## Kalman Filtering: Theory and PracticeA thorough exploration of the theory and application of Kalman filtering to real-world situations. *book contains a floppy disk with C++ and MATLAB algorithms. *offers a heuristic treatment of essential material. *includes many often ignored design and implementation techniques. *explores the appropriate numerical methods for reliable implementation. *contains a variety of examples and problems taken from real-world application situations - e.g., modelling of gyros, accelerometers, inertial navigation; freeway traffic model; a harmonic oscillator; radar tracking; Global Positioning System (GPS) aided Inertial Navigation System (INS). *includes companion software to solve large dimension problems in the text. |

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algebraic algorithm approximation called Chapter Cholesky decomposition Cholesky factor coefficient column components computational complexity condition number continuous-time convergence covariance matrix defined density derived diagonal dimension discrete discrete-time elements estimation error estimation problem estimation uncertainty Example extended Kalman filter filter implementation filter model flops formula Gaussian given Gramian matrix input inverse Kalman gain least squares linear dynamic system loop lower triangular matrix Riccati measurement noise methods nonlinear nonnegative nonsingular notation numerical stability observational update optimal orthogonal orthogonal matrix output Pade approximation parameters performance probability distribution process noise propagation quadratic random process random variable represented Riccati differential equation Riccati equation roundoff errors scalar sensor sequence sigma algebra solution solved square root statistical steady-state stochastic suboptimal filter symbol symmetric system model Table temporal update time-invariant tion trajectory transform transition matrix triangular matrix variance vector white noise Wiener filter zero