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In order to obtain more accurate state estimation from multisensor system, the state estimation with two-level fusion structure is presented. By means of measurement fusion algorithm, the local first-level fusion centers can obtain the globally optimal fused measurement information, and then the local state estimation can be got by classical Kalman(More)
This paper is concerned with the fusion estimation problem for multi-sensor discrete time-invariant linear systems with multiple time delays and colored measurement noise. A fast sequential covariance intersection (SCI) fusion Kalman filter is given based on the augmented Kalman filter in the linear minimum variance sense, which avoids the calculation of(More)
For the multisensor single channel autoregressive moving average(ARMA) signal with common disturbance measurement noise and sensor bias, when the model parameters, the sensor bias and the noise variances are all unknown, their consistent estimates are obtained by a multistage fused identification method, which includes the recursive extended least squares(More)