering. Proc. 28th Internat. Conf. Machine Learn. (Omnipress, Madison, WI), 393–400.Google Scholar
• Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. Leen T, Dietterich T, Tresp V, eds. Advances in Neural Information Processing Systems, vol. 13 (MIT Press, Cambridge, MA).Google Scholar
• Mahler RP (2014) Advances in Statistical Multisource-Multitarget Information Fusion (Artech House, Boston).Google Scholar
• Murphey RA, Pardalos PM, Pitsoulis LS (1997) A greedy randomized adaptive search procedure for the multitarget multisensor tracking problem. Netw. Des. Connect. Facil. Locat. 40:277–302.Google Scholar
• Natu S, Date K, Nagi R (2020) GPU-accelerated Lagrangian heuristic for multidimensional assignment problems with decomposable costs. Parallel Comput. 97:102666.Google Scholar
• Pierskalla WP (1968) Letter to the editor: The multidimensional assignment problem. Oper. Res. 16(2):422–431.Link, Google Scholar
• Poore AB (1994) Multidimensional assignment formulation of data association problems arising from multitarget and multisensor tracking. Comput. Optim. Appl. 3(1):27–57.Crossref, Google Scholar
• Poore AB, Rijavec N (1993) A Lagrangian relaxation algorithm for multidimensional assignment problems arising from multitarget tracking. SIAM J. Optim. 3(3):544–563.Crossref, Google Scholar
• Poore AB, Robertson AJ III (1997) A new Lagrangian relaxation based algorithm for a class of multidimensional assignment problems. Comput. Optim. Appl. 8(2):129–150.Crossref, Google Scholar
• Pourbahrami S, Balafar MA, Khanli LM, Kakarash ZA (2020) A survey of neighborhood construction algorithms for clustering and classifying data points. Comput. Sci. Rev. 38:100315.Crossref, Google Scholar
• Rai N, Negi S, Chaudhury S, Deshmukh O (2016) Partial multi-view clustering using graph regularized NMF. Proc. 23rd Internat. Conf. Pattern Recognition (IEEE, New York), 2192–2197.Google Scholar
• Reynen O, Vadrevu S, Nagi R, LeGrand K (2019) Large-scale multi-dimensional assignment: Problem formulations and GPU accelerated solutions. Proc. 22nd Internat. Conf. Inform. Fusion (IEEE, Piscataway, NJ).Google Scholar
• Vadrevu S, Nagi R (2023) A GPU accelerated dual-ascent algorithm for the multidimensional assignment problem in a multitarget tracking application. IEEE Trans. Automation Sci. Engrg. 20(3):1706–1720.Google Scholar
• Vogiatzis C, Pasiliao EL, Pardalos PM (2014) Graph partitions for the multidimensional assignment problem. Comput. Optim. Appl. 58(1):205–224.Crossref, Google Scholar
• Walteros JL, Vogiatzis C, Pasiliao EL, Pardalos PM (2014) Integer programming models for the multidimensional assignment problem with star costs. Eur. J. Oper. Res. 235(3):553–568.Crossref, Google Scholar
• Yang T, Liu J, Faiz TI, Vogiatzis C, Noor-E-Alam M (2024) Computational framework for target tracking information fusion problems. http://dx.doi.org/10.1287/ijoc.2023.0016.cd, https://github.com/INFORMSJoC/2023.0016.Google Scholar
• Yin Q, Wu S, He R, Wang L (2015) Multi-view clustering via pairwise sparse subspace representation. Neurocomputing 156:12–21.Crossref, Google Scholar