Aug 20

Misc Motion Learning and Prediction, Target Tracking, and related

An Introduction to Graph Neural Networks: Models and Applications

https://www.youtube.com/watch?v=zCEYiCxrL_0

Encoder-Decoder Recurrent Neural Network Models for Neural Machine Translation

https://machinelearningmastery.com/encoder-decoder-recurrent-neural-network-models-neural-machine-translation/

Illustrated Guide to LSTM’s and GRU’s: A step by step explanation

https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21

RNN/LSTM/GRU

https://www.cs.toronto.edu/~tingwuwang/rnn_tutorial.pdf

Maximum Likelihood Joint Tracking and Association in a Strong Clutter

https://arxiv.org/pdf/1010.4236.pdf

Multitarget tracking using maximum likelihood techniques

https://opencommons.uconn.edu/dissertations/AAI3265759/

Maximum Likelihood Joint Tracking and Association in Strong Clutter

https://journals.sagepub.com/doi/full/10.5772/52859

Multiple target tracking using maximum likelihood principle

https://ieeexplore.ieee.org/document/398728

Multiple Target Tracking Using Maximum Likelihood Probabilistic Data Association

https://ieeexplore.ieee.org/document/4161445

Maximum Likelihood Joint Tracking and Association in Strong Clutter

https://www.researchgate.net/publication/288139436_Maximum_Likelihood_Joint_Tracking_and_Association_in_Strong_Clutter

Maximum likelihood method for probabilistic multihypothesis tracking

https://www.researchgate.net/publication/252858795_Maximum_likelihood_method_for_probabilistic_multihypothesis_tracking

Maximum likelihood method for probabilistic multihypothesis tracking

https://www.spiedigitallibrary.org/conference-proceedings-of-spie/2235/0000/Maximum-likelihood-method-for-probabilistic-multihypothesis-tracking/10.1117/12.179066.short?SSO=1

A Tutorial on Bayesian Estimation and Tracking Techniques Applicable to Nonlinear and Non-Gaussian Processes

https://www.mitre.org/sites/default/files/pdf/05_0211.pdf

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