Abstract: To overcome the problem of difficulty in accurate tracking of maneuvering targets, a recurrent Kalman neural network tracking algorithm that can be learned online is proposed. Considering that the state transition matrix, measurement noise and process noise matrix are difficult to estimate in real time and offline in maneuvering target tracking, and the cost for acquisition of corresponding data set is high in practical applications, the neural network of online learning is used to estimate them in real time. Since the Kalman filter algorithm itself is a cyclic structure, a simple fully connected layer network is integrated with it. The fully connected layer network outputs the state transition matrix, and measurement and process noise matrix estimation in real time, forming a generalized cyclic Kalman neural network. The end-to-end online learning is carried out according to the position estimation finally output by the network, and the feasibility of the online learning is proved through theoretical deduction. The simulation results show that the proposed recurrent Kalman neural network needs very little prior information, and has the highest tracking accuracy and robustness in the optimal region in comparison with three classical algorithms, demonstrating the characteristics of high efficiency, low training cost and strong scalability.

Key words: maneuvering target tracking, recurrent neural network, Kalman filter, online learning, end-to-end