Kalman and Bayesian Filters in Python

Livro digital

Título:
Kalman and Bayesian Filters in Python

Autor:
Roger R. Labbe Jr.

Categoria:
Tecnologia > IA

Tags:
PythonProcessamento de Sinais

Pessoa doadora:
Raffaello D. N.

Sinopse:
State estimation is one of the most underexplored tools in a practitioner's arsenal — not because it is obscure, but because most treatments bury it under dense mathematics before the reader has a chance to build intuition. Kalman and Bayesian Filters in Python takes the opposite approach: it opens with the g-h filter, a conceptually transparent algorithm that reveals the underlying logic of every filter in the book. From there, the table of contents traces a deliberate arc — Discrete Bayes Filter, Gaussian math, the Kalman Filter proper, and then progressively richer variants including the Extended Kalman Filter, Unscented Kalman Filter, and particle filters — each chapter adding one layer of realism without abandoning clarity. The book covers the full practical toolkit: SciPy, NumPy, and Matplotlib are used throughout, and every chapter includes executable Jupyter notebooks so the reader can observe filter behavior in live code rather than static equations. Topics range from tracking a dog across a hallway to fusing GPS and inertial data in robotics — grounding abstract probability theory in concrete, reproducible experiments. The author, Roger R. Labbe Jr., structures each chapter around a problem, builds a solution incrementally, and only then introduces the formal notation, inverting the usual order of exposition. This is the book to reach for when theoretical treatments have failed to stick. It does not assume a signal-processing background, and it does not shortcut the mathematics — it earns both sides of that balance by teaching through simulation. Readers who finish it will have not just an understanding of Kalman and Bayesian filtering, but the habit of thinking probabilistically about systems that change over time.