Comparative Investigation of Advanced Non-Linear Control Algorithms for Undersea Sonar-Based Tracing Applications
Unscented Kalman Filter (UKF) and Cubature Kalman Filter (CKF) use Gaussian assumed density approximations. SimoSarkka has shown that UKF is a generalized one of CKF. Extensive performance evaluation of UKF and CKF with respect to bearings-only target tracking problem in Monte-Carlo simulation is carried out and the results are presented. It is observed that UKF is better than that of CKF for bearings-only target tracking problem.
S.C Nardone, A.G. Lindgren and K.F. Gong, “Fundamental properties and performance of conventional bearings only target motion analysis”, IEEE Trans. Automatic Control Vol. Ac -29, No.9, September 1984, pp 775-787.
S. Koteswara Rao, “Maximum Likelihood Estimator for Bearings-only Passive Target Tracking in Electronic Surveillance Measure and Electronic Warfare Systems”, Defence Science journal, Vol. 60, No.2, DESIDOC, Delhi, Mar. 2010, pp. 197-203.
S. Koteswara Rao, “Pseudo linear estimator for Bearings only Passive Target Tracking”, IEE Proc., Radar, Sonar, Navigation, Vol.148, No.1, Feb’01, pp 16-22.
A. G. Lindgren, K. F. Gong, “Position and velocity estimation via bearing observations”, IEEE Trans. Aerospace. Electro. Syst., Vol. 14, 1978, pp 564-577.
V.J. Aidala, “Kalman filter behaviour in bearings only tracking applications”, IEEE Trans. Aerospace. Electro. System, Vol. 15, No.1, January 1979.
T.L. Song & J.L. Speyer, “A stochastic Analysis of a modified gain extended Kalman filter with applications to estimation with bearing only measurements”, IEEE Trans. Automatic Control, Vol. 30, No.10, October 1985, pp 940-949.
W. Grossman, “Bearings only tracking A hybrid coordinate system Approach”, Journal of Guidance, Vol. 17, No.3, May - June, 1994, pp 451-457.
P.J. Galkowski and M.A. Islam, “An alternative derivation of the modified gain function of Song and Speyer”, IEEE Trans. Automatic Control, Vol. 36, No.11, November 1991, pp 1323-1326.
S. Koteswara Rao, “Modified gain extended Kalman filter with application to bearings-only passive maneuvering target tracking”, IEE Proceedings on Radar, Sonar & Navigation, Vol. 152, No.4, August 2005, pp 239-244.
E.A. Wan and R. van der Merwe, “The unscented Kalman filter for nonlinear estimation”, In Proceeding of IEEE Symposium 2000 on Adaptive Systems for Signal Processing, Communication and Control, Lake Louse, Alberta, Canada, October 2000.
S. Koteswara Rao, K. Raja Rajeswari and K. S. Lingamurty, “Unscented Kalman Filter with Application to Bearings-only Target Tracking”. IETE Journal of Research, Vol. 55, No. 2, pp. 63-67, Mar-Apr. 2009.
Dan Simon, “Optimal State Estimation: Kalman, H and nonlinear Approximations”, Wiley, 2006.
B. Ristick, S. Arulampalam and N. Gordon, “Beyond Kalman Filters–Particle filters for tracking applications”, ArtechHouse, DSTO, 2004.
Ito, K. and Xiong K,” Gaussian filters for nonlinear filtering problems”, IEEE transactions on automatic control, vol. 45, no. 5, May 2000 .
Simosarkka,”Bayesian Filtering and smoothing”, Cambridge University Press, 2013.
Wu, Y., Hu, D., Wu, M., and Hu,X., ”Unscented Kalman filtering for additive noise case” augumented verses nonaugumented”, IEEE Signal Processing Letters, American Control Conference, Portland, OR, USA, June 2005, pp.4051-4055.
JouniHartikaninen, ArnoSolin, and SimoSarkka, optimal filtering with Kalman filters and smoothers”, Department of Biomedical Engineering and Computational Science, Aalto University school of science, August 16, 2011.
Pei H. Leong, SanjeevArulapalam, Tharaka A. Lamahewa, Thushara D. Abhayapala,” A Gaussian-Sum Based Cubature Kalman Filter for Bearings-Only Tracking”, IEEE Trans. on Aerospace and Electronic Systems, Vol.49.No.2,April.2013.
Arasaratnam,I. and Haykin, S., and Ellirot,”Cubature Kalman filters,” IEEE Transactions on automatic controls, Vol.54, No.6,pp.1254-1269.
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