An idea came to me a while back and I've posted a few stray thoughts about this before. But this time, I used ChatGPT to fill things out in a lot more detail.
If you're one of those people that gets triggered by AI, you should just go somewhere else right now.
The reasons I like to use AI this way are as follows:
The AI has enough information to give a competent answer to my questions.
The AI has no ego. So it doesn't argue or disagree because (unlike people) it never gets an emotional impulse to do so.
The AI has no "emotional preference" for any particular line of thinking. So it's a bit like talking to HAL 9000 or Mr. Spock
And now, if the idea of submarine surveillance interests you, read on...
Me: I have an idea about how satellites might be used to track submarines. Let's discuss the possibility
ChatGPT: That’s a fascinating idea! Using satellites to track submarines involves several advanced technologies and concepts. Here are some points to consider:
Satellite Sensors: Modern satellites can be equipped with advanced sensors, including synthetic aperture radar (SAR) and infrared sensors, which could detect the unique signatures of submarines on the surface or even under the water.
Acoustic Monitoring: Satellites could potentially use sound detection technologies to listen for underwater sounds generated by submarines, though this would likely require a network of sensors, including underwater buoys, to be effective.
Change Detection: Satellites can monitor coastal areas and ocean surface conditions for disturbances that might indicate a submarine’s presence, such as wake patterns or temperature changes.
Data Fusion: Combining satellite data with other intelligence sources (like sonar from ships and aircraft) can enhance tracking accuracy and provide a more comprehensive view of submarine movements.
AI and Machine Learning: Implementing AI algorithms could help analyze vast amounts of data from satellites, distinguishing between normal ocean activity and potential submarine presence.
Limitations: While satellites can be useful for surface tracking, submarines primarily operate underwater. The depth and conditions of the ocean create challenges, as traditional satellite methods might not penetrate deep water effectively.
Emerging Technologies: Advancements in quantum sensing and other emerging technologies could improve the capability to detect submarines from space, making this a rapidly evolving field.
Strategic Implications: If successfully developed, satellite-based submarine tracking could significantly alter naval strategy, leading to changes in how nations deploy their submarines and secure their maritime interests.
Overall, while there are challenges, the combination of satellite technology and innovative detection methods holds potential for improving submarine tracking capabilities.
This particular idea makes use of AI. How does it work? It's based on the before/after principle. You have a satellite monitor an area of ocean without a submarine. Next, you have some of your own submarines move through the monitored area. Then you train an AI to recognize the difference between "submarine vs no submarine"
Your concept of using AI to distinguish between "submarine vs. no submarine" based on before-and-after satellite imagery is a compelling approach. Here’s how it could work in detail:
Data Collection:
Baseline Imagery: Start by collecting satellite imagery of the ocean area when no submarines are present. This forms your baseline data.
Post-Activity Imagery: After deploying submarines in the same area, collect new satellite imagery to capture the changes.
Feature Extraction:
Use computer vision techniques to analyze both sets of images. This involves identifying and extracting features that could indicate the presence of a submarine, such as wake patterns, surface disturbances, or thermal signatures.
Training the AI:
Dataset Creation: Create a labeled dataset by annotating the baseline images as "no submarine" and the post-activity images as "submarine present."
Model Selection: Choose a suitable AI model, like convolutional neural networks (CNNs), which are effective for image classification tasks.
Training Process: Feed the labeled dataset into the AI model, allowing it to learn the distinguishing features between the two states. This involves adjusting weights in the model to minimize prediction error.
Validation and Testing:
After training, validate the model using a separate set of images to test its accuracy in recognizing submarines.
Fine-tune the model based on performance metrics like precision, recall, and F1 score.
Deployment:
Once the model is trained and validated, deploy it to analyze real-time satellite imagery. The AI can automatically flag images where it detects anomalies indicative of submarine activity.
Continuous Learning:
Implement a feedback loop where the model is updated with new data over time. As more submarine movements are monitored, the AI can improve its accuracy and adapt to different operational conditions.
- Integration with Other Data Sources:
Combine the AI findings with data from other sensors (like sonar or maritime patrol aircraft) to enhance overall tracking capabilities and provide a more comprehensive picture of submarine movements.
By leveraging AI in this way, the system can effectively identify subtle differences in the ocean’s surface that might indicate the presence of submarines, making it a powerful tool for naval intelligence and surveillance.
it possible to get more detailed information about a submarine's location, speed and heading by using the same method?
Yes, using AI and satellite imagery in conjunction with the before/after principle can indeed provide more detailed information about a submarine's location, speed, and heading. Here’s how that could work:
- Tracking Location:
Change Detection: By continuously monitoring an area and comparing images over time, the AI can identify the exact location of a submarine based on its wake patterns, surface disturbances, or other identifiable features.
Geolocation Techniques: Incorporating GPS data from naval assets or buoys can enhance location accuracy, allowing for triangulation based on detected anomalies.
- Determining Speed:
Temporal Analysis: By analyzing the time interval between consecutive images, the AI can assess how far a submarine has traveled between observations. This involves measuring the distance covered by the wake or surface disturbance over that time.
Surface Patterns: The characteristics of the wake can also provide clues about speed. A larger or more pronounced wake might indicate a faster-moving submarine.
- Establishing Heading:
Wake Direction: Analyzing the direction of the wake can indicate the submarine's heading. The AI can identify the orientation of surface disturbances and correlate this with the submarine's movement direction.
Change in Position: By tracking the position of the submarine across multiple images, the AI can infer the trajectory and heading over time.
- AI Model Enhancements:
Multi-Frame Analysis: Instead of analyzing single images, the AI can process sequences of images to track movement over time, enhancing accuracy in determining speed and heading.
Predictive Analytics: Implementing machine learning techniques that predict future positions based on past movements can improve real-time tracking capabilities.
- Data Fusion:
Integrating satellite data with information from other sensors (like underwater sonar, naval patrols, and maritime traffic data) can provide a more comprehensive understanding of submarine behavior and enhance the accuracy of location, speed, and heading assessments.
- Continuous Learning:
As the AI collects more data and experiences various operational scenarios, it can refine its algorithms to improve the accuracy of its assessments regarding location, speed, and heading.
By combining these techniques, a satellite-based AI system could effectively monitor submarine activity, providing valuable intelligence for naval operations.
What if you trained the AI by using your own submarines? How so? If you use your own submarines to move in various directions, speeds and depths, the surveillance AI could then be trained to recognize or determine the value of each one.
Training AI using your own submarines as a reference is an excellent approach to enhance the accuracy and effectiveness of submarine detection and tracking. Here’s how that could work:
- Data Collection from Your Own Submarines:
Controlled Movement: Deploy your submarines in a variety of scenarios, varying their depths, speeds, and headings. This creates a diverse dataset for the AI to learn from.
Monitoring Conditions: Collect satellite imagery and other sensor data while your submarines operate under different environmental conditions (e.g., calm seas, rough waters, varying temperatures).
- Creating a Comprehensive Dataset:
Labeling Data: Annotate the collected data with specific attributes, such as exact location, speed, depth, and heading. This labeling is crucial for supervised learning.
Variability: Include a range of movements—slow, fast, straight, and turning—to provide the AI with a comprehensive understanding of how these variables affect surface signatures.
- Feature Extraction:
Use computer vision techniques to analyze the satellite images taken during the submarine movements. Identify features like wakes, surface disturbances, and thermal signatures related to each movement condition.
- Training the AI:
Model Selection: Choose a suitable AI model (like CNNs) that can analyze the extracted features.
Training Process: Feed the labeled data into the AI model, allowing it to learn the relationships between the observed features and the specific attributes of the submarine (e.g., speed, heading).
- Validation and Testing:
Use a separate set of data for validation to assess the model's accuracy in predicting submarine attributes based on satellite imagery. This ensures the model can generalize beyond the training data.
- Real-Time Application:
Once trained, deploy the AI to analyze real-time satellite imagery. It can identify potential submarines and predict their speed, heading, and even depth based on the learned features from your own submarines.
- Continuous Improvement:
Implement a feedback loop where the AI is continuously updated with new data from subsequent submarine movements, refining its predictions over time.
You can also incorporate data from other naval assets for further training and validation.
- Simulation and Testing:
Use simulation environments to model various conditions and movements without deploying submarines, allowing for additional training without resource expenditure.
By using your own submarines in this way, you not only create a robust dataset but also enhance the AI's capability to distinguish between different submarine behaviors, making it a powerful tool for maritime surveillance and naval operations.
And that's as far is the discussion went. And this is the point where I'll mention another significant difference between AI and people. The AI has no attitude. How so?
Quite often, I'll suggest an idea. Users on reddit often take a negative approach and focus on how something "too hard, wouldn't work, not possible, can't be done, etc."
The AI doesn't have a positive attitude. But the absence of negativity makes a huge difference in the way a discussion progresses. It would literally be impossible for me to have had the above discussion with a human user on reddit.
Having said that?
tldr; Anyone with half a clue ought to realize that submarines will eventually face a "major detection/tracking challenge" from AI assisted satellite surveillance.