# Source code for stonesoup.updater.alphabeta

# -*- coding: utf-8 -*-

import numpy as np

from ..base import Property
from ..updater import Updater
from ..types.prediction import MeasurementPrediction
from ..types.update import Update

[docs]class AlphaBetaUpdater(Updater): r"""Conceptually, the :math:\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 :math:p and velocity :math:v respectively, though they aren't confined to locations in space. If the interval from :math:t_{k-1} \rightarrow t_k is :math:\Delta T, and at :math:k, we can gain a (noisy) measurement of the position, :math:p^z_k. The recursion proceeds as: * Predict .. math:: p_{k|k-1} &= p_{k-1} + \Delta T v_{k-1} v_{k|k-1} &= v_{k-1} * Update .. math:: 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 The :math:\alpha and :math:\beta parameters which give the filter its name are small, :math:0 < \alpha < 1 and :math:0 < \beta \leq 2. Colloquially, the larger the values of the parameters, the more influence the measurements have over the transition model; :math:\beta is usually much smaller than :math:\alpha. As the prediction is just the application of a constant velocity model, there is no :math:\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 :math:p via the :attr:mapping tuple and a binary measurement matrix which returns :math:p. This isn't checked. """ alpha: float = Property(doc="The alpha parameter. Controls the weight given to the " "measurements over the transition model.") beta: float = Property(doc="The beta parameter. Controls the amount of variation allowed in " "the velocity component.") vmap: np.ndarray = Property(default=None, doc="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.")
[docs] def predict_measurement(self, prediction, measurement_model=None, **kwargs): """Return the predicted measurement Parameters ---------- prediction : :class:~.StatePrediction The state prediction Returns ------- : :class:~.StateVector The predicted measurement """ # This necessary if predict_measurement called on its own measurement_model = self._check_measurement_model(measurement_model) pred_meas = measurement_model.matrix(**kwargs) @ prediction.state_vector return MeasurementPrediction.from_state(prediction, pred_meas)
[docs] def update(self, hypothesis, time_interval, **kwargs): """Calculate the inferred state following update Parameters ---------- hypothesis : :class:~.Hypothesis A hypothesis associates a measurement with a prediction time_interval : :class:~.timedelta The time interval over which the prediction has been made. Returns ------- : :class:~.StateUpdate The updated state """ out_statevector = hypothesis.prediction.state_vector.copy() # Check for the measurement_model in the measurement, if not present use the one in this # updater measurement_model = hypothesis.measurement.measurement_model measurement_model = self._check_measurement_model(measurement_model) # Check for a measurement prediction in the hypothesis if hypothesis.measurement_prediction is None: pred_meas = self.predict_measurement(hypothesis.prediction, measurement_model=measurement_model) else: pred_meas = hypothesis.measurement_prediction pmap = np.array(measurement_model.mapping) if self.vmap is None: vmap = pmap + 1 else: vmap = self.vmap innovation = hypothesis.measurement.state_vector - pred_meas.state_vector out_statevector[pmap] = hypothesis.prediction.state_vector[pmap] + self.alpha * innovation out_statevector[vmap] = hypothesis.prediction.state_vector[vmap] +\ (self.beta / time_interval.total_seconds()) * innovation return Update.from_state(hypothesis.prediction, out_statevector, timestamp=hypothesis.measurement.timestamp, hypothesis=hypothesis)