Google Glass real-time visualization with tactical overlays

The purpose of RF-QUANTUM-SCYTHE is to provide comprehensive real-time situational awareness for tactical operations. This is achieved through the complete integration and demonstration of several key capabilities:

• DOMA RF Motion Model for signal tracking and prediction.
• Google Glass real-time visualization with tactical overlays.
• Casualty detection and medical triage visualization.
• RF signal intelligence with threat assessment.

The demonstration script itself represents the culmination of the RF-QUANTUM-SCYTHE project development, showcasing these integrated functionalities. When the demo starts, it highlights its core functionalities as “Real-time RF Tracking • Casualty Detection • Tactical Visualization”.

The RF-QUANTUM-SCYTHE Google Glass Demo demonstrates the DOMA RF Motion Model for signal tracking and prediction through its core integration into the system and its configured use within the demonstration scenarios.

Here’s how it’s demonstrated and its role:

• Core Functionality: The DOMA RF Motion Model is explicitly stated as being used for signal tracking and prediction. It is part of the SignalIntelligence module, specifically within the DOMASignalTracker component.
• Integration with Glass Visualization: The demo showcases the “DOMA-Glass Integration” module, which is responsible for integrating the DOMA model’s capabilities with the Google Glass visualization. If this module is available, a DOMAGlassIntegrator is initialized, and it attempts to initialize its systems with the communication network.
• Demo Configuration: The _create_demo_config method defines specific settings for the doma_motion_tracker within the signal_intelligence configuration. These settings include:
  • "enabled": True
  • "model_path": Specifies the path to the primary DOMA RF motion model (/home/gorelock/gemma/NerfEngine/doma_rf_motion_model.pth).
  • "enhanced_model_path": Points to an enhanced version of the model (/home/gorelock/gemma/NerfEngine/enhanced_doma_rf_motion_model.pth).
  • "prediction_horizon_seconds": 30: Indicates that the model is configured to predict movements up to 30 seconds into the future.
  • "min_signal_strength": -90: Sets a minimum signal strength threshold for tracking.
  • "track_timeout_seconds": 60: Defines how long a track should be maintained without updates.
• Display of Predictions: The doma_glass_integration configuration further specifies how the model’s output is visualized on the Google Glass:
  • "prediction_horizon_seconds": 30: Reconfirms the prediction horizon for display.
  • "max_tracks_displayed": 15: Limits the number of tracks shown simultaneously.
  • "display_prediction_path": True: Crucially, this setting ensures that the predicted path generated by the DOMA RF Motion Model is visualized on the Glass display.
• Processing RF Signals: When the demo processes an rf_signal element, it publishes the detected signal data to the communication network. Additionally, if the glass_integrator (which uses DOMA) is available, it calls its internal _handle_rf_signal method, implying that the DOMA model processes this signal data to generate tracking and prediction information.
• Active RF Tracks in Status: The system status monitoring includes reporting the number of “Active RF Tracks,” which are managed by the glass_integrator. This indicates that the DOMA model’s tracking results are actively maintained and displayed during the demo.

The RF-QUANTUM-SCYTHE Google Glass Demo comprehensively demonstrates the DOMA RF Motion Model for signal tracking and prediction through several integrated functionalities and configurations:

• Core Integration and Availability: The demo explicitly loads and integrates the SignalIntelligence module, which contains the DOMASignalTracker, as well as the DOMA-Glass Integration module, which includes the DOMAGlassIntegrator. The system logs whether these modules are successfully loaded, indicating their foundational role in the demonstration. The DOMAGlassIntegrator is initialized with specific configurations upon system startup and attempts to initialize its internal systems with the communication network.
• Specific Configuration for Tracking and Prediction: The demonstration’s configuration (_create_demo_config) defines detailed settings for the doma_motion_tracker.
  • It is explicitly "enabled": True.
  • It specifies the paths to the primary (doma_rf_motion_model.pth) and an enhanced (enhanced_doma_rf_motion_model.pth) DOMA RF motion models, indicating that these are the AI components responsible for the motion modeling.
  • A "prediction_horizon_seconds" of 30 seconds is configured, which directly showcases the model’s ability to forecast movements into the near future. This parameter is also present in the doma_glass_integration settings, reinforcing its importance for visualization.
  • Parameters like "min_signal_strength": -90 and "track_timeout_seconds": 60 define the operational thresholds for when the DOMA model engages in tracking and how long it maintains a track without new data, demonstrating its practical application in a dynamic environment.
• Real-time Processing of RF Signals: During the execution of demo scenarios, when an rf_signal element is processed (_process_rf_signal_element), the raw signal data (including signal_id, frequency, rssi, and position) is published to the communication network. Crucially, if the glass_integrator (which incorporates DOMA) is available, it receives and processes this signal data via its internal _handle_rf_signal method. This direct interaction demonstrates how incoming RF data feeds into the DOMA model for tracking and prediction.
• Visualization of Predicted Paths: The doma_glass_integration configuration includes "display_prediction_path": True. This setting ensures that the predicted paths generated by the DOMA RF Motion Model are visualized on the Google Glass display, making the model’s predictive capability tangible to the user. When an rf_track is added to the GlassDisplayManager, it includes a field for "predicted_path" which is explicitly noted as being “Will be filled by DOMA”. This highlights that the DOMA model’s output is directly consumed for display purposes.
• Active Tracking Status: The system’s status monitoring, which prints updates every 15 seconds, reports the number of “Active RF Tracks”. These active tracks are managed by the glass_integrator, providing a real-time numerical indication of the DOMA model’s ongoing tracking activities.
• Demonstration Scenarios: The demo utilizes predefined scenarios, such as “Combat Search and Rescue (CSAR)” and “Urban Threat Detection,” which include the generation of simulated rf_signal elements at specific delays and positions. As these signals are introduced, the DOMA model processes them, generates tracks, and predicts movements, which would then be visible on the Google Glass display, directly fulfilling the system’s objective of providing “real-time situational awareness for tactical operations”.
• Starting and Stopping Tracking: The start_demo method explicitly calls self.glass_integrator.start_tracking() to begin the DOMA model’s tracking operations, and stop_demo calls self.glass_integrator.stop_tracking() to cease them, illustrating lifecycle management of the DOMA component within the demo.

One of the demonstration scenarios in RF-QUANTUM-SCYTHE is the “Combat Search and Rescue (CSAR)” scenario.

This scenario is designed to simulate a “Downed pilot scenario with multiple RF threats”. It has a duration of 60 seconds.

The elements within this scenario are introduced at specific delays to simulate real-time events:

• Casualty Detection:
  • After a 2-second delay, a casualty element is introduced.
  • This represents a “pilot_down_001” located at latitude 38.8977 and longitude -77.0365.
  • The type of casualty is “trauma_detected”, with a severity of 4 (out of 5), and a high confidence of 0.92.
  • The source of this detection is an “emergency_beacon”.
  • Vitals like a heart_rate of “110 bpm” and blood_pressure of “90/60” are also provided. This demonstrates the system’s capability for casualty detection and medical triage visualization.
• RF Signal Tracking and Threat Assessment:
  • After a 5-second delay, an rf_signal element for “enemy_radar_001” is introduced.
  • This signal has a frequency of 3.5 GHz, an rssi of -55 dBm, and is positioned at latitude 38.9000, longitude -77.0300, and an altitude of 200.
  • Its metadata explicitly marks it as a “surveillance_radar” and a threat: True. This demonstrates the system’s RF signal intelligence with threat assessment capabilities, leveraging the DOMA RF Motion Model for tracking such signals.
  • After an 8-second delay, another rf_signal element, “friendly_drone_001”, is detected.
  • This friendly signal operates at 2.4 GHz, has an rssi of -45 dBm, and is located at latitude 38.8985, longitude -77.0355, and an altitude of 150.
  • Its metadata identifies it as a “rescue_drone” and friendly: True.

During this scenario, the RF-QUANTUM-SCYTHE system would process these elements, publishing the rf_signal data to the communication network and, if available, handling them through the DOMAGlassIntegrator for tracking and prediction. The casualty data would be added to the GlassDisplayManager and published to the communication network. This allows the demonstration to showcase Google Glass real-time visualization with tactical overlays and contribute to real-time situational awareness for tactical operations.

RF-QUANTUM-SCYTHE integrates multiple system components to achieve its purpose of providing real-time situational awareness for tactical operations.

Three integrated system components are:

• DOMA RF Motion Model (part of Signal Intelligence): This component is explicitly loaded as the SignalIntelligence module, which includes the DOMASignalTracker. It is configured to be enabled, with specified model paths for primary and enhanced versions, and is used for signal tracking and prediction. The system can predict movements up to 30 seconds into the future.
• Google Glass Real-time Visualization (including Glass Display Interface and DOMA-Glass Integration): This encompasses the GlassVisualization module, the GlassDisplayManager, and the DOMAGlassIntegrator. It is responsible for real-time visualization with tactical overlays. The configuration allows for displaying predicted paths generated by DOMA and managing various display elements.
• Communication Network: This foundational component, represented by the CommunicationNetwork module (or a MockCommunicationNetwork for demonstration), handles internal messaging between different parts of the system. It enables modules to publish and subscribe to various channels, facilitating the flow of data like detected RF signals and casualty reports.

Based on the provided sources and our conversation history, the available information does not explicitly identify specific hardware sensors available on Google Glass.

Instead, the sources describe Google Glass’s role within the RF-QUANTUM-SCYTHE system as a platform for real-time visualization with tactical overlays. It functions as a display device, managed by the GlassDisplayManager, which handles elements like RF tracks and casualty alerts.

The system demonstrates the processing of various types of data that would be displayed on Google Glass, but the origin of this data is typically external to the Glass itself:

• RF signals are detected (e.g., “enemy_radar_001”, “friendly_drone_001”, “hostile_jammer_001”, “hostile_aircraft_001”) and processed by the DOMA RF Motion Model (part of the SignalIntelligence module) for tracking and prediction. The sources do not indicate that Google Glass itself possesses RF detection capabilities; rather, it receives this processed data for visualization via the DOMA-Glass Integration module.
• Casualty data is identified from sources such as an “emergency_beacon,” “rf_biomarker,” or “smartphone_rf_sensor”. This data is then displayed on the Glass, demonstrating “Casualty detection and medical triage visualization”.

The configuration for Google Glass in the demo mentions features like “audio_alerts” and “haptic_feedback”, which are output mechanisms for user notification, rather than data collection sensors.

In summary, the sources depict Google Glass as a crucial component for displaying and interacting with processed information, but they do not detail any inherent sensors it possesses for environmental data collection.