A typical tracking program resembles a navigator steering a ship by the stars. But as soon as the sky clouds over, coordinates drift, and entropy (a measure of uncertainty) builds up in the data. The new algorithm gets a ‘co‑pilot’ — a module that monitors how confident the main navigator is.

When calculations begin to diverge from reality, a signal triggers: ‘the stars have shifted, it’s time to update the map.’ Such self‑control is similar to how astronomers recalibrate telescopes when brightness measurements fail, or to the automation of planet hunting via light fluctuations, which initiates additional checks.

Interestingly, the original method — the Kalman filter — guided the Apollo missions to the Moon. Now neural networks are teaching it to navigate even where no stars are visible. As a result, drones and probes become more reliable in the most unpredictable environments without human intervention.