The field of identification and process-parameter estimation has developed rapidly during
the past decade. In this survey paper the state-of-the-art/science is presented in a systematic …

Stochastic differential equations are differential equations whose solutions are stochastic
processes. They exhibit appealing mathematical properties that are useful in modeling …

This unified treatment of linear and nonlinear filtering theory presents material previously
available only in journals, and in terms accessible to engineering students. Its sole …

At the end of 1960s and the beginning of 1970s, when the Russian version of this book was
written, the'general theory of random processes' did not operate widely with such notions as …

The definitive textbook and professional reference on Kalman Filtering–fully updated,
revised, and expanded This book contains the latest developments in the implementation …

This book aims to take a reader, with a basis in classical real analysis, through the theory of
stochastic processes, the stochastic calculus, applications in control and filtering. The aim is …

Many aspects of phenomena critical to our lives can not be measured directly. Fortunately
models of these phenomena, together with more limited observations frequently allow us to …

At the end of 1960s and the beginning of 1970s, when the Russian version of this book was
written, the'general theory of random processes' did not operate widely with such notions as …

Let x (t) be a diffusion process satisfying a stochastic differential equation and let the
observed process y (t) be related to x (t) by dy (t)= g (x (t))+ dw (t) where w (t) is a Brownian …

This paper considers the application of the unscented Kalman filter (UKF) to continuous-time
filtering problems, where both the state and measurement processes are modeled as …