Multi-sensor information fusion nonlinear filter with multiple packet dropouts

For multi-sensor nonlinear systems with multiple packet dropouts, a weighted measurement fusion Unscented Kalman Filter is proposed. Measurement packet loss or state packet loss at time k will result in the loss of state estimation at time k, so that the Unscented Kalman Filter cannot be obtained at time k+1. In this paper, the state one-step predictor at the previous moment is used to approximate the state estimate of the current moment, and then the corresponding Unscented Kalman Filter is proposed. This filter is suitable for dealing with the packet dropouts problem at any time. In order to improve the filtering accuracy, a weighted observation fusion method is proposed. The example is given to show the effectiveness of the proposed algorithm.