Sensitivity of the Kalman Filter with Respect to Parameter Variations
STANFORD RESEARCH INST MENLO PARK CA
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Techniques are given for sensitivity analysis of the Kalman filter with respect to simultaneous variations in measurement noise, plant noise, dynamic model, sampling period, and filter gain. These analytical techniques will greatly aid the design and evaluation of Kalman filters and other types of filters. Two basic assumptions were used There are nominal quantities about which variations may be taken, and The estimation-error covariances are the filter performance measures.
- Statistics and Probability