References
- S .S. Blackman. Multiple-Target Tracking with Radar Applications. Artech House, Inc, 1986
- M. I. Skolnik. Introduction to Radar Systems. McGraw-Hill, New York, NY, USA, 1980
- N. Levanon. Radar Principles. A Wiley-Interscience Publication, New York, NY, USA, 1988
- T. E. Fortmann, Y. Bar-Shalom, and M. Scheffe. Sonar tracking of multiple targets using joint probabilistic data association. IEEE Journal of Oceanic Engineering, OE-8(3):173-- 183, Jul 1983
- Y. Bar-Shalom. Extension of the probabilistic data association filter in multi-target tracking. In Proceedings of the 5th Symposium on Nonlinear Estimation, pages 16--21, Sep. 1974
- Y. Bar-Shalom and E. Tse. Tracking in a cluttered environment with probabilistic data association. Automation, 11:451--460, Sep. 1975
- Y. Bar-Shalom and T. E. Fortmann. Tracking and Data Association. Academic Press, Inc, 1988
- Y. Bar-Shalom and X. R. Li. Estimation and tracking: principles, techniques, and software. Artech House, Inc, 1993
- Y. Bar-Shalom and X. R. Li. Multitarget-Multisensor Tracking: Principles and Techniques. YBS Publishing, Storrs, CT, 1995
- D. Sengupta and R. A. Iltis. Neural solution to the multitarget tracking data association problem. IEEE Trans. Aerosp. Electron. Syst., AES-25:96--108, Jan. 1989 https://doi.org/10.1109/7.18666
- Henry Leung. Neural network data association with application to multiple-target tracking. Opt. Eng., 35(3):693--700, Mar. 1996 https://doi.org/10.1117/1.600661
- Gilles P. Mauroy and Edward W. Kamen. Multiple target tracking using recurrent neural networks. In Proceedings of ICNN'97, 1997 https://doi.org/10.1109/ICNN.1997.614223
- M. Winter and G. Favier. A neural solution for multitarget tracking based on a maximum likelihood approach. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 1141--1144, May 1998 https://doi.org/10.1109/ICASSP.1998.675471
- J. J. Hopfield. Neural networks and physical systems with emergent collective computational abilities. In Proceedings of the National Academy of Science, pages 2554--2558, 1982 https://doi.org/10.1073/pnas.79.8.2554
- J. J. Hopfield and D. W. Tank. Neural computation of decisions in optimization problems. Biological Cybernetics, 52:141--152, 1985 https://doi.org/10.1007/BF00339943
- Y. W. Lee and H. Jeong. A MAP estimate of optimal data association for multi-target tracking. In Proceedings of ICSPAT, 1997
- H. Jeong and J. H. Park. Multiple target tracking using constrained MAP data association. Electronics Letters, 35(1):25--26, Jan. 1999 https://doi.org/10.1049/el:19990002
- A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. Ser. B, 39(1):1--38, 1997
- G. J. McLachlan and T. Krishnan. The EM Algorithm and Extensions. A Wiley-Interscience Publication, 1996
- D. Avitzour. A maximum likelihood approach to data association. IEEE Trans. Aerosp. Electron. Syst., 28(2):560--565, Apr. 1992 https://doi.org/10.1109/7.144581
- H. Gauvrit, J. P. Le Cadre, and C.Jauffret. A formulation of multitarget tracking as an incomplete data problem. IEEE Trans. Aerosp. Electron. Syst., 33(4):1242--1257, Oct. 1997 https://doi.org/10.1109/7.625121
- K. J. Molnar and J. W. Modestino. Application of the EM algorithm for the multitarget/multisensor tracking problem. IEEE Trans. Signal Processing, 46(1):115-- 129, Jan 1998 https://doi.org/10.1109/78.651193
- L. Frenkel and M. Feder. Recursive expectation-maximization (EM) algorithms for time-varying parameters with applications to multiple target tracking. IEEE Trans. Signal Processing, 47(2):306--320, Feb. 1999 https://doi.org/10.1109/78.740104
- R. E. Kalman. A new approach to linear filtering and prediction problems. Trans. ASME, (J. Basic Eng.,)), 82:34--45, Mar 1960
- R. E. Kalman and R. Bucy. New results in linear filtering and prediction theory. Trans. ASME, (J. Basic Eng.,)), 83:95--108, Mar 1961
- M. Grewal and A. Andrews. Kalman Filtering : Theory and Practice. Prentice-Hall, Englewood Cliffs, NJ 07632, USA, 1993
- P. Green. On use of the {EM} algorithm for penalized likelihood estimation. J. Roy. Statist. Soc. Ser. B, 52(3):443--452, 1990
- X. L. Meng and D. B. Rubin. Maximum likelihood estimation via the ECM algorithm: A general framework. Biometrika, 80:267--278, 1993 https://doi.org/10.1093/biomet/80.2.267
- M. Skolnik, editor. Radar Handbook. McGraw-Hill, New York, NY, USA, 1990
- D. G. Luenberger. Optimization by Vector Space Methods. John Wiley & Son, Inc., 1969
- Max Woodbury. Inverting modified matrices. Memorandum Report 42, Statistical Research Group, Princeton, 1950
- Alston S. Householder. The Theory of Matrices in Numerical Analysis. Dover Publications, New York, 1975
- X. L. Meng and D. B. Rubin. Using EM to obtain asymptotic variance-covariance matrices: the SEM algorithm. Journal of the American Statistical Association, 86:899--909, 1991 https://doi.org/10.2307/2290503
- T. Orchard and M. A. Woodbury. A missing information principle: Theory and application. In Proceedings of the Statistical Computing Section, pages 41--45. American Statistical Association, 1972