Detection Probability Evaluator

class DetectionProbEvaluator(radar, confidence=1.0, tolerance=0.0, min_num_samples=1024, plot_scale='log', tick_format=ValueType.LIN, plot_surface=True)[source]

Bases: ScalarEvaluator, Serializable

Estimates the probability of detection for a given radar detector.

Assumes a successful detection if the Radar's RadarReception contains a non-empty point cloud. This is the case if the configured RadarDetector made a positive decision for any bin within the processed RadarCube.

A minimal example within the context of a Simulation evaluating the probability of detection for a single radar target illuminated by an FMCW radar would be:

 1from hermespy.radar import Radar, FMCW,  ThresholdDetector, DetectionProbEvaluator
 2from hermespy.simulation import Simulation
 3from hermespy.channel import SingleTargetRadarChannel
 4
 5# Create a new simulated scenario featuring a single device
 6simulation = Simulation()
 7device = simulation.new_device(carrier_frequency=60e9)
 8
 9# Configure the device to transmit and reveive radar waveforms
10radar = Radar(waveform=FMCW())
11radar.detector = ThresholdDetector(.02, normalize=True)
12device.add_dsp(radar)
13
14# Create a new radar channel with a single illuminated target
15target = SingleTargetRadarChannel(1, 1., attenuate=True)
16simulation.scenario.set_channel(device, device, target)
17
18# Create a new detection probability evaluator
19simulation.add_evaluator(DetectionProbEvaluator(radar))
20
21# Sweep over the target's RCS during the simulation
22simulation.new_dimension('radar_cross_section', [1, .8, .6, .4, .2, .1, 0], target)
23
24# Run the simulation
25result = simulation.run()
Parameters:

  • radar (Radar) – Radar detector to be evaluated.

  • confidence (float) – Required confidence level for the given tolerance between zero and one.

  • tolerance (float) – Acceptable non-negative bound around the mean value of the estimated scalar performance indicator.

  • min_num_samples (int) – Minimum number of samples required to compute the confidence bound.

  • plot_scale (str) – Scale of the plot. Can be 'linear' or 'log'.

  • tick_format (ValueType) – Tick format of the plot.

  • plot_surface (bool) – Enable surface plotting for two-dimensional grids. Enabled by default.

classmethod Deserialize(process)[source]

Deserialize an object’s state.

Objects cannot be deserialized directly, instead a Factory must be instructed to carry out the deserialization process.

Parameters:

process (DeserializationProcess) – The current stage of the deserialization process. This object is generated by the Factory and provides an interface to deserialization methods supporting multiple backends.

Return type:

DetectionProbEvaluator

Returns:

The deserialized object.

evaluate()[source]

Evaluate the state of an investigated object.

Implements the process of extracting an arbitrary performance indicator, represented by the returned Artifact \(X_m\).

Returns: Artifact \(X_m\) resulting from the evaluation.

Return type:

DetectionProbabilityEvaluation

serialize(process)[source]

Serialize this object’s state.

Objects cannot be serialized directly, instead a Factory must be instructed to carry out the serialization process.

Parameters:

process (SerializationProcess) – The current stage of the serialization process. This object is generated by the Factory and provides an interface to serialization methods supporting multiple backends.

Return type:

None

property abbreviation: str[source]

Short string representation of this evaluator.

Used as a label for console output and plot axes annotations.

property radar: Radar[source]

Radar detector to be evaluated.

property title: str[source]

Long string representation of this evaluator.

Used as plot title.

class DetectionProbabilityEvaluation(evaluation)[source]

Bases: EvaluationTemplate[bool, ScatterVisualization]

Evaluation of the probability of detection for a radar detector.

Represents a boolean indicator of whether a target was detected or not. Generated by the DetectionProbEvaluator’s evaluate() method.

Parameters:

evaluation (TypeVar(ET, bound= object)) – The represented evaluation.

artifact()[source]

Generate an artifact from this evaluation.

Returns: The evaluation artifact.

Return type:

DetectionProbArtifact

class DetectionProbArtifact(artifact)[source]

Bases: ArtifactTemplate[bool]

Artifacto of the probability of detection for a radar detector.

Represents a boolean indicator of whether a target was detected or not. Generated by the DetectionProbabilityEvaluation’s artifact() method.

Parameters:

artifact (TypeVar(FAT, bound= SupportsFloat)) – Artifact value.