Self-Tuning Fusion Kalman Filter for ARMA Signals
For the single-channel autoregressive moving average (ARMA) signals with multisensor, and with unknown model parameters and noise variances, the local estimators of unknown model parameters and noise variances are obtained by the recursive instrumental variable (RIV) algorithm and correlation method...
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| Veröffentlicht in: | Applied Mechanics and Materials 2012-11, Vol.229-231, p.1768-1771 |
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| container_end_page | 1771 |
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| container_title | Applied Mechanics and Materials |
| container_volume | 229-231 |
| creator | Yan, Man Liu, Wen Qiang Han, Na Tao, Gui Li |
| description | For the single-channel autoregressive moving average (ARMA) signals with multisensor, and with unknown model parameters and noise variances, the local estimators of unknown model parameters and noise variances are obtained by the recursive instrumental variable (RIV) algorithm and correlation method, and the fused estimators are obtained by taking the average of the local estimators. Substituting them into the optimal fusion Kalman filter, a self-tuning fusion Kalman filter for single-channel ARMA signals is presented. A simulation example shows its effectiveness. |
| doi_str_mv | 10.4028/www.scientific.net/AMM.229-231.1768 |
| format | Article |
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| title | Self-Tuning Fusion Kalman Filter for ARMA Signals |
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