Source code for stonesoup.models.clutter.clutter

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

import numpy as np
from scipy.stats import poisson
from typing import Set, Union, Callable, Tuple, Optional
from abc import ABC

from ..base import Model
from ...base import Property
from ...types.detection import Clutter
from ...types.groundtruth import GroundTruthState
from ...types.array import StateVector, StateVectors
from ...types.numeric import Probability
from ...types.state import State

[docs]class ClutterModel(Model, ABC): """A model for generating sensor clutter (false alarms) according to a specified distribution in the state space relative to the sensor's position. Note ---- Instances of this class do not hold information about the measurement space until immediately before they are called to function. As such, the same :class:`ClutterModel` object could be used with multiple different :class:`~.MeasurementModel` as long as they operate in the same state space. """ clutter_rate: float = Property( default=1.0, doc="The average number of clutter points per time step. The actual " "number is Poisson distributed") distribution: Callable = Property( default=np.random.default_rng().uniform, doc="A function which represents the distribution of the clutter over the " "measurement space. The function should return a single value (ie, do " "not use multivariate distributions).") dist_params: Tuple = Property( default=((-200, 200), (-200, 200)), doc="The required parameters for the clutter distribution function. The " "length of the list must be equal to the number of state dimensions " "and should be defined for use in Cartesian space." "The default defines the space for a uniform distribution in 2D. The call " "`np.array([self.distribution(*arg) for arg in self.dist_params])` " "must return a numpy array of length equal to the number of dimensions.") seed: Optional[Union[int, np.random.RandomState]] = Property( default=None, doc="Seed or state for random number generation. If defined as an integer, " "it will be used to create a numpy RandomState. Or it can be defined directly " "as a RandomState (useful if you want to pass one of the random state's " "functions as the :attr:`distribution`).") def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if isinstance(self.seed, int): self.random_state = np.random.RandomState(self.seed) elif isinstance(self.seed, np.random.RandomState): self.random_state = self.seed else: self.random_state = None
[docs] def function(self, ground_truths: Set[GroundTruthState], **kwargs) -> Set[Clutter]: """ Use the defined distribution and parameters to create simulated clutter for the current time step. Return this clutter to the calling sensor so that it can be added to the measurements. Parameters ---------- ground_truths : a set of :class:`~.GroundTruthState` The truth states which exist at this time step. Returns ------- : set of :class:`~.Clutter` The simulated clutter. """ # Extract the timestamp from the ground_truths. Groundtruth is # necessary to get the proper timestamp. If there is no # groundtruth return a set of no Clutter. if not ground_truths: return set() timestamp = next(iter(ground_truths)).timestamp # Generate the clutter for this time step clutter = set() for _ in range(poisson.rvs(self.clutter_rate, random_state=self.random_state)): # Call the distribution function to generate a random vector in the space random_vector = np.array([self.distribution(*arg) for arg in self.dist_params]) # Make a State object with the random vector state = State([0.0] * self.measurement_model.ndim_state, timestamp=timestamp) state.state_vector[self.measurement_model.mapping, 0] += random_vector # Use the sensor's measurement model to incorporate the # translation offset and sensor rotation. This will also # convert the vector to the proper measurement space # (polar or spherical coordinates) clutter_vector = self.measurement_model.function(state) # Create a clutter object. clutter.add(Clutter(state_vector=clutter_vector, timestamp=timestamp, measurement_model=self.measurement_model)) return clutter
@property def ndim(self) -> int: """ Return the number of measurement dimensions or, if a measurement model has not yet been assigned, the number of state dimensions. """ if hasattr(self, 'measurement_model'): return self.measurement_model.ndim_meas else: return len(self.dist_params)
[docs] def rvs(self, num_samples: int = 1, **kwargs) -> Union[StateVector, StateVectors]: """ Must be implemented to properly inherit the parent Model. """ return None
[docs] def pdf(self, state1: State, state2: State, **kwargs) -> Union[Probability, np.ndarray]: """ Must be implemented to properly inherit the parent Model. """ return None