“If you can be smart about what you’re taking pictures of, then you only image the ground and skip the clouds. That way, you’re not storing, processing, and downloading all this imagery researchers really can’t use,” said Ben Smith of JPL, an associate with NASA’s Earth Science Technology Office, which funds the Dynamic Targeting work. “This technology will help scientists get a much higher proportion of usable data.”
How Dynamic Targeting Works
The testing is taking place on CogniSAT-6, a briefcase-size CubeSat that launched in March 2024. The satellite — designed, built, and operated by Open Cosmos — hosts a payload designed and developed by Ubotica featuring a commercially available AI processor. While working with Ubotica in 2022, Chien’s team conducted tests aboard the International Space Station running algorithms similar to those in Dynamic Targeting on the same type of processor. The results showed the combination could work for space-based remote sensing.
Since CogniSAT-6 lacks an imager dedicated to looking ahead, the spacecraft tilts forward 40 to 50 degrees to point its optical sensor, a camera that sees both visible and near-infrared light. Once look-ahead imagery has been acquired, Dynamic Targeting’s advanced algorithm, trained to identify clouds, analyzes it. Based on that analysis, the Dynamic Targeting planning software determines where to point the sensor for cloud-free views. Meanwhile, the satellite tilts back toward nadir (looking directly below the spacecraft) and snaps the planned imagery, capturing only the ground.
This all takes place in 60 to 90 seconds, depending on the original look-ahead angle, as the spacecraft speeds in low Earth orbit at nearly 17,000 mph (7.5 kilometers per second).
What’s Next
With the cloud-avoidance capability now proven, the next test will be hunting for storms and severe weather — essentially targeting clouds instead of avoiding them. Another test will be to search for thermal anomalies like wildfires and volcanic eruptions. The JPL team developed unique algorithms for each application.
“This initial deployment of Dynamic Targeting is a hugely important step,” Chien said. “The end goal is operational use on a science mission, making for a very agile instrument taking novel measurements.”
There are multiple visions for how that could happen — possibly even on spacecraft exploring the solar system. In fact, Chien and his JPL colleagues drew some inspiration for their Dynamic Targeting work from another project they had also worked on: using data from ESA’s (the European Space Agency’s) Rosetta orbiter to demonstrate the feasibility of autonomously detecting and imaging plumes emitted by comet 67P/Churyumov-Gerasimenko.
On Earth, adapting Dynamic Targeting for use with radar could allow scientists to study dangerous extreme winter weather events called deep convective ice storms, which are too rare and short-lived to closely observe with existing technologies. Specialized algorithms would identify these dense storm formations with a satellite’s look-ahead instrument. Then a powerful, focused radar would pivot to keep the ice clouds in view, “staring” at them as the spacecraft speeds by overhead and gathers a bounty of data over six to eight minutes.
Some ideas involve using Dynamic Targeting on multiple spacecraft: The results of onboard image analysis from a leading satellite could be rapidly communicated to a trailing satellite, which could be tasked with targeting specific phenomena. The data could even be fed to a constellation of dozens of orbiting spacecraft. Chien is leading a test of that concept, called Federated Autonomous MEasurement, beginning later this year.
