Researchers have successfully identified a space-based GNSS interference source causing widespread disruptions across Europe, Greenland, and Canada as a constellation of Russian early-warning satellites in Molniya orbits.

// 2h agoRESEARCH PAPER

Researchers have successfully identified a space-based GNSS interference source causing widespread disruptions across Europe, Greenland, and Canada as a constellation of Russian early-warning satellites in Molniya orbits.

A research paper titled "Chasing Lightning" details the detection and identification of a space-based Global Navigation Satellite System (GNSS) interference source that has caused powerful, transient wide-area disruptions over continental Europe, Greenland, and Canada since 2019. Using data collected over seven years from terrestrial GNSS reference stations, the authors developed a received-power-based detection framework and analyzed the spatial, temporal, and spectral characteristics of the interference. By combining received-power and time-difference-of-arrival measurements, the researchers confidently attributed the source to a constellation of Russian early warning satellites in highly elliptical Molniya orbits.

// ANALYSIS

Space-based GNSS interference is graduating from a theoretical concern to a major geopolitical tool, drastically expanding the scope of electronic warfare.

–Unlike terrestrial jamming, space-based signals can disrupt navigation across vast geographic regions, including Europe and North America simultaneously.
–Attributing the source to Russian early-warning satellites in Molniya orbits reveals that military space infrastructure is actively being utilized to interfere with civilian and navigation signals.
–Ground-based sensor networks combined with time-difference-of-arrival algorithms are proving to be powerful tools for civilian attribution of orbital electronic warfare.

// TAGS

gnssgpssatellitespacesecurityelectronic-warfarerussiaresearch

DISCOVERED

2h ago

2026-06-05

PUBLISHED

5h ago

2026-06-05

RELEVANCE

8/ 10

AUTHOR

mimorigasaka

// KEEP READING

More AI developer news from the feed

EXPLORE FULL FEED

UPDATE51m ago

Anthropic adds 'Dreams' to Managed Agents API

Anthropic launched "Dreams", an asynchronous memory consolidation feature for the Claude Managed Agents API. The offline job prunes stale context and resolves contradictions, resulting in a cleaner and more performant long-term memory store.

MODEL1h ago

Gemma 4 12B Assistant accelerates decoding

The Gemma 4 12B IT Assistant checkpoint has caused developer confusion, as it is actually a speculative decoding drafter model rather than a standalone chatbot. When configured in tools like vLLM alongside the primary model, it accelerates inference via Multi-Token Prediction without degrading quality.

BENCHMARK1h ago

Google's Gemma 4 12B model faces criticism for slow local performance and poor multimodal generation on Apple Silicon.

A developer tested Google's Gemma 4 12B model on a MacBook Pro M5 Max with 128GB of unified memory, finding its performance disappointing for a model of its size. Achieving only 44 tokens per second, the developer noted that the model's generation quality was subpar—specifically criticizing a produced lava lamp image—and recommended developers stick with Qwen 3.6 27B or 35B instead.