Abstract
A new nonlinear filter based on Bayesian MAP estimation criterion is introduced. The filter is more stable and robust in use than the extended Kaiman or similar filters and needs less calculation in the on-line algorithm. No on-line covariance matrix calculation is necessary for the gain when the filter is run long after initialization. Application experiences are considered when using for simultaneous state and parameter estimation in adaptive control and sensor/actuator fault detection.
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