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For the multisensor autoregressive moving average (ARMA) signals with unknown model parameters and noise variances, using recursive instrumental variable (RIV) algorithm, the correlation method and the Gevers-Wouters algorithm with dead band, the information fusion estimators of model parameters and noise variances are presented. They have strong consistence. Then substituting them into the optimal...
For the multisensor systems with correlated measurement noises, different measurement matrices and unknown noise variances, based on the autoregressive moving average (ARMA) model and the reduced dimension measurement fusion algorithm, using the correlated method, a self-tuning reduced dimension measurement fusion Kalman filter is obtained, and its convergence in a realization is proved by the dynamic...
For the multisensor autoregressive moving average (ARMA) signal systems with measurement noises, when the ARMA model parameters and noise variances are unknown, using recursive instrumental variable(RIV) algorithm, the correlation method and the Gevers-Wouters algorithm with dead band, the local and fused model parameter estimators and the information fusion noise variance estimators are presented...
For the multisensor system with different measurement matrices, correlated measurement noises and unknown noise variances, by correlated method, the online identifiers of the noise variances are obtained. Based on ARMA innovation model, a self-tuning weighted measurement fusion Kalman filter is presented, which avoids Lyapunov and Riccati equations, reduces the computational burden and is suitable...
White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. For the multisensor linear discrete time-invariant stochastic systems with correlated measurement noises and unknown noise statistics, an on-line noise statistics estimator is presented by using the correlation method...
For the multisensor multi-channel autoregressive moving average (ARMA) signals with unknown parameters and noise variances, using the modern time series analysis method, based on the on-line identification of the local ARMA innovation models and fused moving average (MA) innovation model, a class of self-tuning weighted measurement fusion filter and smoother are presented. By using the dynamic error...
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