| Paper: | SPTM-L2.5 | ||
| Session: | Particle Filtering and Other Tracking Algorithms | ||
| Time: | Tuesday, May 16, 15:20 - 15:40 | ||
| Presentation: | Lecture | ||
| Topic: | Signal Processing Theory and Methods: Adaptive Systems and Filtering | ||
| Title: | Adaptive-Gain Tracking Filters Based on Minimization of the Innovation Variance | ||
| Authors: | Naum Chernoguz, TAMAM, Israel Aircraft Industries, Israel | ||
| Abstract: | A kinematic tracking filter is considered in the context of gain adaptation problem. The study suggests a simple adaptive-gain tracker based on minimization of the innovation variance. This is shown to provide the optimal Kalman gain. Accordingly, the innovation-based adaptive Kalman-like filter is constructed. The adaptive scheme is associated with a recursive MA-parameter estimator. With proper links for the optimal gain-vector components, the multiple-parameter adaptive filter reduces to a constrained single-parameter version. The simulation study justifies the filter performance for a wide range of conditions. | ||
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