Giter VIP home page Giter VIP logo

minikalman-rs's Introduction

Kalman Filters for Embedded Targets (in Rust)

This is the Rust port of my kalman-clib library, a microcontroller targeted Kalman filter implementation. Uses micromath for square root calculations on no_std. At the moment, this crate requires f32 / FPU support.

This implementation uses statically allocated buffers for all matrix operations. Due to lack of const generics for array allocations in Rust, this crate also provides helper macros to create the required arrays. See examples/gravity.rs for a worked example.

no_std vs std

This crate builds as no_std by default. To build with std support, run:

cargo build --no-default-features --features std

Example

The provided example code will print output only on std builds. To run the example gravity simulation, run

cargo run --example gravity --no-default-features --features=std

This will estimate the (earth's) gravitational constant (g ≈ 9.807 m/s²) through observation of the position of a free-falling object. When executed, it should print something along the lines of:

At t = 0, predicted state: s = 3 m, v = 6 m/s, a = 6 m/s²
At t = 0, measurement: s = 0 m, noise ε = 0.13442 m
At t = 0, corrected state: s = 0.908901 m, v = 3.6765568 m/s, a = 5.225519 m/s²
At t = 1, predicted state: s = 7.1982174 m, v = 8.902076 m/s, a = 5.225519 m/s²
At t = 1, measurement: s = 4.905 m, noise ε = 0.45847 m
At t = 1, corrected state: s = 5.6328573 m, v = 7.47505 m/s, a = 4.5993752 m/s²
At t = 2, predicted state: s = 15.407595 m, v = 12.074425 m/s, a = 4.5993752 m/s²
At t = 2, measurement: s = 19.62 m, noise ε = -0.56471 m
At t = 2, corrected state: s = 18.50683 m, v = 14.712257 m/s, a = 5.652767 m/s²
At t = 3, predicted state: s = 36.04547 m, v = 20.365025 m/s, a = 5.652767 m/s²
At t = 3, measurement: s = 44.145 m, noise ε = 0.21554 m
At t = 3, corrected state: s = 42.8691 m, v = 25.476515 m/s, a = 7.3506646 m/s²
At t = 4, predicted state: s = 72.02094 m, v = 32.82718 m/s, a = 7.3506646 m/s²
At t = 4, measurement: s = 78.48 m, noise ε = 0.079691 m
At t = 4, corrected state: s = 77.09399 m, v = 36.10087 m/s, a = 8.258889 m/s²
At t = 5, predicted state: s = 117.3243 m, v = 44.359756 m/s, a = 8.258889 m/s²
At t = 5, measurement: s = 122.63 m, noise ε = -0.32692 m
At t = 5, corrected state: s = 120.94025 m, v = 46.38022 m/s, a = 8.736543 m/s²
At t = 6, predicted state: s = 171.68874 m, v = 55.11676 m/s, a = 8.736543 m/s²
At t = 6, measurement: s = 176.58 m, noise ε = -0.1084 m
At t = 6, corrected state: s = 174.93135 m, v = 56.704926 m/s, a = 9.062785 m/s²
At t = 7, predicted state: s = 236.16766 m, v = 65.76771 m/s, a = 9.062785 m/s²
At t = 7, measurement: s = 240.35 m, noise ε = 0.085656 m
At t = 7, corrected state: s = 238.87048 m, v = 66.942894 m/s, a = 9.276019 m/s²
At t = 8, predicted state: s = 310.4514 m, v = 76.21891 m/s, a = 9.276019 m/s²
At t = 8, measurement: s = 313.92 m, noise ε = 0.8946 m
At t = 8, corrected state: s = 313.03793 m, v = 77.22877 m/s, a = 9.44006 m/s²
At t = 9, predicted state: s = 394.98672 m, v = 86.66882 m/s, a = 9.44006 m/s²
At t = 9, measurement: s = 397.31 m, noise ε = 0.69236 m
At t = 9, corrected state: s = 396.6648 m, v = 87.26297 m/s, a = 9.527418 m/s²
At t = 10, predicted state: s = 488.69147 m, v = 96.79039 m/s, a = 9.527418 m/s²
At t = 10, measurement: s = 490.5 m, noise ε = -0.33747 m
At t = 10, corrected state: s = 489.46213 m, v = 97.03994 m/s, a = 9.560934 m/s²
At t = 11, predicted state: s = 591.28253 m, v = 106.600876 m/s, a = 9.560934 m/s²
At t = 11, measurement: s = 593.51 m, noise ε = 0.75873 m
At t = 11, corrected state: s = 592.75964 m, v = 107.04147 m/s, a = 9.615404 m/s²
At t = 12, predicted state: s = 704.6088 m, v = 116.656876 m/s, a = 9.615404 m/s²
At t = 12, measurement: s = 706.32 m, noise ε = 0.18135 m
At t = 12, corrected state: s = 705.4952 m, v = 116.90193 m/s, a = 9.643473 m/s²
At t = 13, predicted state: s = 827.2188 m, v = 126.5454 m/s, a = 9.643473 m/s²
At t = 13, measurement: s = 828.94 m, noise ε = -0.015764 m
At t = 13, corrected state: s = 827.97705 m, v = 126.74077 m/s, a = 9.66432 m/s²
At t = 14, predicted state: s = 959.55 m, v = 136.40509 m/s, a = 9.66432 m/s²
At t = 14, measurement: s = 961.38 m, noise ε = 0.17869 m
At t = 14, corrected state: s = 960.39984 m, v = 136.6101 m/s, a = 9.684802 m/s²

minikalman-rs's People

Contributors

sunsided avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.