Locally and Spatially Uniform Linear Operator Construction with the Kalman Filter

• This repository w.r.t. locally and spatially uniform linear operator construction with the Kalman filter (LSLOCK), which enable us to estimate states and state transition of linear Gaussian state space model in real-time.
• This repository includes the method in src directory, examples of application in examples directory.
• For ConvLSTM, we started this implementation and changed structure in order to compare w/ the prposed method.

Examples

• We provide two examples of this method at example directory.
  1. global flow data: objects flow each direction in each interval
     • the generation process is condeucted by python data_global_flow.py
     • application of the proposed method is conducted by python global_flow.py
     • application of ConvLSTM is conducted by python run_convlstm.py
  2. circular wave data: a wave propagates concentrically from a source point
     • the generation process is conducted by python data_circular_wave.py
     • application of our method is conducted by python circular_wave.py
     • application of ConvLSTM is conducted by python run_convlstm.py

Packages

• The proposed method uses Python3.7
• ConvLSTM uses Python3.7 and pytorch 1.2.0
  • If you do not conduct run_convlstm.py, not need to install pytorch