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The damped modified iterated Kalman filter for nonlinear discrete time systems. (English) Zbl 0915.93061
If we compare Kalman filters of different types, we see that the extended Kalman filter (EKF) gives efficient estimation properties but converges slowly or even diverges, and the iterated Kalman filter (IKF) converges quickly but has a high computational complexity. The authors propose the damped modified iterated Kalman filter (MIKF) to make a compromise between the EKF and the IKF. The MIKF is derived from the modified Newton method and also it is obtained by an iteration scheme for the EKF equations. A convergence analysis of the MIKF is done and it shows efficient convergence behaviour. Nevertheless it requires many calculations for treating system nonlinearities. To overcome these disadvantages, the authors apply the damping method to the MIKF. A numerical example is presented and it shows the effective convergence behaviour of the damped MIKF.
MSC:
93E11 Filtering in stochastic control theory
93C55 Discrete-time control/observation systems
60G35 Signal detection and filtering (aspects of stochastic processes)
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