kalmanformer 0.0.2

KalmanFormer - using transformer to model the Kalman Gain in Kalman Filters

KalmanFormer

Implementation of KalmanFormer.

The paper proposes learning the Kalman Gain directly from data using Transformers, bypassing the limitations of traditional Kalman Filters on non-linear systems.

Install

$ pip install kalmanformer

Usage

import torch
from kalmanformer import KalmanFormer

# kalmanformer

kalmanformer = KalmanFormer(
    state_dim = 3,
    obs_dim = 3,
    dim = 64,
    depth = 2,
    heads = 2,
    dim_head = 32,
    mlp_dim = 64
)

# mock observations

observations = torch.randn(2, 10, 3)

# state transition matrix f and observation matrix h

F = torch.randn(3, 3)
H = torch.randn(3, 3)

# initial state

x_0 = torch.zeros(2, 3)

# tracking over sequence

post_states = kalmanformer(
    observations,
    F,
    H,
    x_0 = x_0
)

assert post_states.shape == (2, 10, 3)

Citations

@article{Shen2025KalmanFormer,
    title   = {KalmanFormer: using transformer to model the Kalman Gain in Kalman Filters},
    author  = {Siyuan Shen and Jichen Chen and Guanfeng Yu and Zhengjun Zhai and Pujie Han},
    journal = {Frontiers in Neurorobotics},
    year    = {2025},
    volume  = {18},
    doi     = {10.3389/fnbot.2024.1460255}
}