  Kalman Filter requires the true parameters of the model and solves optimal
state estimation recursively. Expectation Maximization (EM) algorithm is
applicable for estimating the parameters of the model that are not available
before Kalman filtering, which is EM-KF algorithm. To improve the preciseness
of EM-KF algorithm, the author presents a state estimation method by combining
the Long-Short Term Memory network (LSTM), Transformer and EM-KF algorithm in
the framework of Encoder-Decoder in Sequence to Sequence (seq2seq). Simulation
on a linear mobile robot model demonstrates that the new method is more
accurate. Source code of this paper is available at
https://github.com/zshicode/Deep-Learning-Based-State-Estimation.
