Kalman Filters for Embedded Targets (in Rust)
This is the Rust port of my kalman-clib library, a microcontroller targeted
Kalman filter implementation, as well as the
libfixkalman C library for Q16.16 fixed-point Kalman filters. It optionally
uses micromath
for square root calculations on no_std
, and can use libm
is wished for.
Depending on the configuration, this crate may require 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.
no_std
vs std
, alloc
This crate builds as no_std
by default. To build with std
support, run:
cargo build --features=std
Independently of std
you can turn on alloc
features. This enables simplified builders with heap-allocated buffers:
cargo build --features=alloc
Examples
Targets with allocations (std
or alloc
)
When the alloc
crate feature is enabled either directly or implicitly via std
,
some builders are enabled that allow for simple creation of filters. This should help non-embedded use cases, or any
use case that does not have to explicitly manage buffer locations, to get an easier start:
const NUM_STATES: usize = 3;
const NUM_CONTROLS: usize = 2;
const NUM_OBSERVATIONS: usize = 1;
Extended Kalman Filters
The general setup remains the same, however the predict
and correct
methods are
replaced with their nonlinear counterparts:
const NUM_STATES: usize = 3;
const NUM_OBSERVATIONS: usize = 1;
For a slightly more realistic EKF example that simulates radar measurements of a moving object,
see the radar-2d
example.
cargo run --example radar-2d --features=std
Embedded Targets
An example for STM32F303 microcontrollers can be found in the
xbuild-tests/stm32
directory. It showcases both fixed-point and floating-point support.
Q16.16
fixed-point
Run the fixed
example with the fixed
crate feature. This enables I16F16
type support, similar to
the libfixkalman C library.
cargo run --example fixed --features=fixed
Gravity Constant Estimation Example
To run the example gravity
simulation, run either
cargo run --example gravity --features=std
cargo run --example gravity --features=std,libm
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