Source code for stonesoup.models.measurement.linear

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

import numpy as np

from ...base import Property
from ...types.array import CovarianceMatrix
from ..base import LinearModel, GaussianModel
from .base import MeasurementModel

# TODO: Probably should call this LinearGaussianMeasurementModel
[docs]class LinearGaussian(MeasurementModel, LinearModel, GaussianModel): r"""This is a class implementation of a time-invariant 1D Linear-Gaussian Measurement Model. The model is described by the following equations: .. math:: y_t = H_k*x_t + v_k,\ \ \ \ v(k)\sim \mathcal{N}(0,R) where ``H_k`` is a (:py:attr:`~ndim_meas`, :py:attr:`~ndim_state`) \ matrix and ``v_k`` is Gaussian distributed. """ noise_covar: CovarianceMatrix = Property(doc="Noise covariance") @property def ndim_meas(self): """ndim_meas getter method Returns ------- :class:`int` The number of measurement dimensions """ return len(self.mapping)
[docs] def matrix(self, **kwargs): """Model matrix :math:`H(t)` Returns ------- :class:`numpy.ndarray` of shape \ (:py:attr:`~ndim_meas`, :py:attr:`~ndim_state`) The model matrix evaluated given the provided time interval. """ model_matrix = np.zeros((self.ndim_meas, self.ndim_state)) for dim_meas, dim_state in enumerate(self.mapping): if dim_state is not None: model_matrix[dim_meas, dim_state] = 1 return model_matrix
[docs] def function(self, state, noise=False, **kwargs): """Model function :math:`h(t,x(t),w(t))` Parameters ---------- state: :class:`~.State` An input state noise: :class:`numpy.ndarray` or bool An externally generated random process noise sample (the default is `False`, in which case no noise will be added if 'True', the output of :meth:`~.Model.rvs` is added) Returns ------- :class:`numpy.ndarray` of shape (:py:attr:`~ndim_meas`, 1) The model function evaluated given the provided time interval. """ if isinstance(noise, bool) or noise is None: if noise: noise = self.rvs(num_samples=state.state_vector.shape[1], **kwargs) else: noise = 0 return self.matrix(**kwargs)@state.state_vector + noise
[docs] def covar(self, **kwargs): """Returns the measurement model noise covariance matrix. Returns ------- :class:`~.CovarianceMatrix` of shape\ (:py:attr:`~ndim_meas`, :py:attr:`~ndim_meas`) The measurement noise covariance. """ return self.noise_covar