ArnabKar / KalmanNet_TSP

code for KalmanNet

KalmanNet

Link to paper

KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics

Running code

Branch - main

This branch simulates architecture #1 in our paper. There are two main files simulating the linear and non-linear cases respectively.

• Linear case

python3 main_linear.py

• Non-linear Lorenz Attractor(LA) case

python3 main_lorenz.py

• Non-linear pendulum, toy problem or real-world NCLT case

change the path_model parameter in filling_paths.py accordingly and then run main_lorenz.py.

Branch - new_architecture

This branch simulates architecture #2 in our paper. There are two main files simulating the Discrete-Time and Decimation cases respectively.

• Discrete-Time LA case

python3 main_lor_DT.py

• Decimated LA case

python3 main_lor_decimation.py

For pendulum, toy problem or real-world NCLT case, similarly, you could change the path_model parameter in filling_paths.py.

Introduction to other files

Branch - main

• KNet/

This folder is used to store the trained KalmanNet model and pipeline results of your simulation.

• Simulations/

This folder stores the dataset for synthetic linear case, as well as model and parameter files for LA, pendulum, toy problem and real-world NCLT.

• EKF.py & EKF_test.py

This is where we define Extended Kalman Filter (EKF) model and its testing.

• Extended_data.py

This is a parameter-setting and data-generating/loading/preprocessing file.

You could set the number of Training/Cross-Validation(CV)/Testing examples through N_E/N_CV/N_T.

You could set trajectory length of Training/CV examples for linear case through T, while T_test is for Testing trajectory length.

You could set the synthetic linear model through F10 and H10, and uncomment 2x2, 5x5, 10x10 according to your needs.

• KalmanNet_nn.py & Extended_KalmanNet_nn.py

These files describe the architecture #1 of KalmanNet model for linear and non-linear cases respectively.

• Linear_sysmdl.py & Extended_sysmdl.py

These are system model files for linear and non-linear cases respectively. They store system information (F/f, H/h, Q, R, m, n) and define functions for generating data according to your system model.

• filling_paths.py

This is where you switch between different system models.

• Linear_KF.py & KalmanFilter_test.py

This is where we define Linear Kalman Filter (KF) model and its testing.

• Optimal_q_search.py

This is a main file to search for optimal Q or R for fair comparison with KF.

• PF_test.py & UKF_test.py

These files defines the testing of Particle Filter (PF) and Unscented Kalman Filter (UKF) benchmarks.

• Pipeline_KF.py & Pipeline_EKF.py

These are the pipeline files for linear and non-linear cases of KalmanNet respectively. The pipeline mainly defines the Training/CV/Testing processes of KalmanNet.

• Plot.py

This file mainly defines the plotting of training process, histogram, MSE results, etc.

Branch - new_architecture

Since the file arrangement is very simular with the main branch, here we only briefly introduce the different parts.

• KNet/KNet_TSP

This folder stores the simulation results in our paper.

• KalmanNet_build.py & KalmanNet_train.py & KalmanNet_test.py

These files defines the building, training and testing of KalmanNet architecture #2.