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ADAPTIVE KALMAN FILTERING
| Content Provider | CiteSeerX |
|---|---|
| Author | Boye, Adviser A. John Remus, Marlys Rae |
| Abstract | The Kalman filter provides an effective means of estimating the state of a system from noisy measurements given that the system parameters are completely specified. The innovations sequence for a properly specified Kalman filter will be a zero-mean white noise process. However, when the system parameters change with time the Kalman filter will need to be adapted to compensate for the changes. Traditionally this has been accomplished by using nonlinear filtering, parallel Kalman filtering and covariance matching techniques. These methods have produced good results at the expense of large amounts of computational time. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Computational Time Covariance Matching Technique Nonlinear Filtering Zero-mean White Noise Process Parallel Kalman Filtering Kalman Filter Innovation Sequence Noisy Measurement Adaptive Kalman Filtering System Parameter Effective Mean |
| Content Type | Text |
