A Reduced Dimension Static, Linearized Kalman Filter and Smoother

An approximate Kalman filter and smoother, based on approximations of the state estimation error covariance matrix, will be described. Approximations include. a Auction of the effective state dimension, use of a static asymptotic error limit, and a time-invariant linearization of the dynamic model for error integration. The approximations lead to dramatic computational savings in applying estimation theory to large, complex systems. Examples of oceanographic applications will be pmsentcd analyzing altimeter data from TOPEX/POSEIDON, an ongoing joint U.S.-French oceanographic satellite mission.