Kalman Filtering: Theory and Application

algorithm applications approach assumed Automat bias calculated components computed continuous-time Contr coordinates correlation covariance matrix defined denotes derived described deviation differential equations discrete discrete-time divergence dynamic systems eigenvalue error covariance estimation error estimation problem estimation theory example extended Kalman filter filter equations filtered estimate formulation frequency function Gaussian given IEEE IEEE Trans implementation ingot input least-squares linear dynamic systems linear filtering measurement noise method motion navigation nonlinear observations obtained optimal estimate optimal filter optimum orbit orthogonal paper parameters performance position error prediction priori procedure process noise pseudorange r₁ radar random variables recursive residuals sampling satellite scalar Section sensor sequence sequential simulation smoothing problem solution spacecraft square root statistical steady-state stochastic Stochastic Processes superheater target technique temperature terrain Theorem tion tracking transition matrix uncertainty update values variance equation vector velocity correction white noise Wiener Wiener filtering zero