Self-tuning Distributed Measurement Fusion Kalman Filter
自校正分布式观测融合Kalman滤波器

For the multisensor system with unknown noise statistics, and with the measurement matrices having a same right factor, based on Weighted Least Squares(WLS) method, an equivalent fusion measurement equation is obtained. Using the modern time series analysis method, based on on-line identification of the innovation model parameters, unknown noise variances can be estimated, and a self-tuning weighted measurement fusion Kalman filter is presented. Under the assumptions that the parameter estimation of the innovation model is consistent and the measurement data are bounded, it is proved that the self-tuning Kalman filter converges to globally optimal fusion Kalman filter with known noise statistics, so that it has asymptotic global optimality. A simulation example for a tracking system with 4-sensor shows its effectiveness.