Information Fusion Identification Method for the Multidimension ARMA Signal with Sensor Bias and Common Disturbance Noise

Abstract

For the multisensor multi-dimension autoregressive moving average(ARMA) signal system with a common disturbance measurement noise and sensor bias, when the model parameters, sensor bias and noise variances are all unknown, their consistent estimates are obtained by the multistage information fusion identification method. Firstly, by multi-dimension recursive extended least squares (RELS) algorithm, the estimates of the autoregressive parameters and sensor bias are obtained. Secondly, applying the correlation method, the estimates of the measurement noise variances are obtained. Finally, the fused estimates of the moving average(MA) parameters and the process noise variances are obtained by the Gevers-Wouters algorithm with a dead band. A simulation example verifies the consistency of unknown parameters estimates.