You may not be able to see it with the naked eye, but a silent crisis is unfolding in Earth’s orbit. With more than 11,000 active satellites currently in orbit – this number is expected to reach between 30,000 and 60,000 by 2030 – 40,500 tracked objects of 10 cm and larger, 1.1 million pieces of space debris between 1 and 10 centimeters, 130 million pieces of space debris between 1 millimeter and 1 centimeter, making our orbital infrastructure unprecedented. Challenges have to be faced. Traditional space monitoring systems that were designed for a much simpler era of space operations are struggling to keep pace with this rapid increase in orbital activity and space debris accumulation.
And the risks are high: Even a single collision between two satellites or between a satellite and debris of 1 centimeter or more could trigger a cascade of additional debris, potentially rendering entire orbiters unusable for decades, as Kessler syndrome predicts. The dangers extend far beyond mere risks of conflict. From sophisticated signal interference to potential adversarial actions, the security challenges facing our space assets have evolved dramatically.
As commercial launches accelerate, microsatellites become cheaper and mega-constellations become reality, the mathematical complexity of tracking and protecting our space assets has outstripped human analytical capabilities.. If We have to manage threats in orbit, we must build better space situational awareness (SSA), anomaly detection and predictability in orbit. The inherent complexity and need to respond rapidly to threats and issues requires, among other things, AI assistance. In my view, the world needs a Planetary Neural Network (PNN) – a system capable of managing these challenges for operators around the world.
AI in space security framework
Optical and radar ground systems detect orbiting objects by transmitting electromagnetic waves and analyzing echoes reflected from those objects. While traditional radar signal processing methods have proven robust, they reach their physical and algorithmic limits when dealing with weak radar cross-sections, cluttered signals, or transient detection. As a result, small debris often goes undetected, leaving orbital catalogs incomplete and increasing the risk of collisions.
Recent advances in machine learning and deep learning have opened up new possibilities for processing complex radar and optical data. By introducing AI-based layers into the signal processing chain, systems can improve weak signal detection, filtering out noise and atmospheric interference. AI can identify and classify orbital objects, recognize patterns consistent with size, spin state, or orbital regime, and predict trajectories and potential collisions with greater accuracy, even when observational data are incomplete or uncertain.
This integration marks a paradigm shift from purely physics-based models to data-driven intelligence capable of detecting, confirming, and classifying small pieces of debris as well as dynamic observational conditions in real time.
For example, a convolutional neural network (CNN) trained on thousands of radar echoes can recognize the unique spatial signature of a small metal piece, even if its signal is partially masked by noise. This significantly improves the sensitivity and reliability of detection networks operated by emerging SSA providers.
While CNNs excel at spatial analysis, they do not take into account temporal evolution, which is an essential aspect of object tracking and orbit prediction. To address this limitation, researchers combine CNNs with Long Short-Term Memory (LSTM) networks. These recurrent neural networks are capable of learning long-term dependencies in sequential data, making them ideal for analyzing how an object’s radar or optical signature evolves over time.
AI capabilities allow continuous tracking, even when data is intermittent due to sensor lag or poor observing conditions. The system can also disambiguate overlapping trajectories and maintain object identity across multiple sensor networks.
Planar Neural Network (PNN)
To transform the way orbital debris is tracked, inventoried, and managed, I propose a “central nervous system” for orbital awareness. The system will integrate multiple data streams – from satellite telemetry, ground-based sensors and electromagnetic spectrum analysis to social media reports, which is typically far beyond the scope of space engineering. All contributing to a dynamic, real-time picture of the space environment and SSA.
PNN looks beautiful. But it would be very easy to turn this into reality. The path to full-scale adoption is fraught with difficult technical and operational hurdles.
One of the most persistent challenges is the lack of data interoperability. Both public and private satellite operators use different formats, sampling rates, and labeling conventions. For accurate orbit tracking, global synchronization must reach millisecond accuracy which is quite difficult on Earth and even harder at orbital nodes.
For AI, it’s like trying to assemble a puzzle where each piece was cut by a different maker. The result is a fragmented data pipeline that slows down learning and makes it difficult to deploy models at scale.
To make a PNN viable, interoperability must occur at three levels:
- data format standardization: All observations must be converted into a shared, machine-readable schema, such as the CCSDS (Consultative Committee for Space Data Systems) standard.
- coordination and time integration: Interoperability is impossible without a common spatio-temporal context.
- Semantic and Metadata Harmonization: Even with similar formats, sensors use different terminology and measurement units.
Another technical barrier to the use of AI is the generation of false positives. A false positive occurs when a system detects an object that is not there, a random fluctuation, an interference pattern or a misinterpreted signal. For radar and optical tracking, these ghost detections are surprisingly common. They can come from: thermal or electronic noise that mimics a weak radar echo, continuous-wave interference, atmospheric reflections or simply AI over-sensitivity, when a neural network “sees” patterns in random noise.
Multiply this across hundreds of sensors around the world, in edge case events – the rare, extreme scenarios that are most likely to confound models – and in over-sensitivity, a side effect of trying to undetectably detect – and the chaos becomes digital as well as physical.
This is where PNN can make a difference. By connecting radar, optical and infrared sensors around the world, it provides a built-in mechanism for cross-verification. If a radar station reports a new object, but no other optical or orbital node confirms it, the network may mark the identification as suspicious. In contrast, if multiple sensors independently detect the same signature, confidence increases exponentially. This multi-sensor consensus is one of the most powerful weapons against false positives. Instead of relying on a single pair of “eyes”, the PNN would allow the entire planet to see, compare, and agree.
Another defense lies in the temporal intelligence of the system. LSTM-based models, as mentioned above, analyze not only individual frames but also the evolution of signals over time. A true orbital object follows predictable physics, its motion across the sky is constant and consistent. A false positive, in contrast, appears suddenly and disappears just as quickly. By tracking temporal stability, LSTMs can learn to reject transient anomalies by effectively asking: “Is this object behaving like something in orbit, or like a glitch?” This temporal logic turns the raw detection into a stable track, and filters out the fleeting noise that plagues single-sensor systems.
To further refine decisions, each detection in a PNN can be assigned a confidence score, a number indicating how likely it is to represent a real object. This score integrates several factors: signal-to-noise ratio, multi-sensor correlation, trajectory stability, and agreement between different AI models.
When multiple models, say, a CNN, an autoencoder, and a Transformer evaluate the same data, their results can be combined through collective learning. If all models agree, the detection is likely to be real. If only one does this, it’s probably noise. This type of AI committee dramatically reduces false positives, replacing single-model overconfidence with collective judgment. Despite automation, human expertise remains critical: operators can review ambiguous detections through visualization dashboards that show signal strength, orbital geometry, and sensor correlations.
Each confirmed false positive helps retrain the model, improving its future resilience, resulting in a feedback loop between machine learning and human reasoning. It is a practical middle path and is rapidly proving its value in the rapidly evolving space sector.
AI and the future of space security
The trajectory of AI in space security points in only one direction: toward increasingly sophisticated systems that are able to detect and proactively prevent space-based threats.
As machine learning models become more advanced and edge computing and TPU capabilities expand, we are moving toward such a future AI can predict autonomously Predict, detect and classify potential collisions, signal interference in real-time and automatically apply countermeasures against identified threats.
In the future, we will see even more applications, some of which we cannot yet imagine, from coordination of multiple satellites for distributed threat detection to quantum-resistant security protocols and advanced predictive maintenance capabilities. All of these will extend satellite operational lifetime and maintain safety and security integrity.
In other words, AI in space applications, whether they are ground or space based, are an absolute game changer for the future of space safety and security, and we can’t wait to see what’s on the horizon.
Hans Martin Steiner is Vice President and Head of Business Segment Institutional Space Business at Terma
