LAUSR

For the Inertial Navigation System (INS) in hypersonic flight, when the centroid of the Inertial Measurement Unit (IMU) does not coincide with that of hypersonic vehicle, a lever-arm effect error will be generated, which will further expand in the dynamic complex environment. In order to improve the navigation accuracy, the lever-arm effect error needs to be compensated. Although much research has been done on the compensation of lever-arm effect, the existing methods are almost based on the fixed lever-arm length, which can not adapt to the changes of lever-arm in a dynamic and complex environment. In order to obtain the higher accuracy of navigation in the complex dynamic environment for a hypersonic vehicle, a more perfect dynamic lever-arm model is established, and a new compensation method for dynamic lever-arm effect based on reinforcement learning (RL-DLAC) is proposed. The method adopts the framework of reinforcement learning and obtains the optimal parameter estimation of the dynamic lever-arm model from the continuous action space according to the deep deterministic policy gradient (DDPG). The simulation results show that the proposed method can estimate the parameters of the dynamic lever-arm model with an error of only 0.31%, compensate the lever-arm effect error in complex dynamic environment and improve the navigation accuracy.