论文信息 - Scalar weighting optimal fusion predictors for discrete multichannel ARMA signals

Scalar weighting optimal fusion predictors for discrete multichannel ARMA signals

Based on the multi-sensor optimal information fusion criterion weighted by scalars in the linear minimum variance sense, the distributed optimal fusion Kalman multi-step predictor is given for discrete multi-channel ARMA (autoregressive moving average) signals. The precision of the fusion predictor is higher than that of any local predictor. It only requires the computation of scalar weights, the computational burden can be reduced comparing with one weighted by matrices. An example of double-channel signal system with three sensors shows the effectiveness.

Shu-Li Sun

[1] Thiagalingam Kirubarajan,et al. Performance limits of track-to-track fusion versus centralized estimation: theory and application [sensor fusion] , 2003 .

[2] T. Kirubarajan,et al. Performance Limits of Track-to-Track Fusion vs . Centralized Estimation : Theory and Application , 2001 .

[3] Andrzej Cichocki,et al. ADAPTIVE NOISE CANCELLATION FOR MULTI-SENSORY SIGNALS , 2001 .

[4] Sun Shu-li. Multi-sensor optimal information fusion steady-state Kalman filter weighted by scalars , 2004 .

[5] K. H. Kim,et al. Development of track to track fusion algorithms , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[6] Shuli Sun,et al. Multi-sensor optimal information fusion Kalman filter for discrete multichannel ARMA signals , 2003, Proceedings of the 2003 IEEE International Symposium on Intelligent Control.