Stochastic filtering theory: A discussion of concepts, methods, and results

[1]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[2]  C. Doléans-Dade,et al.  Equations differentielles stochastiques , 1977 .

[3]  G. Picci,et al.  On the Stochastic Realization Problem , 1979 .

[4]  Donald L. Snyder,et al.  Random point processes , 1975 .

[5]  Donald L. Snyder,et al.  Estimation and decision for observations derived from martingales: Part I, Representations , 1976, IEEE Trans. Inf. Theory.

[6]  H. Kushner.  Dynamical equations for optimal nonlinear filtering , 1967 .

[7]  P. Meyer,et al.  Séminaire de Probabilités XI , 1977 .

[8]  W. Wonham.  Some applications of stochastic difierential equations to optimal nonlinear ltering , 1964 .

[9]  P. Brémaud.  The Martingale Theory of Point Processes Over the Real Half Line Admitting an Intensity , 1975 .

[10]  H. Kunita.  Asymptotic behavior of the nonlinear filtering errors of Markov processes , 1971 .

[11]  J. H. Schuppen.  Filtering, Prediction and Smoothing for Counting Process Observations, a Martingale Approach , 1977 .

[12]  A. Willsky,et al.  Estimation for Rotational Processes with One Degree of Freedom , 1972 .

[13]  Thomas Kailath,et al.  A view of three decades of linear filtering theory , 1974, IEEE Trans. Inf. Theory.

[14]  J. Jacod.  Un théorème de représentation pour les martingales discontinues , 1976 .

[15]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[16]  Donald L. Snyder,et al.  How to track a swarm of fireflies by observing their flashes (Corresp.) , 1975, IEEE Trans. Inf. Theory.

[17]  J. Szpirglas,et al.  Modèle général de filtrage non linéaire et équations différentielles stochastiques associées , 1979 .

[18]  Adrian Segall,et al.  Dynamic file assignment in a computer network , 1976 .

[19]  Donald L. Snyder,et al.  Filtering and detection for doubly stochastic Poisson processes , 1972, IEEE Trans. Inf. Theory.

[20]  Marc Yor,et al.  Changes of filtrations and of probability measures , 1978 .

[21]  M. Yor.  Sur les théories du filtrage et de la prédiction , 1977 .

[22]  H. Kunita,et al.  Stochastic differential equations for the non linear filtering problem , 1972 .

[23]  G. Picci.  Stochastic realization of Gaussian processes , 1976, Proceedings of the IEEE.

[24]  E. Polak,et al.  System Theory , 1963 .

[25]  Séminaire de probabilités,et al.  Séminaire de probabilités IX, Université de Strasbourg , 1970 .

[26]  Adrian Segall,et al.  A martingale approach to modeling, estimation, and detection of jump processes (Ph.D. Thesis abstr.) , 1973, IEEE Trans. Inf. Theory.

[27]  Eugene Wong,et al.  Stochastic processes in information and dynamical systems , 1979 .

[28]  M. Skibinsky.  Adequate Subfields and Sufficiency , 1967 .

[29]  Norbert Wiener,et al.  Extrapolation, Interpolation, and Smoothing of Stationary Time Series, with Engineering Applications , 1949 .

[30]  M. Rudemo.  Doubly stochastic Poisson processes and process control , 1972, Advances in Applied Probability.

[31]  Thomas Kailath,et al.  Nonlinear filtering with counting observations , 1975, IEEE Trans. Inf. Theory.

[32]  A. Willsky,et al.  Estimation for rotational processes with one degree of freedom--Part I: Introduction and continuous- , 1975 .

[33]  J. P. McKean.  Brownian Motion with a Several-Dimensional Time , 1963 .

[34]  Michael A. Arbib,et al.  Topics in Mathematical System Theory , 1969 .

[35]  P. Varaiya,et al.  Martingales on Jump Processes. I: Representation Results , 1975 .

[36]  Alan S. Willsky,et al.  Algebraic Structure and Finite Dimensional Nonlinear Estimation , 1978 .

[37]  M. Zakai.  On the optimal filtering of diffusion processes , 1969 .

[38]  R. L. Stratonovich.  CONDITIONAL MARKOV PROCESSES , 1960 .

[39]  M. Rudemo.  State estimation for partially observed Markov chains , 1973 .

[40]  Alʹbert Nikolaevich Shiri︠a︡ev,et al.  Statistics of random processes , 1977 .

[41]  G. Picci,et al.  A State-Space Theory for Stationary Stochastic Processes, , 1978 .

[42]  T. Kailath.  The innovations approach to detection and estimation theory , 1970 .