Updaters
- class stonesoup.updater.base.Updater(measurement_model: MeasurementModel)[source]
Bases:
stonesoup.base.Base
Updater base class
An updater is used to update the predicted state, utilising a measurement and a
MeasurementModel
. The general observation model is\[\mathbf{z} = h(\mathbf{x}, \mathbf{\sigma})\]where \(\mathbf{x}\) is the state, \(\mathbf{\sigma}\), the measurement noise and \(\mathbf{z}\) the resulting measurement.
- Parameters
measurement_model (
MeasurementModel
) – measurement model
- measurement_model: stonesoup.models.measurement.base.MeasurementModel
measurement model
- abstract predict_measurement(state_prediction, measurement_model=None, **kwargs)[source]
Get measurement prediction from state prediction
- Parameters
state_prediction (
StatePrediction
) – The state predictionmeasurement_model (
MeasurementModel
, optional) – The measurement model used to generate the measurement prediction. Should be used in cases where the measurement model is dependent on the received measurement. The default is None, in which case the updater will use the measurement model specified on initialisation
- Returns
The predicted measurement
- Return type
- abstract update(hypothesis, **kwargs)[source]
Update state using prediction and measurement.
- Parameters
hypothesis (
Hypothesis
) – Hypothesis with predicted state and associated detection used for updating.- Returns
The state posterior
- Return type
Kalman
- class stonesoup.updater.kalman.KalmanUpdater(measurement_model: LinearGaussian = None, force_symmetric_covariance: bool = False)[source]
Bases:
stonesoup.updater.base.Updater
A class which embodies Kalman-type updaters; also a class which performs measurement update step as in the standard Kalman Filter.
The Kalman updaters assume \(h(\mathbf{x}) = H \mathbf{x}\) with additive noise \(\sigma = \mathcal{N}(0,R)\). Daughter classes can overwrite to specify a more general measurement model \(h(\mathbf{x})\).
update()
first callspredict_measurement()
function which proceeds by calculating the predicted measurement, innovation covariance and measurement cross-covariance,\[ \begin{align}\begin{aligned}\mathbf{z}_{k|k-1} = H_k \mathbf{x}_{k|k-1}\\S_k = H_k P_{k|k-1} H_k^T + R_k\\\Upsilon_k = P_{k|k-1} H_k^T\end{aligned}\end{align} \]where \(P_{k|k-1}\) is the predicted state covariance.
predict_measurement()
returns aGaussianMeasurementPrediction
. The Kalman gain is then calculated as,\[K_k = \Upsilon_k S_k^{-1}\]and the posterior state mean and covariance are,
\[ \begin{align}\begin{aligned}\mathbf{x}_{k|k} = \mathbf{x}_{k|k-1} + K_k (\mathbf{z}_k - H_k \mathbf{x}_{k|k-1})\\P_{k|k} = P_{k|k-1} - K_k S_k K_k^T\end{aligned}\end{align} \]These are returned as a
GaussianStateUpdate
object.- Parameters
measurement_model (
LinearGaussian
, optional) – A linear Gaussian measurement model. This need not be defined if a measurement model is provided in the measurement. If no model specified on construction, or in the measurement, then error will be thrown.force_symmetric_covariance (
bool
, optional) – A flag to force the output covariance matrix to be symmetric by way of a simple geometric combination of the matrix and transpose. Default is False.
- measurement_model: stonesoup.models.measurement.linear.LinearGaussian
A linear Gaussian measurement model. This need not be defined if a measurement model is provided in the measurement. If no model specified on construction, or in the measurement, then error will be thrown.
- force_symmetric_covariance: bool
A flag to force the output covariance matrix to be symmetric by way of a simple geometric combination of the matrix and transpose. Default is False.
- predict_measurement(predicted_state, measurement_model=None, **kwargs)[source]
Predict the measurement implied by the predicted state mean
- Parameters
predicted_state (
GaussianState
) – The predicted state \(\mathbf{x}_{k|k-1}\), \(P_{k|k-1}\)measurement_model (
MeasurementModel
) – The measurement model. If omitted, the model in the updater object is used**kwargs (various) – These are passed to
function()
andmatrix()
- Returns
The measurement prediction, \(\mathbf{z}_{k|k-1}\)
- Return type
GaussianMeasurementPrediction
- update(hypothesis, **kwargs)[source]
The Kalman update method. Given a hypothesised association between a predicted state or predicted measurement and an actual measurement, calculate the posterior state.
- Parameters
hypothesis (
SingleHypothesis
) – the prediction-measurement association hypothesis. This hypothesis may carry a predicted measurement, or a predicted state. In the latter case a predicted measurement will be calculated.**kwargs (various) – These are passed to
predict_measurement()
- Returns
The posterior state Gaussian with mean \(\mathbf{x}_{k|k}\) and covariance \(P_{x|x}\)
- Return type
- class stonesoup.updater.kalman.ExtendedKalmanUpdater(measurement_model: MeasurementModel = None, force_symmetric_covariance: bool = False)[source]
Bases:
stonesoup.updater.kalman.KalmanUpdater
The Extended Kalman Filter version of the Kalman Updater. Inherits most of the functionality from
KalmanUpdater
.The difference is that the measurement model may now be non-linear, though must be differentiable to return the linearisation of \(h(\mathbf{x})\) via the matrix \(H\) accessible via
jacobian()
.- Parameters
measurement_model (
MeasurementModel
, optional) – A measurement model. This need not be defined if a measurement model is provided in the measurement. If no model specified on construction, or in the measurement, then error will be thrown. Must be linear or capable or implement thejacobian()
.force_symmetric_covariance (
bool
, optional) – A flag to force the output covariance matrix to be symmetric by way of a simple geometric combination of the matrix and transpose. Default is False.
- measurement_model: stonesoup.models.measurement.base.MeasurementModel
A measurement model. This need not be defined if a measurement model is provided in the measurement. If no model specified on construction, or in the measurement, then error will be thrown. Must be linear or capable or implement the
jacobian()
.
- class stonesoup.updater.kalman.UnscentedKalmanUpdater(measurement_model: MeasurementModel = None, force_symmetric_covariance: bool = False, alpha: float = 0.5, beta: float = 2, kappa: float = 0)[source]
Bases:
stonesoup.updater.kalman.KalmanUpdater
The Unscented Kalman Filter version of the Kalman Updater. Inherits most of the functionality from
KalmanUpdater
.In this case the
predict_measurement()
function uses theunscented_transform()
function to estimate a (Gaussian) predicted measurement. This is then updated via the standard Kalman update equations.- Parameters
measurement_model (
MeasurementModel
, optional) – The measurement model to be used. This need not be defined if a measurement model is provided in the measurement. If no model specified on construction, or in the measurement, then error will be thrown.force_symmetric_covariance (
bool
, optional) – A flag to force the output covariance matrix to be symmetric by way of a simple geometric combination of the matrix and transpose. Default is False.alpha (
float
, optional) – Primary sigma point spread scaling parameter. Default is 0.5.beta (
float
, optional) – Used to incorporate prior knowledge of the distribution. If the true distribution is Gaussian, the value of 2 is optimal. Default is 2kappa (
float
, optional) – Secondary spread scaling parameter. Default is calculated as 3-Ns
- measurement_model: stonesoup.models.measurement.base.MeasurementModel
The measurement model to be used. This need not be defined if a measurement model is provided in the measurement. If no model specified on construction, or in the measurement, then error will be thrown.
- beta: float
Used to incorporate prior knowledge of the distribution. If the true distribution is Gaussian, the value of 2 is optimal. Default is 2
- predict_measurement(predicted_state, measurement_model=None)[source]
Unscented Kalman Filter measurement prediction step. Uses the unscented transform to estimate a Gauss-distributed predicted measurement.
- Parameters
predicted_state (
GaussianStatePrediction
) – A predicted statemeasurement_model (
MeasurementModel
, optional) – The measurement model used to generate the measurement prediction. This should be used in cases where the measurement model is dependent on the received measurement (the default is None, in which case the updater will use the measurement model specified on initialisation)
- Returns
The measurement prediction
- Return type
- class stonesoup.updater.kalman.SqrtKalmanUpdater(measurement_model: LinearGaussian = None, force_symmetric_covariance: bool = False, qr_method: bool = False)[source]
Bases:
stonesoup.updater.kalman.KalmanUpdater
The Square root version of the Kalman Updater.
The input
State
is aSqrtGaussianState
which means that the covariance of the predicted state is stored in square root form. This can be achieved by keepingcovar
attribute as \(L\) where the ‘full’ covariance matrix \(P_{k|k-1} = L_{k|k-1} L^T_{k|k-1}\) [Eq1].In its basic form \(L\) is the lower triangular matrix returned via Cholesky factorisation. There’s no reason why other forms that satisfy Eq 1 above can’t be used.
References
Schmidt, S.F. 1970, Computational techniques in Kalman filtering, NATO advisory group for aerospace research and development, London 1970
Andrews, A. 1968, A square root formulation of the Kalman covariance equations, AIAA Journal, 6:6, 1165-1166
- Parameters
measurement_model (
LinearGaussian
, optional) – A linear Gaussian measurement model. This need not be defined if a measurement model is provided in the measurement. If no model specified on construction, or in the measurement, then error will be thrown.force_symmetric_covariance (
bool
, optional) – A flag to force the output covariance matrix to be symmetric by way of a simple geometric combination of the matrix and transpose. Default is False.qr_method (
bool
, optional) – A switch to do the update via a QR decomposition, rather than using the (vector form of) the Potter method.
- class stonesoup.updater.kalman.IteratedKalmanUpdater(measurement_model: MeasurementModel = None, force_symmetric_covariance: bool = False, tolerance: float = 1e-06, measure: Measure = Euclidean(mapping=None, mapping2=None), max_iterations: int = 1000)[source]
Bases:
stonesoup.updater.kalman.ExtendedKalmanUpdater
This version of the Kalman updater runs an iteration over the linearisation of the sensor function in order to refine the posterior state estimate. Specifically,
\[ \begin{align}\begin{aligned}x_{k,i+1} &= x_{k|k-1} + K_i [z - h(x_{k,i}) - H_i (x_{k|k-1} - x_{k,i}) ]\\P_{k,i+1} &= (I - K_i H_i) P_{k|k-1}\end{aligned}\end{align} \]where,
\[ \begin{align}\begin{aligned}H_i &= h^{\prime}(x_{k,i}),\\K_i &= P_{k|k-1} H_i^T (H_i P_{k|k-1} H_i^T + R)^{-1}\end{aligned}\end{align} \]and
\[ \begin{align}\begin{aligned}x_{k,0} &= x_{k|k-1}\\P_{k,0} &= P_{k|k-1}\end{aligned}\end{align} \]It inherits from the ExtendedKalmanUpdater as it uses the same linearisation of the sensor function via the
_measurement_matrix()
function.- Parameters
measurement_model (
MeasurementModel
, optional) – A measurement model. This need not be defined if a measurement model is provided in the measurement. If no model specified on construction, or in the measurement, then error will be thrown. Must be linear or capable or implement thejacobian()
.force_symmetric_covariance (
bool
, optional) – A flag to force the output covariance matrix to be symmetric by way of a simple geometric combination of the matrix and transpose. Default is False.tolerance (
float
, optional) – The value of the difference in the measure used as a stopping criterion.measure (
Measure
, optional) – The measure to use to test the iteration stopping criterion. Defaults to the Euclidean distance between current and prior posterior state estimate.max_iterations (
int
, optional) – Number of iterations before while loop is exited and a non-convergence warning is returned
- measure: stonesoup.measures.Measure
The measure to use to test the iteration stopping criterion. Defaults to the Euclidean distance between current and prior posterior state estimate.
- max_iterations: int
Number of iterations before while loop is exited and a non-convergence warning is returned
- update(hypothesis, **kwargs)[source]
The iterated Kalman update method. Given a hypothesised association between a predicted state or predicted measurement and an actual measurement, calculate the posterior state.
- Parameters
hypothesis (
SingleHypothesis
) – the prediction-measurement association hypothesis. This hypothesis may carry a predicted measurement, or a predicted state. In the latter case a predicted measurement will be calculated.**kwargs (various) – These are passed to the measurement model function
- Returns
The posterior state Gaussian with mean \(\mathbf{x}_{k|k}\) and covariance \(P_{k|k}\)
- Return type
Particle
- class stonesoup.updater.particle.ParticleUpdater(measurement_model: MeasurementModel, resampler: Resampler = None)[source]
Bases:
stonesoup.updater.base.Updater
Particle Updater
Perform an update by multiplying particle weights by PDF of measurement model (either
measurement_model
ormeasurement_model
), and normalising the weights. If provided, aresampler
will be used to take a new sample of particles (this is called every time, and it’s up to the resampler to decide if resampling is required).- Parameters
measurement_model (
MeasurementModel
) – measurement modelresampler (
Resampler
, optional) – Resampler to prevent particle degeneracy
- resampler: stonesoup.resampler.base.Resampler
Resampler to prevent particle degeneracy
- update(hypothesis, **kwargs)[source]
Particle Filter update step
- Parameters
hypothesis (
Hypothesis
) – Hypothesis with predicted state and associated detection used for updating.- Returns
The state posterior
- Return type
- predict_measurement(state_prediction, measurement_model=None, **kwargs)[source]
Get measurement prediction from state prediction
- Parameters
state_prediction (
StatePrediction
) – The state predictionmeasurement_model (
MeasurementModel
, optional) – The measurement model used to generate the measurement prediction. Should be used in cases where the measurement model is dependent on the received measurement. The default is None, in which case the updater will use the measurement model specified on initialisation
- Returns
The predicted measurement
- Return type
- class stonesoup.updater.particle.GromovFlowParticleUpdater(measurement_model: MeasurementModel)[source]
Bases:
stonesoup.updater.base.Updater
Gromov Flow Particle Updater
This is implementation of Gromov method for stochastic particle flow filters 1. The Euler Maruyama method is used for integration, over 20 steps using an exponentially increase step size.
- Parameters
measurement_model (
MeasurementModel
) – measurement model
References
- 1
Daum, Fred & Huang, Jim & Noushin, Arjang. “Generalized Gromov method for stochastic particle flow filters.” 2017
- update(hypothesis, **kwargs)[source]
Update state using prediction and measurement.
- Parameters
hypothesis (
Hypothesis
) – Hypothesis with predicted state and associated detection used for updating.- Returns
The state posterior
- Return type
- predict_measurement(state_prediction, measurement_model=None, **kwargs)
Get measurement prediction from state prediction
- Parameters
state_prediction (
StatePrediction
) – The state predictionmeasurement_model (
MeasurementModel
, optional) – The measurement model used to generate the measurement prediction. Should be used in cases where the measurement model is dependent on the received measurement. The default is None, in which case the updater will use the measurement model specified on initialisation
- Returns
The predicted measurement
- Return type
- class stonesoup.updater.particle.GromovFlowKalmanParticleUpdater(measurement_model: MeasurementModel, kalman_updater: KalmanUpdater = None)[source]
Bases:
stonesoup.updater.particle.GromovFlowParticleUpdater
Gromov Flow Parallel Kalman Particle Updater
This is a wrapper around the
GromovFlowParticleUpdater
which can use aExtendedKalmanUpdater
orUnscentedKalmanUpdater
in parallel in order to maintain a state covariance, as proposed in 2. In this implementation, the mean of theParticleState
is used the EKF/UKF update.This should be used in conjunction with the
ParticleFlowKalmanPredictor
.- Parameters
measurement_model (
MeasurementModel
) – measurement modelkalman_updater (
KalmanUpdater
, optional) – Kalman updater to use. Default None where a new instance of:class:~.ExtendedKalmanUpdater will be created utilising thesame measurement model.
References
- 2
Ding, Tao & Coates, Mark J., “Implementation of the Daum-Huang Exact-Flow Particle Filter” 2012
- kalman_updater: stonesoup.updater.kalman.KalmanUpdater
Kalman updater to use. Default None where a new instance of:class:~.ExtendedKalmanUpdater will be created utilising thesame measurement model.
- update(hypothesis, **kwargs)[source]
Update state using prediction and measurement.
- Parameters
hypothesis (
Hypothesis
) – Hypothesis with predicted state and associated detection used for updating.- Returns
The state posterior
- Return type
- predict_measurement(state_prediction, *args, **kwargs)[source]
Get measurement prediction from state prediction
- Parameters
state_prediction (
StatePrediction
) – The state predictionmeasurement_model (
MeasurementModel
, optional) – The measurement model used to generate the measurement prediction. Should be used in cases where the measurement model is dependent on the received measurement. The default is None, in which case the updater will use the measurement model specified on initialisation
- Returns
The predicted measurement
- Return type
Information
- class stonesoup.updater.information.InformationKalmanUpdater(measurement_model: LinearGaussian = None, force_symmetric_covariance: bool = False)[source]
Bases:
stonesoup.updater.kalman.KalmanUpdater
A class which implements the update of information form of the Kalman filter. This is conceptually very simple. The update proceeds as:
\[ \begin{align}\begin{aligned}Y_{k|k} = Y_{k|k-1} + H^{T}_k R^{-1}_k H_k\\\mathbf{y}_{k|k} = \mathbf{y}_{k|k-1} + H^{T}_k R^{-1}_k \mathbf{z}_{k}\end{aligned}\end{align} \]where \(\mathbf{y}_{k|k-1}\) is the predicted information state and \(Y_{k|k-1}\) the predicted information matrix which form the
InformationStatePrediction
object. The measurement matrix \(H_k\) and measurement covariance \(R_k\) are those in the Kalman filter (see tutorial 1). AnInformationStateUpdate
object is returned.Note
Analogously with the
InformationKalmanPredictor
, the measurement model is queried for the existence of aninverse_covar()
property. If absent, thecovar()
is inverted.- Parameters
measurement_model (
LinearGaussian
, optional) – A linear Gaussian measurement model. This need not be defined if a measurement model is provided in the measurement. If no model specified on construction, or in the measurement, then error will be thrown.force_symmetric_covariance (
bool
, optional) – A flag to force the output covariance matrix to be symmetric by way of a simple geometric combination of the matrix and transpose. Default is False.
- measurement_model: stonesoup.models.measurement.linear.LinearGaussian
A linear Gaussian measurement model. This need not be defined if a measurement model is provided in the measurement. If no model specified on construction, or in the measurement, then error will be thrown.
- predict_measurement(predicted_state, measurement_model=None, **kwargs)[source]
There’s no direct analogue of a predicted measurement in the information form. This method is therefore provided to return the predicted measurement as would the standard Kalman updater. This is mainly for compatibility as it’s not anticipated that it would be used in the usual operation of the information filter.
- Parameters
predicted_information_state (
State
) – The predicted state in information form \(\mathbf{y}_{k|k-1}\)measurement_model (
MeasurementModel
) – The measurement model. If omitted, the model in the updater object is used**kwargs (various) – These are passed to
matrix()
- Returns
The measurement prediction, \(H \mathbf{x}_{k|k-1}\)
- Return type
- update(hypothesis, **kwargs)[source]
The Information filter update (corrector) method. Given a hypothesised association between a predicted information state and an actual measurement, calculate the posterior information state.
- Parameters
hypothesis (
SingleHypothesis
) – the prediction-measurement association hypothesis. This hypothesis carries a predicted information state.**kwargs (various) – These are passed to
predict_measurement()
- Returns
The posterior information state with information state \(\mathbf{y}_{k|k}\) and precision \(Y_{k|k}\)
- Return type
Point Process
- class stonesoup.updater.pointprocess.PointProcessUpdater(updater: KalmanUpdater, clutter_spatial_density: float = 1e-26, normalisation: bool = True, prob_detection: Probability = 1, prob_survival: Probability = 1)[source]
Bases:
stonesoup.base.Base
Base updater class for the implementation of any Gaussian Mixture (GM) point process derived multi target filters such as the Probability Hypothesis Density (PHD), Cardinalised Probability Hypothesis Density (CPHD) or Linear Complexity with Cumulants (LCC) filters
- Parameters
updater (
KalmanUpdater
) – Underlying updater used to perform the single target Kalman Update.clutter_spatial_density (
float
, optional) – Spatial density of the clutter process uniformly distributed across the state space.normalisation (
bool
, optional) – Flag for normalisationprob_detection (
Probability
, optional) – Probability of a target being detected at the current timestepprob_survival (
Probability
, optional) – Probability of a target surviving until the next timestep
- updater: stonesoup.updater.kalman.KalmanUpdater
Underlying updater used to perform the single target Kalman Update.
- clutter_spatial_density: float
Spatial density of the clutter process uniformly distributed across the state space.
- prob_detection: stonesoup.types.numeric.Probability
Probability of a target being detected at the current timestep
- prob_survival: stonesoup.types.numeric.Probability
Probability of a target surviving until the next timestep
- update(hypotheses)[source]
Updates the current components in a
GaussianMixture
by applying the underlyingKalmanUpdater
updater to each component with the supplied measurements.- Parameters
hypotheses (list of
MultipleHypothesis
) – Measurements obtained at time \(k+1\)- Returns
updated_components – GaussianMixtureMultiTargetTracker with updated components at time \(k+1\)
- Return type
GaussianMixtureUpdate
- class stonesoup.updater.pointprocess.PHDUpdater(updater: KalmanUpdater, clutter_spatial_density: float = 1e-26, normalisation: bool = True, prob_detection: Probability = 1, prob_survival: Probability = 1)[source]
Bases:
stonesoup.updater.pointprocess.PointProcessUpdater
A implementation of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) multi-target filter
References
[1] B.-N. Vo and W.-K. Ma, “The Gaussian Mixture Probability Hypothesis Density Filter,” Signal Processing,IEEE Transactions on, vol. 54, no. 11, pp. 4091–4104, 2006. https://ieeexplore.ieee.org/document/1710358.
- Parameters
updater (
KalmanUpdater
) – Underlying updater used to perform the single target Kalman Update.clutter_spatial_density (
float
, optional) – Spatial density of the clutter process uniformly distributed across the state space.normalisation (
bool
, optional) – Flag for normalisationprob_detection (
Probability
, optional) – Probability of a target being detected at the current timestepprob_survival (
Probability
, optional) – Probability of a target surviving until the next timestep
- class stonesoup.updater.pointprocess.LCCUpdater(updater: KalmanUpdater, clutter_spatial_density: float = 1e-26, normalisation: bool = True, prob_detection: Probability = 1, prob_survival: Probability = 1, mean_number_of_false_alarms: float = 1, variance_of_false_alarms: float = 1)[source]
Bases:
stonesoup.updater.pointprocess.PointProcessUpdater
A implementation of the Gaussian Mixture Linear Complexity with Cumulants (GM-LCC) multi-target filter
References
- [1] D. E. Clark and F. De Melo. “A Linear-Complexity Second-Order
Multi-Object Filter via Factorial Cumulants”. In: 2018 21st International Conference on Information Fusion (FUSION). 2018. DOI: 10. 23919/ICIF.2018.8455331. https://ieeexplore.ieee.org/document/8455331..
- Parameters
updater (
KalmanUpdater
) – Underlying updater used to perform the single target Kalman Update.clutter_spatial_density (
float
, optional) – Spatial density of the clutter process uniformly distributed across the state space.normalisation (
bool
, optional) – Flag for normalisationprob_detection (
Probability
, optional) – Probability of a target being detected at the current timestepprob_survival (
Probability
, optional) – Probability of a target surviving until the next timestepmean_number_of_false_alarms (
float
, optional) – Mean number of false alarms (clutter) expected per timestepvariance_of_false_alarms (
float
, optional) – Variance on the number of false alarms (clutter) expected per timestep
AlphaBeta
- class stonesoup.updater.alphabeta.AlphaBetaUpdater(measurement_model: MeasurementModel, alpha: float, beta: float, vmap: numpy.ndarray = None)[source]
Bases:
stonesoup.updater.base.Updater
Conceptually, the \(\alpha-\beta\) filter is similar to its Kalman cousins in that it operates recursively over predict and update steps. It assumes that a state vector is decomposable into quantities and the rates of change of those quantities. We refer to these as position \(p\) and velocity \(v\) respectively, though they aren’t confined to locations in space. If the interval from \(t_{k-1} \rightarrow t_k\) is \(\Delta T\), and at \(k\), we can gain a (noisy) measurement of the position, \(p^z_k\).
The recursion proceeds as:
Predict
\[ \begin{align}\begin{aligned}p_{k|k-1} &= p_{k-1} + \Delta T v_{k-1}\\v_{k|k-1} &= v_{k-1}\end{aligned}\end{align} \]Update
\[ \begin{align}\begin{aligned}s_k &= p^z_k - p_{k|k-1} \: (\mathrm{innovation})\\p_k &= p_{k|k-1} + \alpha s_k\\v_k &= v_{k|k-1} + \frac{\beta}{\Delta T} s_k\end{aligned}\end{align} \]The \(\alpha\) and \(\beta\) parameters which give the filter its name are small, \(0 < \alpha < 1\) and \(0 < \beta \leq 2\). Colloquially, the larger the values of the parameters, the more influence the measurements have over the transition model; \(\beta\) is usually much smaller than \(\alpha\).
As the prediction is just the application of a constant velocity model, there is no \(\alpha-\beta\) predictor provided in Stone Soup. It is assumed that the predictions passed to the hypothesis have been generated by a constant velocity model. Any application of a control model is also assumed to have taken place during the prediction stage.
This class assumes the velocity is in units of the length per second. If different units are required, scale the prior appropriately.
The measurement model used should be linear and a measurement model such that it provides a ‘mapping’ to \(p\) via the
mapping
tuple and a binary measurement matrix which returns \(p\). This isn’t checked.- Parameters
measurement_model (
MeasurementModel
) – measurement modelalpha (
float
) – The alpha parameter. Controls the weight given to the measurements over the transition model.beta (
float
) – The beta parameter. Controls the amount of variation allowed in the velocity component.vmap (
numpy.ndarray
, optional) – Binary map of the velocity elements in the state vector. If left default, the class will assume that the velocity elements interleave the position elements in the state vector.
- alpha: float
The alpha parameter. Controls the weight given to the measurements over the transition model.
- beta: float
The beta parameter. Controls the amount of variation allowed in the velocity component.
- vmap: numpy.ndarray
Binary map of the velocity elements in the state vector. If left default, the class will assume that the velocity elements interleave the position elements in the state vector.
- predict_measurement(prediction, measurement_model=None, **kwargs)[source]
Return the predicted measurement
- Parameters
prediction (
StatePrediction
) – The state prediction- Returns
The predicted measurement
- Return type
- update(hypothesis, time_interval, **kwargs)[source]
Calculate the inferred state following update
- Parameters
hypothesis (
Hypothesis
) – A hypothesis associates a measurement with a predictiontime_interval (
timedelta
) – The time interval over which the prediction has been made.
- Returns
The updated state
- Return type