|
|
 |
| |
|
|
|
Self-tuning Information Fusion Kalman Filter for Multisensor Multi-channel ARMA Signals with Colored Measurement Noises and its Convergence |
|
|
|
PP: 607-618 |
|
|
|
Author(s) |
|
|
|
Guili Tao,
Zili Deng,
|
|
|
|
Abstract |
|
|
| For the multisensor multi-channel autoregressive moving average (ARMA) signals with white measurement noises and an
ARMA colored measurement noise as a common disturbance noise, a multi-stage information fusion identification method is presented
when model parameters and noise variances are partially unknown. The local estimators of model parameters and noise variances
are obtained by the multi-dimensional recursive instrumental variable (MRIV) algorithm, correlation method, and the Gevers-Wouters
algorithm, and the fused estimators are obtained by taking the average of the local estimators. They have the consistency. Substituting
them into the optimal fusion Kalman filter weighted by scalars, a self-tuning fusion Kalman filter for multi-channel ARMA signals
is presented. It requires a less computational burden, and is suitable for real time applications. Applying the dynamic error system
analysis (DESA) method, it is proved that the proposed self-tuning fusion Kalman filter converges to the optimal fusion Kalman filter
in a realization, so that it has asymptotic optimality. A simulation example shows its effectiveness.
|
|
|
|
|
|
|
|
