Parameter Adaptive Non-Model-Based State Estimation Combining Attention Mechanism and LSTM

Nowadays, state estimation is widely used in fields such as autonomous driving and drone navigation. However, in practical applications, it is difficult to obtain accurate target motion models and noise covariance.This leads to a decrease in the estimation accuracy of traditional Kalman filters. To address this issue, this paper proposes an adaptive model free state estimation method based on attention parameter learning module. This method combines Transformer's encoder with Long Short Term Memory Network (LSTM), and obtains the system's operational characteristics through offline learning of measurement data without modeling the system dynamics and measurement characteristics. In addition, based on the output of the attention learning module, the expectation maximization (EM) algorithm is used to estimate the system model parameters online, and a Kalman filter is used to obtain state estimation. This paper was validated using the GPS trajectory path dataset, and the experimental results showed that the proposed parameter adaptive model free state estimation method has better estimation accuracy than other models, providing an effective method for using deep learning networks for state estimation.