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Kushner equation : ウィキペディア英語版
Kushner equation
In filtering theory the Kushner equation〔Kushner H.J. (1964) ''On the differential equations satisfied by conditional probability
densities of Markov processes, with applications.''. J. SIAM Control Ser. A, 2(1), pp. 106-119.〕 (after Harold Kushner) is an equation for the conditional probability density of the state of a stochastic non-linear dynamical system, given noisy measurements of the state. It therefore provides the solution of the nonlinear filtering problem in estimation theory. The equation is sometimes referred to as the Stratonovich–Kushner〔Stratonovich, R.L. (1959). ''Optimum nonlinear systems which bring about a separation of a signal with constant parameters from noise''. Radiofizika, 2:6, pp. 892–901.〕〔Stratonovich, R.L. (1959). ''On the theory of optimal non-linear filtering of random functions''. Theory of Probability and its Applications, 4, pp. 223–225.〕〔Stratonovich, R.L. (1960) ''Application of the Markov processes theory to optimal filtering''. Radio Engineering and Electronic Physics, 5:11, pp. 1–19.〕〔Stratonovich, R.L. (1960). ''Conditional Markov Processes''. Theory of Probability and its Applications, 5, pp. 156–178.〕 (or Kushner–Stratonovich) equation. However, the correct equation in terms of Itō calculus was first derived by Kushner although a more heuristic Stratonovich version of it appeared already in Stratonovich's works in late fifties. However, the derivation in terms of Itō calculus is due to Richard Bucy.〔Bucy, R. S. (1965) (Nonlinear filtering theory ). IEEE Transactions on Automatic Control, 10, pp. 198–198.〕
== Overview ==

Assume the state of the system evolves according to
:dx = f(x,t) \, dt + \sigma dw
and a noisy measurement of the system state is available:
:dz = h(x,t) \, dt + \eta dv
where ''w'', ''v'' are independent Wiener processes. Then the conditional probability density ''p''(''x'', ''t'') of the state at time ''t'' is given by the Kushner equation:
:dp(x,t) = L() dt + p(x,t) (h(x,t) )^T \eta^\eta^ (h(x,t) dt ).
where L p = -\sum \frac + \frac \sum (\sigma \sigma^\top)_ \frac is the Kolmogorov Forward operator and dp(x,t) = p(x,t + dt) - p(x,t) is the variation of the conditional probability.
The term dz-E_t h(x,t) dt is the innovation i.e. the difference between the measurement and its expected value.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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