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On the development of practical nonlinear filters. (English) Zbl 0291.93052

MSC:
93E10 Estimation and detection in stochastic control theory
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[1] Ho, Y.C.; Lee, R.C.K., A Bayesian approach to problems in stochastic estimation and control, IEEE trans. auto control, AC9, 333-339, (1964)
[2] Kalman, R.E., A new approach to linear filtering and prediction problems, J. basic engr., 82D, 35-45, (March 1960)
[3] Sorenson, H.W., Comparison of Kalman, Bayesian and maximum likelihood estimation techniques, (), 119-142
[4] Jazwinski, A.H., Stochastic processes and filtering theory, (1970), Academic New York · Zbl 0203.50101
[5] Sorenson, H.W., Kalman filtering techniques, () · Zbl 0178.44402
[6] Fitzgerald, R.J., Divergence of the Kalman filter, IEEE trans. auto. control, AC-16, 736-747, (1971)
[7] Mehra, R.K., A comparison of two nonlinear filters for ballistic trajectory estimation, (), 273-280
[8] Whitcombe, D.W., Pseudostate measurements applied to recursive nonlinear filtering, (), 278-281
[9] Sorenson, H.W.; Sacks, J.E., Recursive fading memory filtering, Info. sci., 3, 101-119, (1971) · Zbl 0225.93041
[10] Sorenson, H.W.; Stubberud, A.R., Recursive filtering for systems with small but nonnegligible nonlinearities, Int. J. control, 7, 271-280, (1968) · Zbl 0175.38902
[11] Athans, M.; Wishner, R.P.; Bertolini, A., Suboptimal state estimators for continuous-time nonlinear systems from discrete noisy measurements, IEEE trans. auto. control, AC-13, 504-514, (1968)
[12] Sorenson, H.W.; Stubberud, A.R., Nonlinear filtering by approximation of the a posteriori density, Int. J. control, 8, 33-51, (1968) · Zbl 0176.08302
[13] Kizner, W., Optimal nonlinear estimation based on orthogonal expansions, JPL tech. rep. 32-1366, (1969)
[14] Srinivasan, K., State estimation by orthogonal expansion of probability distributions, IEEE trans. auto. control, AC15, 3-10, (1970)
[15] Magill, D.T., Optimal adaptive estimation of sampled stochastic processes, IEEE trans. auto. control, AC10, 434-439, (1965)
[16] Hilborn, C.G.; Lainiotis, D.G., Optimal estimation in the presence of unknown parameters, IEEE trans. syst. sci. cybernet., SSC5, 38-43, (1969) · Zbl 0184.22002
[17] Bucy, R.S., Bayes’ theorem and digital realization for nonlinear filters, J. astronaut. sic., 17, 80-94, (1969)
[18] Bucy, R.S.; Senne, K.D., Realization of optimum discrete-time nonlinear estimators, (), 6-17 · Zbl 0269.93070
[19] Alspach, D.L.; Sorenson, H.W., Approximation of density function by a sum of gaussians for nonlinear Bayesian estimation, (), 19-31 · Zbl 0313.93044
[20] Center, J.L., Practical nonlinear filtering of discrete observations by generalized least-squares approximation of the conditional probability distribution, (), 88-99
[21] Center, J.L., Practical nonlinear filtering based on generalized least-squares approximation of the conditional probability distribution, () · Zbl 0314.93031
[22] de Figueiredo, R.J.P.; Jan, J.G., Spline filters, (), 127-138
[23] Sorenson, H.W.; Alspach, D.L., Recursive Bayesian estimation using Gaussian sums, Automatica, 7, 465-479, (1971) · Zbl 0219.93020
[24] Alspach, D.L.; Sorenson, H.W., Nonlinear Bayesian estimation using Gaussian sum approximations, IEEE trans. auto. control, AC17, 439-448, (1972) · Zbl 0264.93023
[25] Aoki, M., Optimization of stochastic systems, (1967), Academic New York
[26] Cameron, A.V., Control and estimation of linear systems with non-Gaussian a priori distributions, ()
[27] Lainiotis, D.G.; Lainiotis, D.G., Optimal adaptive estimation: structure and parameter adaptation, (), AC16, 160-170, (1971) · Zbl 0229.68034
[28] Buxbaum, P.J.; Haddad, R.A., Recursive optimal estimation for a class of non-Gaussian processes, (), 375-399
[29] Lainiotis, D.G., Optimal nonlinear estimation, Int. J. control, 14, (1971) · Zbl 0225.93034
[30] Lo, J.T.; Lo, J.T., Finite-dimensional sensor orbits and optimal nonlinear filtering, (), IT18, (1972) · Zbl 0246.93042
[31] Park, S.K.; Lainiotis, D.G., Monte-Carlo study of the optimal nonlinear state-vector estimator for linear model and non-Gaussian initial state, Int. J. control, 16, (1972) · Zbl 0246.93035
[32] Mendel, J.M., Computational requirements for a discrete Kalman filter, IEEE trans. auto. control, AC16, 748-758, (1971)
[33] Tse, E., Parallel computation of the conditional Mean state estimate for nonlinear systems, (), 385-394
[34] Shore, J.E., Second thoughts on parallel processing, Computers elect. engr., 1, 95-110, (1973) · Zbl 0335.68041
[35] Hecht, C., Digital realization of nonlinear filters, (), 152-158
[36] Kasemratanasunti, W.; Klein, R.L., Quadrature formulae realization of nonlinear estimators, () · Zbl 0289.93051
[37] Tse, E.; Larson, R.E., Optimum quantization and parallel algorithms for nonlinear state estimation, (), 260-265
[38] Bucy, R.S.; Merritt, M.J.; Miller, D.S., Hybrid computer synthesis of optimal discrete nonlinear filters, (), 59-87 · Zbl 0314.93035
[39] Bucy, R.S.; Hecht, C.; Senne, K.D., An Engineer’s guide to building nonlinear filters, Frank J. seiler research laboratory report SRL-TR-72-0004, (May 1972)
[40] Korn, G.A., Back to parallel computation: proposal for a completely new on-line simulation system using standard mini-computers for low-cost multiprocessing, Simulation, 19, 37-46, (1972)
[41] Wishner, R.P.; Tabaczynski, J.A.; Athans, M., A comparison of three nonlinear filters, Automatica, 5, 487-496, (1969) · Zbl 0214.47303
[42] Kushner, H.J., Approximations to nonlinear filters, IEEE trans. auto. control, AC12, 546-556, (1969)
[43] Schwartz, L.; Stear, E.B., A computational comparison of several nonlinear filters, IEEE trans. auto. control, AC13, 83-86, (1969)
[44] Bucy, R.S., Realization of nonlinear filters, (), 51-58 · Zbl 0314.93034
[45] Bucy, R.S.; Hecht, C.; Senne, K.D., An application of Bayes law estimation to non-linear phase demodulation, (), 23-35
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