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Kalman filtering and smoothing in Rust
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Kalman filtering and smoothing library written in Rust

Build Status License: GPL v3

Access documentation for the library here. Library is also referenced in Cargo index.

Currently, library provides only time-invariant linear Kalman filtering and smoothing technique is known as fixed-interval smoothing (Rauch-Tung-Striebel smoother) which relies on Kalman filter estimates for the entire dataset. linearkalman relies on rulinalg library to implement linear algebra structures and operations and so input data is expected to be a std::vec::Vec of Vector objects, i.e. a vector of vectors.

In order to use this library, make sure your Cargo.toml file contains the following:

linearkalman = "0.1.3"

Library can then be imported using:

extern crate linearkalman;


Below example assumes 3-dimensional measurement data with an underlying 2-dimensional state space model. With the help of a few macros from rulinalg, a simple attempt to use the library to run Kalman filter and smoother would be as follows.

extern crate rulinalg;
extern crate linearkalman;

use rulinalg::vector::Vector;
use linearkalman::KalmanFilter;

fn main() {

  let kalman_filter = KalmanFilter {
    // Process noise covariance
    q: matrix![1.0, 0.1;
               0.1, 1.0],
    // Measurement noise matrix
    r: matrix![1.0, 0.2, 0.1;
               0.2, 0.8, 0.5;
               0.1, 0.5, 1.2],
    // Observation matrix
    h: matrix![1.0, 0.7;
               0.5, 0.7;
               0.8, 0.1],
    // State transition matrix
    f: matrix![0.6, 0.2;
               0.1, 0.3],
    // Initial guess for state mean at time 1
    x0: vector![1.0, 1.0],
    // Initial guess for state covariance at time 1
    p0: matrix![1.0, 0.0;
                0.0, 1.0],

  let data: Vec<Vector<f64>> = vec![vector![1.04, 2.20, 3.12],
                                    vector![1.11, 2.33, 3.34],
                                    vector![1.23, 2.21, 3.45]];

  let run_filter = kalman_filter.filter(&data);
  let run_smooth = kalman_filter.smooth(&run_filter.0, &run_filter.1);

  // Print filtered and smoothened state variable coordinates
  for k in 0..3 {
               &run_filter.0[k].x[0], &run_filter.0[k].x[1],
               &run_smooth[k].x[0], &run_smooth[k].x[1])

examples directory contains code sample which allows to import data from a CSV file and returns filtered and smoothed data to stdout.


This project is licensed under GPL3.

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