kalmanformer

KalmanFormer is a Python library that combines two ideas from engineering and machine learning: the Kalman filter and the transformer neural network architecture. The Kalman filter is a mathematical method used in tracking and state-estimation tasks, such as following the position of a moving vehicle, smoothing noisy sensor readings, or predicting where something will be next. It has been used in GPS, robotics, and aerospace for decades because it is principled and computationally efficient.

The classical Kalman filter works best when the system being tracked behaves in a linear, predictable way. In real-world applications, many systems are non-linear, which means the standard filter either produces poor estimates or requires manual tuning of its internal parameters. One of those internal parameters is called the Kalman Gain, a value that determines how much the filter trusts incoming sensor observations versus its own predictions. Getting this value right is important and traditionally requires mathematical derivation for each specific system.

This library implements a research approach where a transformer, the architecture behind many modern AI language models, is used to learn the appropriate Kalman Gain directly from data instead of deriving it by hand. The idea is that the transformer can pick up on patterns in a sequence of observations and figure out the right balance between trusting the model and trusting the sensor, even in non-linear situations.

Installation is a single pip command. The code example in the README shows setting up the model with a few size parameters, providing a sequence of observations plus two matrices that describe how the system transitions and how observations relate to the internal state, and receiving a sequence of estimated states as output.

The library is by lucidrains, a prolific open-source author known for clean, installable implementations of recent AI research papers.