The invention discloses a cooperative state
estimation method and
system based on Kalman filtering and
deep learning, and relates to the technical field of cluster control, and the method comprises the steps: obtaining a state value of an
intelligent agent at a (k-1) th sampling moment and a control value of the
intelligent agent from the (k-1) th sampling moment to a kth sampling moment; according to the obtained state value and control value, a predictive state value and predictive uncertainty of the
intelligent agent at the kth sampling moment are obtained by using a deterministic
kinematics model and a
process noise neural network; obtaining
observation data of the intelligent agent on a neighbor intelligent agent, calculating a structured residual error based on the prediction state value, the
observation data and the prediction state value of the neighbor intelligent agent, and inputting the prediction state value, the prediction uncertainty, the
observation data and the structured residual error of the intelligent agent at the kth sampling moment into a posterior neural network to obtain a predicted state value of the intelligent agent at the kth sampling moment; and obtaining a posterior state value and posterior uncertainty of the intelligent agent at the kth sampling moment. According to the method, the state
estimation precision and the robustness to environmental interference can be remarkably improved.