Trajectory Tracking of Reentry Vehicle Based on KalmanNet with Time-Varying Observation Matrix

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This paper proposes a trajectory-tracking algorithm for reentry vehicles based on KalmanNet with a time-varying observation matrix. First, a nonlinear state evolution model of the reentry vehicle and a radar measurement model are developed in the radar measurement coordinate system. Then, inspired by the computation process of the Kalman gain (KG) in the extended Kalman filter (EKF), the recurrent neural network (RNN) architecture of KalmanNet is improved. The gated recurrent unit (GRU) originally used to track process noise statistics is removed. Instead, the input features are redesigned to directly estimate the prior state covariance. Furthermore, another GRU is introduced to estimate the time-varying observation matrix, considering the nonlinear characteristics of radar measurements. The calculated observation matrix is fed into both the GRU responsible for estimating the covariance of the difference between the predicted observation and the observed value and the fully connected layer that computes the KG. Finally, the proposed method is compared with six representative algorithms, including EKF, particle filter (PF), unscented Kalman filter (UKF), convolutional neural network (CNN), Long Short-Term Memory (LSTM), and the original KalmanNet. Simulation results demonstrate that the proposed method achieves the highest estimation accuracy, while its computational time remains nearly the same as that of the original KalmanNet. Monte Carlo simulations under three model-mismatch conditions are conducted to validate the robustness of the proposed method.