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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 single channel autoregressive moving average (ARMA) signals with multisensor and white measurement noises, unknown model parameters and unknown noise variances, a multi-stage information identification method is presented. It consists of the recursive instrument variable (RIV) algorithm-based information fusion estimator of the autoregressive (AR) parameters, the correlation method-based information...
For the multisensor system with identical measurement matrix and correlated measurement noise, by the correlation method, the online estimators of the noise statistics are obtained. Based on modern time series analysis method, a self-tuning weighted measurement fusion Kalman filter is presented, which avoids Lyapunov and Riccati equations, reduces the computational burden and is suitable for real...
For the multisensor system with identical measurement matrix and correlated measurement noises, by correlated method, the online estimators of the noise statistics are obtained. Based on modern time series analysis method, a self-tuning weighted measurement fusion Wiener filter is presented, which avoids Lyapunov and Riccati equations, reduces the computational burden and is suitable for real time...
The convergence and local research ability of genetic algorithm is a well concerned research field in recent years. New evolution law of species is introduced in the paper, and based on the new evolution law, compulsive operator was introduced and a new genetic algorithm - compulsive genetic algorithm (CGA) was proposed to improve the convergence of GA. CGA takes advantage of the fitness of current...
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...
For the multisensor system with unknown noise statistics and with identical measurement matrices, based on the solution of the matrix equations for correlation function, the online estimators of the noise variance matrices are obtained, further, a self-tuning weighted measurement fusion Kalman smoother is presented. Based on the stability of the dynamic error system, a new convergence analysis tool...
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