SCYTHE Google Glass Tactical Visualization

PODCAST: explore the RF-QUANTUM-SCYTHE project, focusing on its integration with Google Glass for tactical operations. This system combines a DOMA RF Motion Model for signal tracking, real-time visualization on Google Glass with tactical overlays, and casualty detection for medical triage. It also incorporates RF signal intelligence for threat assessment and aims to provide real-time situational awareness. The script simulates various scenarios, including combat search and rescue and urban threat detection, showcasing how the system processes and displays information about RF signals and detected casualties to enhance operational understanding.

RF-QUANTUM-SCYTHE: Combat Search and Rescue Simulation

One of the demonstration scenarios in RF-QUANTUM-SCYTHE is the “Combat Search and Rescue (CSAR)” scenario [1].

This scenario is designed to simulate a “Downed pilot scenario with multiple RF threats” [1]. It has a duration of 60 seconds [1].

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 [2].

    ◦ This represents a “pilot_down_001” located at latitude 38.8977 and longitude -77.0365 [2].

    ◦ The type of casualty is “trauma_detected”, with a severity of 4 (out of 5), and a high confidence of 0.92 [2].

    ◦ The source of this detection is an “emergency_beacon” [2].

    ◦ Vitals like a heart_rate of “110 bpm” and blood_pressure of “90/60” are also provided [2]. This demonstrates the system’s capability for casualty detection and medical triage visualization [3].

• RF Signal Tracking and Threat Assessment:

    ◦ After a 5-second delay, an rf_signal element for “enemy_radar_001” is introduced [2].

    ◦ 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 [2].

    ◦ Its metadata explicitly marks it as a “surveillance_radar” and a threat: True [2]. This demonstrates the system’s RF signal intelligence with threat assessment capabilities, leveraging the DOMA RF Motion Model for tracking such signals [3].

    ◦ After an 8-second delay, another rf_signal element, “friendly_drone_001”, is detected [2].

    ◦ 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 [2].

    ◦ Its metadata identifies it as a “rescue_drone” and friendly: True [2].

During this scenario, the RF-QUANTUM-SCYTHE system would process these elements, publishing the rf_signal data to the communication network [4] and, if available, handling them through the DOMAGlassIntegrator for tracking and prediction [4]. The casualty data would be added to the GlassDisplayManager [5] and published to the communication network [5]. This allows the demonstration to showcase Google Glass real-time visualization with tactical overlays [3] and contribute to real-time situational awareness for tactical operations [3, 6].

RF-QUANTUM-SCYTHE: DOMA Motion Model in Google Glass

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 [1].

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 [1]. It is part of the SignalIntelligence module, specifically within the DOMASignalTracker component [2].

• 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 [3]. If this module is available, a DOMAGlassIntegrator is initialized, and it attempts to initialize its systems with the communication network [4].

• Demo Configuration: The _create_demo_config method defines specific settings for the doma_motion_tracker within the signal_intelligence configuration [5]. These settings include:

    ◦ "enabled": True [5]

    ◦ "model_path": Specifies the path to the primary DOMA RF motion model (/home/gorelock/gemma/NerfEngine/doma_rf_motion_model.pth) [5].

    ◦ "enhanced_model_path": Points to an enhanced version of the model (/home/gorelock/gemma/NerfEngine/enhanced_doma_rf_motion_model.pth) [6].

    ◦ "prediction_horizon_seconds": 30: Indicates that the model is configured to predict movements up to 30 seconds into the future [6].

    ◦ "min_signal_strength": -90: Sets a minimum signal strength threshold for tracking [6].

    ◦ "track_timeout_seconds": 60: Defines how long a track should be maintained without updates [6].

• 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 [6].

    ◦ "max_tracks_displayed": 15: Limits the number of tracks shown simultaneously [6].

    ◦ “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 [6].

• 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 [7].

• Active RF Tracks in Status: The system status monitoring includes reporting the number of “Active RF Tracks,” which are managed by the glass_integrator [8, 9]. This indicates that the DOMA model’s tracking results are actively maintained and displayed during the demo.

RF-QUANTUM-SCYTHE: System Components and Real-time Awareness

RF-QUANTUM-SCYTHE integrates multiple system components to achieve its purpose of providing real-time situational awareness for tactical operations [1].

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 [2]. It is configured to be enabled, with specified model paths for primary and enhanced versions, and is used for signal tracking and prediction [1, 3]. The system can predict movements up to 30 seconds into the future [4].

• Google Glass Real-time Visualization (including Glass Display Interface and DOMA-Glass Integration): This encompasses the GlassVisualization module [2], the GlassDisplayManager [5, 6], and the DOMAGlassIntegrator [7, 8]. It is responsible for real-time visualization with tactical overlays [1]. The configuration allows for displaying predicted paths generated by DOMA and managing various display elements [4].

• Communication Network: This foundational component, represented by the CommunicationNetwork module (or a MockCommunicationNetwork for demonstration), handles internal messaging between different parts of the system [5, 6, 9]. It enables modules to publish and subscribe to various channels, facilitating the flow of data like detected RF signals and casualty reports [10, 11].

The RF-QUANTUM-SCYTHE system is explicitly designed to provide real-time situational awareness for tactical operations. This is achieved through the comprehensive integration of several key system components that work together to track, visualize, and disseminate critical information.

Here’s how each component contributes to real-time situational awareness:

• DOMA RF Motion Model (part of Signal Intelligence):
  • Signal Tracking and Prediction: This model is a core element for signal tracking and prediction. It is incorporated within the SignalIntelligence module as the DOMASignalTracker.
  • Anticipatory Awareness: The model is configured to predict movements up to 30 seconds into the future. This predictive capability is crucial for enhancing situational awareness by allowing operators to anticipate potential threats or movements before they occur.
  • RF Signal Processing: When RF signals are detected, the DOMA-Glass Integrator processes this data, implying the DOMA model generates tracking and prediction information from these signals.
  • Configurable Tracking: The system allows for detailed configuration of the DOMA RF Motion Model, including enabling it, specifying paths for primary and enhanced models, setting a minimum signal strength threshold of -90 dBm for tracking, and defining a track timeout of 60 seconds. The status monitoring also reports the number of “Active RF Tracks” managed by the DOMA-Glass integrator, indicating ongoing tracking.
• Google Glass Real-time Visualization (including Glass Display Interface and DOMA-Glass Integration):
  • Tactical Overlays: This component is responsible for providing real-time visualization with tactical overlays directly on Google Glass. It includes the GlassVisualization module, the GlassDisplayManager, and the DOMAGlassIntegrator.
  • Casualty and RF Signal Visualization: It facilitates casualty detection and medical triage visualization, displaying critical details such as severity, type, location, and confidence levels for casualties. It also presents RF signal intelligence with threat assessment, including visualizing hostile and friendly signals, their frequencies, strengths, and assessed threat levels through various display elements.
  • Predicted Path Display: A significant feature for situational awareness is the setting to display the predicted path generated by the DOMA RF Motion Model directly on the Glass display. This visual representation helps users understand future movements of tracked entities.
  • Alerts and Feedback: The visualization system can provide audio alerts and haptic feedback for critical events, such as casualty alerts or high threat detections, ensuring operators are immediately notified of urgent situations. The GlassDisplayManager is utilized to add casualty, RF track, and alert data for display.
• Communication Network:
  • Data Flow and Distribution: This foundational component, represented by the CommunicationNetwork module (or a MockCommunicationNetwork for demonstration), handles internal messaging between different parts of the system.
  • Inter-component Communication: It enables various modules to publish and subscribe to different channels, facilitating a seamless flow of crucial data such as detected RF signals and casualty reports. For instance, casualty data is published to the “casualty_detected” channel, and RF signal data to “rf_signal_detected”.
  • Integrated Awareness: This continuous and rapid exchange of information ensures that all relevant system components have access to the most current data, which is essential for maintaining a comprehensive and up-to-date real-time situational awareness picture.

The combined functionality of these integrated components ensures that operators receive timely, relevant, and visually intuitive information on RF signals, potential threats, and casualty status, thereby greatly enhancing their real-time situational awareness for tactical operations.

The import sys statement is utilized in the RF-QUANTUM-SCYTHE Google Glass Demo script to modify the Python system path.

Specifically, after import sys, the script uses sys.path.append() twice:

• sys.path.append('/home/gorelock/gemma/NerfEngine')
• sys.path.append('/home/gorelock/gemma/NerfEngine/RF_QUANTUM_SCYTHE')

These lines add the /home/gorelock/gemma/NerfEngine and /home/gorelock/gemma/NerfEngine/RF_QUANTUM_SCYTHE directories to the list of paths where Python looks for modules. This ensures that the various modules of the RF-QUANTUM-SCYTHE system, such as SignalIntelligence.core, GlassVisualization.core, GlassVisualization.doma_glass_integration, GlassVisualization.glass_display_interface, and CommunicationNetwork.core, can be correctly imported, even if they are not in the default Python search paths. This setup is crucial for the comprehensive integration of all these components, which collectively contribute to real-time situational awareness for tactical operations.

In the RF-QUANTUM-SCYTHE Google Glass Demo script, the import os, import time, and import logging statements are used for specific functionalities that support the comprehensive demonstration of the system’s capabilities.

Here’s how each is utilized:

• import os:
  • The os module is imported at the beginning of the script.
  • However, based solely on the provided excerpts, there is no explicit direct usage of the os module demonstrated within the available code. While it’s imported, its specific functions (e.g., for interacting with the operating system, file paths, environment variables) are not shown in use.
• import time:
  • The time module is imported at the beginning of the script.
  • It is extensively used throughout the demo for managing the flow and timing of events and operations:
    • Timestamping Messages: When data is published to the communication network, the time.time() function is used to add a timestamp to messages in the MockCommunicationNetwork‘s message queue. Similarly, casualty data prepared for display also includes a timestamp using time.time().
    • Controlling Scenario Execution and Delays:
      • It is used to introduce pauses or delays between different demo scenarios.
      • Within a scenario, time.sleep() is used to wait for specific delays before processing scenario elements (e.g., before introducing a casualty or an RF signal).
      • time.time() is used to track the start_time of a scenario and calculate the elapsed time to ensure elements are introduced at their designated delay. It also helps ensure scenarios run for their full duration by calculating and sleeping for any remaining_time.
    • Scheduling Status Updates: In the _start_status_monitoring function, time.sleep(15) is used to periodically update the system status every 15 seconds, providing a real-time overview of active elements and RF tracks.
    • Maintaining the Main Loop: In the main function, time.sleep(1) is used within a while loop to keep the demo running until it is explicitly stopped or interrupted.
• import logging:
  • The logging module is imported at the beginning of the script.
  • It is crucial for configuring and managing the logging system for the entire RF-QUANTUM-SCYTHE demonstration.
  • The logging.basicConfig call sets up the logging level to INFO and defines the format of the log messages, including timestamp, logger name, level, and message.
  • A logger instance is then created using logging.getLogger(__name__).
  • This logger is used throughout the script to provide informational messages, warnings for potential issues (e.g., module import failures or failed initializations), and error messages when exceptions occur. This systematic logging is vital for understanding the demo’s operation, troubleshooting, and monitoring its real-time performance in showcasing situational awareness capabilities.

The import os, import time, and import logging statements are used for specific functionalities that support the comprehensive demonstration of the system’s capabilities.

The imports from typing import Dict, List, Any, Optional, Tuple, import json, import threading, and import signal are utilized in the RF-QUANTUM-SCYTHE Google Glass Demo script for the following purposes:

• from typing import Dict, List, Any, Optional, Tuple:
  • These are used for type hinting, enhancing code readability and maintainability by explicitly declaring the expected types of variables, function parameters, and return values.
  • Dict is used extensively, for example, in the return types of _create_demo_config and _run_scenario, as well as for function parameters like signal_data: Dict[str, Any].
  • List is used, for instance, in the return type of _create_demo_scenarios.
  • Any is used when a type can be anything, such as in the data parameter of MockCommunicationNetwork.publish(self, channel: str, data: Any).
  • Tuple is used to define the return type for color functions, specifically Tuple[int, int, int] for RGB color values, as seen in _get_severity_color and _get_threat_color.
  • While Optional is imported, its explicit usage in the provided code excerpts for type hints is not shown.
• import json:
  • The json module is imported at the beginning of the script.
  • However, based solely on the provided excerpts, there is no explicit direct usage of the json module demonstrated within the available code. This means no functions like json.dumps() or json.loads() are shown in use.
• import threading:
  • The threading module is crucial for managing concurrent operations within the demo.
  • It is used to run core parts of the demonstration in separate threads to prevent blocking the main execution flow and simulate real-time parallel processes.
  • A threading.Thread is created for the _run_demo_scenarios method, which handles the progression of the demo’s tactical scenarios. This thread is set as a daemon thread and started.
  • Another threading.Thread is used to create a status monitoring thread (status_monitor) that periodically prints system status updates every 15 seconds. This thread is also set as a daemon and started.
  • During the demo shutdown, self.demo_thread.join(timeout=5) is called to wait for the demo thread to complete before stopping.
• import signal:
  • The signal module is used to handle system signals for graceful shutdown of the application.
  • In the main function, signal.signal() is called to register a custom signal_handler function for signal.SIGINT (typically triggered by Ctrl+C) and signal.SIGTERM (a termination signal).
  • The signal_handler function ensures that when an interrupt signal is received, a shutdown message is printed, the stop_demo() method of the ComprehensiveGlassDemo instance is called to clean up resources, and the program exits gracefully. This is critical for robust application termination in a real-time system context.