Papers › Incorporating Transformer and LSTM to Kalman Filter with EM algorithm for state estimation

Incorporating Transformer and LSTM to Kalman Filter with EM algorithm for state estimation

1 May 2021arXiv:2105.00250archive 2025-07-28

Zhuangwei Shi

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.

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zshicode/Deep-Learning-Based-State-Estimation officialmentioned in papermentioned on GitHubpytorch report

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Decoder State Estimation

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Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Memory Network Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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