kalman-filter
v2.3.0
Published
Kalman filter (and Extended Kalman Filter) Multi-dimensional implementation in Javascript
Downloads
2,320
Maintainers
Keywords
Readme
kalman-filter
Kalman Filter in JavaScript (for both node.js and the browser)
This library implements following features:
- N-dimensional Kalman Filter (for multivariate Gaussian)
- Forward Kalman Filter (Online)
- Forward-Backward Smoothing Kalman Filter
- Split Prediction/Correction steps
- Extended Kalman Filter
- Correlation Matrix
Demos/Examples
|Link | Description | Image | |---|---|---| | Bikes | 4D Constant Acceleration boxes | | | Bouncing Ball | 2D constant acceleration with bounces | | | Sinusoidale Extended Kalman-Filter | 1D Extended KF Sinus | | | Code pen GPS Data smoothing with constant speed | 2D constant speed | | | Partial Observation | 1D / 2 sensor with missing values | | | Smooth 3x3 rotation matrix | 4d smoothing | |
Open an issue to add more examples in this section explaining how you use this library !
Installation
Npm
npm install kalman-filter
const {KalmanFilter} = require('kalman-filter');
Browser usage
Download the file kalman-filter.min.js
from Releases page
Add it to your project like :
<script src="dist/kalman-filter.min.js"></script>
<script>
var {KalmanFilter} = kalmanFilter;
// ... do whatever you want with KalmanFilter
</script>
Simple Example
1D Smoothing Usage
const {KalmanFilter} = require('kalman-filter');
const observations = [0, 0.1, 0.5, 0.2, 3, 4, 2, 1, 2, 3, 5, 6];
// this is creating a smoothing
const kFilter = new KalmanFilter();
const res = kFilter.filterAll(observations)
// res is a list of list (for multidimensional filters)
// [
// [ 0 ],
// [ 0.06666665555510715 ],
// [ 0.3374999890620582 ],
// [ 0.25238094852592136 ],
// [ 1.9509090885288296 ],
// [ 3.2173611101031616 ],
// [ 2.4649867370240965 ],
// [ 1.5595744679428254 ],
// [ 1.831772445766021 ],
// [ 2.5537767922925685 ],
// [ 4.065625882212133 ],
// [ 5.26113483436549 ]
// ]
Result is :
2D Smoothing Usage
const {KalmanFilter} = require('kalman-filter');
const observations = [[0, 1], [0.1, 0.5], [0.2, 3], [4, 2], [1, 2]];
const kFilter = new KalmanFilter({observation: 2});
// equivalent to
// new KalmanFilter({
// observation: {
// name: 'sensor',
// sensorDimension: 2
// }
// });
const res = kFilter.filterAll(observations)
2D Smoothing with constant-speed model
const {KalmanFilter} = require('kalman-filter');
const observations = [[0, 1], [0.1, 0.5], [0.2, 3], [4, 2], [1, 2]];
const kFilter = new KalmanFilter({
observation: 2,
dynamic: 'constant-speed'
});
// equivalent to
// new KalmanFilter({
// observation: {
// name: 'sensor',
// sensorDimension: 2
// },
// dynamic: {
// name: 'constant-speed'
// },
// });
const res = kFilter.filterAll(observations)
How to instantiate your kalman filter
Advanced usage
This library gives you the ability to fully configure your kalman-filter.
For advanced usage, here is the correspondance table with the matrix name of the wikipedia article
| Wikipedia article | kalman-filter js lib |
|--|--|
| $F_k$, the state-transition model | dynamic.transition
|
| $H_k$, the observation model | observation.stateProjection
|
| $Q_k$, the covariance of the process noise | dynamic.covariance
|
| $R_k$, the covariance of the observation noise | observation.covariance
|
| $B_k u_k$, the control-input model multiplied by the control vector | dynamic.constant
|
|$\mathbf{P}{0\mid 0}$| dynamic.init.covariance
|
|$\mathbf{x}{0\mid 0}$| dynamic.init.mean
|
Configure the dynamic with dynamic.name
dynamic.name
is a shortcut to give you access to preconfigured dynamic models, you can also register your own shortcust see Register models shortcuts
Available default models as :
- constant-position
- constant-speed
- constant-acceleration
This will automatically configure the dynamic.transition
matrix.
constant-position
\begin{align}
State :& \begin{bmatrix} x_t \end{bmatrix}\\
Transition Equation :& x_t \sim x_{t-1} \\
dynamic.transition :& \begin{bmatrix} 1 \end{bmatrix}
\end{align}
constant-speed
\begin{align}
State :& \begin{bmatrix} x_t \\ speed_t \end{bmatrix} \\
Transition Equation :&
\begin{split}
x_t &\sim x_{t-1} + speed_{t-1},\\
speed_t &\sim speed_{t-1}
\end{split} \\
dynamic.transition :& \begin{bmatrix} 1 & 1 \\ 0 & 1 \end{bmatrix}
\end{align}
constant-acceleration
\begin{align}
State :& \begin{bmatrix} x_t \\ speed_t \\ acc_t \end{bmatrix} \\
Transition Equation :&
\begin{split}
x_t &\sim x_{t-1} + speed_{t-1} \\
speed_t &\sim speed_{t-1} + acc_{t-1} \\
acc_t &\sim acc_{t-1}
\end{split} \\
dynamic.transition :& \begin{bmatrix} 1 & 1 & 0 \\ 0 & 1 & 1 \\ 0 & 0 & 1\end{bmatrix}
\end{align}
'constant-position' on 2D data
This is the default behavior
const {KalmanFilter} = require('kalman-filter');
const kFilter = new KalmanFilter({
observation: {
sensorDimension: 2,
name: 'sensor'
},
dynamic: {
name: 'constant-position',// observation.sensorDimension == dynamic.dimension
covariance: [3, 4]// equivalent to diag([3, 4])
}
});
'constant-speed' on 3D data
const {KalmanFilter} = require('kalman-filter');
const kFilter = new KalmanFilter({
observation: {
sensorDimension: 3,
name: 'sensor'
},
dynamic: {
name: 'constant-speed',// observation.sensorDimension * 2 == state.dimension
timeStep: 0.1,
covariance: [3, 3, 3, 4, 4, 4]// equivalent to diag([3, 3, 3, 4, 4, 4])
}
});
'constant-acceleration' on 2D data
const {KalmanFilter} = require('kalman-filter');
const kFilter = new KalmanFilter({
observation: {
sensorDimension: 2,
name: 'sensor'
},
dynamic: {
name: 'constant-acceleration',// observation.sensorDimension * 3 == state.dimension
timeStep: 0.1,
covariance: [3, 3, 4, 4, 5, 5]// equivalent to diag([3, 3, 4, 4, 5, 5])
}
});
Instanciation of a generic linear model
This is an example of how to build a constant speed model, in 3D without dynamic.name
, using detailed api.
dynamic.dimension
is the size of the statedynamic.transition
is the state transition model that defines the dynamic of the systemdynamic.covariance
is the covariance matrix of the transition modeldynamic.init
is used for initial state (we generally set a big covariance on it)
const {KalmanFilter} = require('kalman-filter');
const timeStep = 0.1;
const huge = 1e8;
const kFilter = new KalmanFilter({
observation: {
dimension: 3
},
dynamic: {
init: {
// We just use random-guessed values here that seems reasonable
mean: [[500], [500], [500], [0], [0], [0]],
// We init the dynamic model with a huge covariance cause we don't
// have any idea where my modeled object before the first observation is located
covariance: [
[huge, 0, 0, 0, 0, 0],
[0, huge, 0, 0, 0, 0],
[0, 0, huge, 0, 0, 0],
[0, 0, 0, huge, 0, 0],
[0, 0, 0, 0, huge, 0],
[0, 0, 0, 0, 0, huge],
],
},
// Corresponds to (x, y, z, vx, vy, vz)
dimension: 6,
// This is a constant-speed model on 3D : [ [Id , timeStep*Id], [0, Id]]
transition: [
[1, 0, 0, timeStep, 0, 0],
[0, 1, 0, 0, timeStep, 0],
[0, 0, 1, 0, 0, timeStep],
[0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1]
],
// Diagonal covariance for independant variables
// since timeStep = 0.1,
// it makes sense to consider speed variance to be ~ timeStep^2 * positionVariance
covariance: [1, 1, 1, 0.01, 0.01, 0.01]// equivalent to diag([1, 1, 1, 0.01, 0.01, 0.01])
}
});
Configure the observation
Using sensor
observation
The observation is made from 2 different sensors with identical properties (i.e. same covariances) , the input measure will be [<sensor0-dim0>, <sensor0-dim1>, <sensor1-dim0>, <sensor1-dim1>]
.
const {KalmanFilter} = require('kalman-filter');
const timeStep = 0.1;
const kFilter = new KalmanFilter({
observation: {
sensorDimension: 2,// observation.dimension == observation.sensorDimension * observation.nSensors
nSensors: 2,
sensorCovariance: [3, 4], // equivalent to diag([3, 4])
name: 'sensor'
},
dynamic: {
name: 'constant-speed',// observation.sensorDimension * 2 == state.dimension
covariance: [3, 3, 4, 4]// equivalent to diag([3, 3, 4, 4])
}
});
Custom Observation matrix
The observation is made from 2 different sensors with different properties (i.e. different covariances), the input measure will be [<sensor0-dim0>, <sensor0-dim1>, <sensor1-dim0>, <sensor1-dim1>]
.
This can be achived manually by using the detailed API :
observation.dimension
is the size of the observationobservation.stateProjection
is the matrix that transforms state into observation, also called observation modelobservation.covariance
is the covariance matrix of the observation model
const {KalmanFilter} = require('kalman-filter');
const timeStep = 0.1;
const kFilter = new KalmanFilter({
observation: {
dimension: 4,
stateProjection: [
[1, 0, 0, 0],
[0, 1, 0, 0],
[1, 0, 0, 0],
[0, 1, 0, 0]
],
covariance: [3, 4, 0.3, 0.4]
},
dynamic: {
name: 'constant-speed',// observation.sensorDimension * 2 == state.dimension
covariance: [3, 3, 4, 4]// equivalent to diag([3, 3, 4, 4])
}
});
Play with Kalman Filter
In order to use the Kalman-Filter with a dynamic or observation model which is not strictly a General linear model, it is possible to use function
in following parameters :
observation.stateProjection
observation.covariance
dynamic.transition
dynamic.covariance
dynamic.constant
In this situation this function
will return the value of the matrix at each step of the kalman-filter.
In this example, we create a constant-speed filter with non-uniform intervals;
const {KalmanFilter} = require('kalman-filter');
const intervals = [1,1,1,1,2,1,1,1];
const kFilter = new KalmanFilter({
observation: {
dimension: 2,
/**
* @param {State} opts.predicted
* @param {Array.<Number>} opts.observation
* @param {Number} opts.index
*/
stateProjection: function(opts){
return [
[1, 0, 0, 0],
[0, 1, 0, 0]
]
},
/**
* @param {State} opts.predicted
* @param {Array.<Number>} opts.observation
* @param {Number} opts.index
*/
covariance: function(opts){
return [
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
]
}
},
dynamic: {
dimension: 4, //(x, y, vx, vy)
/**
* @param {State} opts.previousCorrected
* @param {Number} opts.index
*/
transition: function(opts){
const dT = intervals[opts.index];
if(typeof(dT) !== 'number' || isNaN(dT) || dT <= 0){
throw(new Error('dT should be positive number'))
}
return [
[1, 0, dT, 0],
[0, 1, 0, dT]
[0, 0, 1, 0]
[0, 0, 0, 1]
]
},
/**
* @param {State} opts.previousCorrected
* @param {Number} opts.index
*/
covariance: function(opts){
const dT = intervals[opts.index];
if(typeof(dT) !== 'number' || isNaN(dT) || dT <= 0){
throw(new Error('dT should be positive number'))
}
return [
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1*dT, 0],
[0, 0, 0, 1*dT]
]
}
}
});
Extended
If you want to implement an extended kalman filter
You will need to put your non-linear functions in the following parameters
observation.fn
dynamic.fn
See an example in Sinusoidale Extended Kalman-Filter
Using Control model
If you want to add a constant parameter in the dynamic model (also called control input
), you can use dynamic.constant
function
See an example code in demo/bouncing-ball
or the result in Bouncing Ball example
Use your kalman filter
Simple Batch usage (run it once for the whole dataset)
const observations = [[0, 2], [0.1, 4], [0.5, 9], [0.2, 12]];
// batch kalman filter
const results = kFilter.filterAll(observations);
Online filter
When using online usage (only the forward step), the output of the filter
method is an instance of the "State" class.
// online kalman filter
let previousCorrected = null;
const results = [];
observations.forEach(observation => {
previousCorrected = kFilter.filter({previousCorrected, observation});
results.push(previousCorrected.mean);
});
Predict/Correct detailed usage (run it online)
If you want to use KalmanFilter in more advanced usage, you might want to dissociate the predict
and the correct
functions
// online kalman filter
let previousCorrected = null;
const results = [];
observations.forEach(observation => {
const predicted = kFilter.predict({
previousCorrected
});
const correctedState = kFilter.correct({
predicted,
observation
});
results.push(correctedState.mean);
// update the previousCorrected for next loop iteration
previousCorrected = correctedState
});
console.log(results);
Batch Forward - Backward smoothing usage
The Forward - Backward process
// batch kalman filter
const results = kFilter.filterAll({observations, passMode: 'forward-backward'});
Register models shortcuts
To get more information on how to build a dynamic model, check in the code lib/dynamic/
(or lib/observation
for observation models).
If you feel your model can be used by other, do not hesitate to create a Pull Request.
const {registerDynamic, KalmanFilter, registerObservation} = require('kalman-filter');
registerObservation('custom-sensor', function(opts1){
// do your stuff
return {
dimension,
stateProjection,
covariance
}
})
registerDynamic('custom-dynamic', function(opts2, observation){
// do your stuff
// here you can use the parameter of observation (like observation.dimension)
// to build the parameters for dynamic
return {
dimension,
transition,
covariance
}
})
const kFilter = new KalmanFilter({
observation: {
name: 'custom-sensor',
// ... fields of opts1
},
dynamic: {
name: 'custom-dynamic',
// ... fields of opts2
}
});
Set your model parameters from the ground truths state values
In order to find the proper values for covariance matrix, we use following approach :
const {getCovariance, KalmanFilter} = require('kalman-filter');
// Ground truth values in the dynamic model hidden state
const groundTruthStates = [ // here this is (x, vx)
[[0, 1.1], [1.1, 1], [2.1, 0.9], [3, 1], [4, 1.2]], // example 1
[[8, 1.1], [9.1, 1], [10.1, 0.9], [11, 1], [12, 1.2]] // example 2
]
// Observations of this values
const measures = [ // here this is x only
[[0.1], [1.3], [2.4], [2.6], [3.8]], // example 1
[[8.1], [9.3], [10.4], [10.6], [11.8]] // example 2
];
const kFilter = new KalmanFilter({
observation: {
name: 'sensor',
sensorDimension: 1
},
dynamic: {
name: 'constant-speed'
}
})
const dynamicCovariance = getCovariance({
measures: groundTruthStates.map(ex =>
return ex.slice(1)
).reduce((a,b) => a.concat(b)),
averages: groundTruthStates.map(ex =>
return ex.slice(1).map((_, index) => {
return kFilter.predict({previousCorrected: ex[index - 1]}).mean;
})
).reduce((a,b) => a.concat(b))
});
const observationCovariance = getCovariance({
measures: measures.reduce((a,b) => a.concat(b)),
averages: groundTruthStates.map((a) => a[0]).reduce((a,b) => a.concat(b))
});
How to measure how good does a specific model fits with data
There are different ways to measure the performance of a model against some measures :
Model fits with a specific measurements
We use Mahalanobis distance
const observations = [[0, 2], [0.1, 4], [0.5, 9], [0.2, 12]];
// online kalman filter
let previousCorrected = null;
const results = [];
observations.forEach(observation => {
const predicted = kFilter.predict({
previousCorrected
});
const dist = predicted.mahalanobis(observation)
previousCorrected = kFilter.correct({
predicted,
observation
});
distances.push(dist);
});
const distance = distances.reduce((d1, d2) => d1 + d2, 0);
How precise is this Model
We compare the model with random generated numbers sequence.
const h = require('hasard')
const observationHasard = h.array({value: h.number({type: 'normal'}), size: 2})
const observations = observationHasard.run(200);
// online kalman filter
let previousCorrected = null;
const results = [];
observations.forEach(observation => {
const predicted = kFilter.predict({
previousCorrected
});
const dist = predicted.mahalanobis(measure)
previousCorrected = kFilter.correct({
predicted,
observation
});
distances.push(dist);
});
const distance = distances.reduce((d1, d2) => d1 + d2, 0);
Credits
Thanks to Adrien Pellissier for his hard work on this library.
Similar Project
For a simple 1D Kalman filter in javascript see https://github.com/wouterbulten/kalmanjs