Abstract
A simplified adaptive scheme is suggested for the estimation of the state vector of linear systems driven by white process noise that is added to an unknown deterministic signal. The design approach is based on embedding the Kalman filter (KF) within a simplified adaptive control loop that is driven by the innovation process. The simplified adaptive loop is idle during steady-state phases that involve white driving noise only. However, when the deterministic signal is added to the driving noise signal, the simplified adaptive control loop enhances the KF gains and helps in reducing the resulting transients. The stability of the overall estimation scheme is established under strictly passive conditions of a related system. The suggested method is applied to the target acceleration estimation problem in a Theater Missile Defence scenario.
| Original language | English |
|---|---|
| Pages (from-to) | 49-55 |
| Number of pages | 7 |
| Journal | Systems and Control Letters |
| Volume | 57 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2008 |
Keywords
- Adaptive estimation
- Almost strict passivity
- Kalman filter
- Neural networks
- Simplified adaptive control