# Component Interfaces

This sections contains the base classes for the different components within the Stone Soup Framework.

## Enabling Components

Detector base class

A Detector processes SensorData to generate Detection data.

Parameters

sensor (SensorDataReader) – Source of sensor data

Source of sensor data

abstract detections_gen()

Returns a generator of detections for each time step.

Yields

Feeder base class

Feeder consumes and outputs State data and can be used to modify the sequence, duplicate or drop data.

Parameters

reader (Reader) – Source of detections

Source of detections

class stonesoup.metricgenerator.MetricGenerator[source]

Metric Generator base class

Generates Metric objects used to asses the performance of a tracker using data held in a MetricManager object

abstract compute_metric(manager, **kwargs)[source]

Compute metric

Parameters

manager (MetricManager) – containing the data to be used to create the metric(s)

Returns

Generated metrics

Return type

list of Metric objects

class stonesoup.tracker.Tracker[source]

Tracker base class

abstract tracks_gen()[source]

Returns a generator of tracks for each time step.

Yields

### Data Input

abstract detections_gen()[source]

Returns a generator of detections for each time step.

Yields

abstract groundtruth_paths_gen()[source]

Returns a generator of ground truth paths for each time step.

Yields

abstract sensor_data_gen()[source]

Returns a generator of sensor data for each time step.

Yields

### Data Output

class stonesoup.writer.MetricsWriter(metric_generator: MetricGenerator)[source]

Metrics Writer base class.

Writes out metrics to some form of storage for analysis.

Parameters

metric_generator (MetricGenerator) – Source of metric to be written out

metric_generator: stonesoup.metricgenerator.base.MetricGenerator

Source of metric to be written out

class stonesoup.writer.TrackWriter(tracker: Tracker)[source]

Track Writer base class.

Writes out tracks to some form of storage for analysis.

Parameters

tracker (Tracker) – Source of tracks to be written out

tracker: stonesoup.tracker.base.Tracker

Source of tracks to be written out

### Simulation

class stonesoup.simulator.DetectionSimulator[source]

Detection Simulator base class

abstract detections_gen()

Returns a generator of detections for each time step.

Yields
class stonesoup.simulator.GroundTruthSimulator[source]

Ground truth simulator

abstract groundtruth_paths_gen()

Returns a generator of ground truth paths for each time step.

Yields
class stonesoup.simulator.SensorSimulator[source]

Sensor Simulator base class

abstract sensor_data_gen()

Returns a generator of sensor data for each time step.

Yields
class stonesoup.platform.Platform(movement_controller: Movable = None, sensors: MutableSequence[Sensor] = None)[source]

A platform that can carry a number of different sensors.

The location of platform mounted sensors will be maintained relative to the sensor position. Platforms move within a 2 or 3 dimensional rectangular cartesian space.

A simple platform is considered to always be aligned with its principle velocity. It does not take into account issues such as bank angle or body deformation (e.g. flex).

Movement is controlled by the Platform’s Platform.movement_controller, and access to attributes of the Platform is proxied to the movement controller, to allow the Platform to report it’s own position, orientation etc.

If a movement_controller argument is not supplied to the constructor, the Platform will try to construct one using unused arguments passed to the Platform’s constructor.

Note

This class is abstract and not intended to be instantiated. To get the behaviour of this class use a subclass which gives movement behaviours. Currently these are FixedPlatform and MovingPlatform

Parameters
• movement_controller (Movable, optional) – Movable object to control the Platform’s movement. Default is None, but it can be constructed transparently by passing Movable’s constructor parameters to the Platform constructor.

• sensors (MutableSequence[Sensor], optional) – A list of N mounted sensors. Defaults to an empty list

movement_controller: stonesoup.movable.movable.Movable

Movable object to control the Platform’s movement. Default is None, but it can be constructed transparently by passing Movable’s constructor parameters to the Platform constructor.

sensors: MutableSequence[stonesoup.sensor.sensor.Sensor]

A list of N mounted sensors. Defaults to an empty list

Add a sensor to the platform

Parameters

sensor (BaseSensor) – The sensor object to add

remove_sensor(sensor: Sensor) None[source]

Remove a sensor from the platform

Parameters

sensor (BaseSensor) – The sensor object to remove

pop_sensor(index: int = - 1)[source]

Remove and return a sensor from the platform by index. If no index is specified, remove and return the last sensor in self.sensors

Parameters

index (int) – The index of the sensor to remove. Defaults to the last item in the list.

class stonesoup.sensor.sensor.Sensor(rotation_offset: StateVector = None, mounting_offset: StateVector = None, movement_controller: Movable = None)[source]

Sensor Base class for general use.

Most properties and methods are inherited from PlatformMountable.

Sensors must have a measure function.

Parameters
• rotation_offset (StateVector, optional) – A StateVector containing the sensor rotation offsets from the platform’s primary axis (defined as the direction of motion). Defaults to a zero vector with the same length as the Platform’s velocity_mapping

• mounting_offset (StateVector, optional) – A StateVector containing the sensor translation offsets from the platform’s reference point. Defaults to a zero vector with length 3

• movement_controller (Movable, optional) – The Movable object that controls the movement of this sensor. Will be set by the platform if the sensor is assigned to a platform.

abstract measure(ground_truths: Set[GroundTruthState], noise: Union[numpy.ndarray, bool] = True, **kwargs) [source]

Generate a measurement for a given state

Parameters
Returns

A set of measurements generated from the given states. The timestamps of the measurements are set equal to that of the corresponding states that they were calculated from. Each measurement stores the ground truth path that it was produced from.

Return type
mounting_offset: stonesoup.types.array.StateVector

A StateVector containing the sensor translation offsets from the platform’s reference point. Defaults to a zero vector with length 3

movement_controller: stonesoup.movable.movable.Movable

The Movable object that controls the movement of this sensor. Will be set by the platform if the sensor is assigned to a platform.

property orientation: Optional[stonesoup.types.array.StateVector]

A 3x1 StateVector of angles (rad), specifying the sensor orientation in terms of the counter-clockwise rotation around each Cartesian axis in the order $$x,y,z$$. The rotation angles are positive if the rotation is in the counter-clockwise direction when viewed by an observer looking along the respective rotation axis, towards the origin.

Note

This property delegates the actual calculation of orientation to the Sensor’s movement_controller

It is settable if, and only if, the sensor holds its own internal movement_controller.

property position: Optional[stonesoup.types.array.StateVector]

The sensor position on a 3D Cartesian plane, expressed as a 3x1 StateVector of Cartesian coordinates in the order $$x,y,z$$.

Note

This property delegates the actual calculation of position to the Sensor’s movement_controller

It is settable if, and only if, the sensor holds its own internal movement_controller.

rotation_offset: stonesoup.types.array.StateVector

A StateVector containing the sensor rotation offsets from the platform’s primary axis (defined as the direction of motion). Defaults to a zero vector with the same length as the Platform’s velocity_mapping

property velocity: Optional[stonesoup.types.array.StateVector]

The sensor velocity on a 3D Cartesian plane, expressed as a 3x1 StateVector of Cartesian coordinates in the order $$x,y,z$$.

Note

This property delegates the actual calculation of velocity to the Sensor’s movement_controller

It is settable if, and only if, the sensor holds its own internal movement_controller which is a MovingMovable.

## Algorithm Components

class stonesoup.dataassociator.DataAssociator(hypothesiser: Hypothesiser)[source]

Data Associator base class

A data associator is used to associate tracks and detections, and may also include an association of a missed detection. The associations generate are in the form a mapping each track to a hypothesis, based on “best” choice from hypotheses generate from a Hypothesiser.

Parameters

hypothesiser (Hypothesiser) – Generate a set of hypotheses for each track-detection pair

hypothesiser: stonesoup.hypothesiser.base.Hypothesiser

Generate a set of hypotheses for each track-detection pair

abstract associate(tracks: Set[Track], detections: Set[Detection], timestamp: datetime.datetime, **kwargs) [source]

Associate tracks and detections

Parameters
Returns

Mapping of track to Hypothesis

Return type

mapping of Track : Hypothesis

class stonesoup.deleter.Deleter(delete_last_pred: bool = False)[source]

Deleter base class.

Proposes tracks for deletion.

Parameters

delete_last_pred (bool, optional) – Remove the state that caused a track to be deleted if it is a prediction.

delete_last_pred: bool

Remove the state that caused a track to be deleted if it is a prediction.

abstract check_for_deletion(track: Track, **kwargs) bool[source]

Check if a given track should be deleted.

Parameters

track (Track) – A track object to be checked for deletion.

Returns

True if track should be deleted, False otherwise.

Return type

bool

delete_tracks(tracks: Set[Track], **kwargs) [source]

Generic/Base track deletion method.

Iterates through all tracks in a given list and calls check_for_deletion() to determine which tracks should be deleted and which should survive.

Parameters

tracks (set of Track) – A set of Track objects

Returns

Set of tracks proposed for deletion.

Return type

set of Track

class stonesoup.hypothesiser.Hypothesiser[source]

Hypothesiser base class

Given a track and set of detections, generate hypothesis of association.

hypothesise(track: Track, detections: Set[Detection], timestamp: datetime.datetime, **kwargs) Sequence[stonesoup.types.hypothesis.Hypothesis][source]

Hypothesise track and detection association

Parameters
• track (Track) – Track which hypotheses will be generated for.

• detections (set of Detection) – Detections used to generate hypotheses.

• timestamp (datetime.datetime) – A timestamp used when evaluating the state and measurement predictions. Note that if a given detection has a non empty timestamp, then prediction will be performed according to the timestamp of the detection.

Returns

Ordered sequence of “best” to “worse” hypothesis.

Return type

sequence of Hypothesis

class stonesoup.gater.Gater(hypothesiser: Union[Hypothesiser, Gater])[source]

Gater base class

Gaters wrap Hypothesiser objects and can be used to modify (typically reduce) the returned hypotheses.

Parameters

hypothesiser (Union[Hypothesiser, ForwardRef('Gater')]) – Hypothesiser or Gater that is being wrapped.

hypothesiser: Union[stonesoup.hypothesiser.base.Hypothesiser, stonesoup.gater.base.Gater]

Hypothesiser or Gater that is being wrapped.

class stonesoup.initiator.Initiator[source]

Initiator base class

Creates zero or more tracks based on provided detections.

abstract initiate(detections: Set[Detection], timestamp: datetime.datetime, **kwargs) [source]

Generate tracks from detections.

Parameters
Returns

Tracks generated from detections

Return type

set of Track

class stonesoup.mixturereducer.MixtureReducer[source]

Mixture Reducer base class

class stonesoup.predictor.Predictor(transition_model: TransitionModel, control_model: ControlModel = None)[source]

Predictor base class

A predictor is used to predict a new State given a prior State and a TransitionModel. In addition, a ControlModel may be used to model an external influence on the state.

$\mathbf{x}_{k|k-1} = f_k(\mathbf{x}_{k-1}, \mathbf{\nu}_k) + b_k(\mathbf{u}_k, \mathbf{\eta}_k)$

where $$\mathbf{x}_{k-1}$$ is the prior state, $$f_k(\mathbf{x}_{k-1})$$ is the transition function, $$\mathbf{u}_k$$ the control vector, $$b_k(\mathbf{u}_k)$$ the control input and $$\mathbf{\nu}_k$$ and $$\mathbf{\eta}_k$$ the transition and control model noise respectively.

Parameters
transition_model: stonesoup.models.transition.base.TransitionModel

transition model

control_model: stonesoup.models.control.base.ControlModel

control model

abstract predict(prior, timestamp=None, **kwargs)[source]

The prediction function itself

Parameters
Returns

State prediction

Return type

StatePrediction

class stonesoup.resampler.Resampler[source]

Resampler base class

class stonesoup.smoother.Smoother(transition_model: TransitionModel = None)[source]

Smoother Base Class

(Fixed interval) Smoothers in general are used to infer a state, or series of states, $$\mathbf{x}_k$$ from measurements $$\mathbf{z}_{1:K}$$ where $$k < K$$.

The calculation is forward-backward in nature. The forward algorithm is “standard” filtering, provided by other Stone Soup components. The Smoother’s input is therefore a Track (created by whatever means) The smooth() function undertakes the backward algorithm.

Parameters

transition_model (TransitionModel, optional) – Transition Model.

transition_model: stonesoup.models.transition.base.TransitionModel

Transition Model.

class stonesoup.updater.Updater(measurement_model: MeasurementModel)[source]

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 prediction

• measurement_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

MeasurementPrediction

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

State

### Models

class stonesoup.models.control.ControlModel(ndim_state: int, mapping: Sequence[int])[source]

Control Model base class

Parameters
• ndim_state (int) – Number of state dimensions

• mapping (Sequence[int]) – Mapping between control and state dims

ndim_state: int

Number of state dimensions

mapping: Sequence[int]

Mapping between control and state dims

property ndim: int

Number of dimensions of model

abstract property ndim_ctrl: int

Number of control input dimensions

abstract function(state: State, noise: Union[bool, numpy.ndarray] = False, **kwargs)

Model function $$f_k(x(k),w(k))$$

Parameters
Returns

The StateVector(s) with the model function evaluated.

Return type

StateVector or StateVectors

abstract pdf(state1: State, state2: State, **kwargs)

Model pdf/likelihood evaluation function

Evaluates the pdf/likelihood of state1, given the state state2 which is passed to function().

Parameters
Returns

The likelihood of state1, given state2

Return type
abstract rvs(num_samples: int = 1, **kwargs)

Model noise/sample generation function

Generates noise samples from the model.

Parameters

num_samples (scalar, optional) – The number of samples to be generated (the default is 1)

Returns

noise – A set of Np samples, generated from the model’s noise distribution.

Return type

2-D array of shape (ndim, num_samples)

class stonesoup.models.measurement.MeasurementModel(ndim_state: int, mapping: Sequence[int])[source]

Measurement Model base class

Parameters
• ndim_state (int) – Number of state dimensions

• mapping (Sequence[int]) – Mapping between measurement and state dims

ndim_state: int

Number of state dimensions

mapping: Sequence[int]

Mapping between measurement and state dims

property ndim: int

Number of dimensions of model

abstract property ndim_meas: int

Number of measurement dimensions

abstract function(state: State, noise: Union[bool, numpy.ndarray] = False, **kwargs)

Model function $$f_k(x(k),w(k))$$

Parameters
Returns

The StateVector(s) with the model function evaluated.

Return type

StateVector or StateVectors

abstract pdf(state1: State, state2: State, **kwargs)

Model pdf/likelihood evaluation function

Evaluates the pdf/likelihood of state1, given the state state2 which is passed to function().

Parameters
Returns

The likelihood of state1, given state2

Return type
abstract rvs(num_samples: int = 1, **kwargs)

Model noise/sample generation function

Generates noise samples from the model.

Parameters

num_samples (scalar, optional) – The number of samples to be generated (the default is 1)

Returns

noise – A set of Np samples, generated from the model’s noise distribution.

Return type

2-D array of shape (ndim, num_samples)

class stonesoup.models.transition.TransitionModel[source]

Transition Model base class

property ndim: int

Number of dimensions of model

abstract property ndim_state: int

Number of state dimensions

abstract function(state: State, noise: Union[bool, numpy.ndarray] = False, **kwargs)

Model function $$f_k(x(k),w(k))$$

Parameters
Returns

The StateVector(s) with the model function evaluated.

Return type

StateVector or StateVectors

abstract pdf(state1: State, state2: State, **kwargs)

Model pdf/likelihood evaluation function

Evaluates the pdf/likelihood of state1, given the state state2 which is passed to function().

Parameters
Returns

The likelihood of state1, given state2

Return type
abstract rvs(num_samples: int = 1, **kwargs)

Model noise/sample generation function

Generates noise samples from the model.

Parameters

num_samples (scalar, optional) – The number of samples to be generated (the default is 1)

Returns

noise – A set of Np samples, generated from the model’s noise distribution.

Return type

2-D array of shape (ndim, num_samples)