Multi-sensor optimal information fusion steady-state Kalman filterweighted by scalars for systems with colored measurement noises
带有色观测噪声系统多传感器标量加权最优信息融合稳态Kalman滤波器

Based on the multi_sensor optimal information fusion criterion weighted by scalars in the linear minimum variance,a scalar weighting information fusion steady_state Kalman filter with a two_layer fusion structure is given for discrete linear stochastic control systems measured by multiple sensors with colored measurement noises,which is equivalent to an optimal information fusion steady_state Kalman predictor for the corresponding systems with correlated noises.The optimal information fusion steady_state predictor can be obtained only by fusing once after all local predictors reach the steady state.The solutions of steady_state prediction error cross_covariance matrices between any two subsystems can be obtained by iteration with arbitrary initial values,whose convergence is proved.Its effectiveness is shown by applying it to a radar tracking system with three sensors.