Dynamic Targeting of Satellite Observations Using Supplemental Geostationary Satellite Data and Hierarchical Planning
Akseli Kangaslahti, Itai Zilberstein, Alberto Candela, Steve Chien
AI summary
Problem
Traditional dynamic targeting satellites rely on short-range onboard lookahead sensors, limiting their ability to plan observations across long orbital trajectories. Extending this lookahead horizon to improve planning causes an exponential explosion in computational complexity that onboard systems cannot handle in real time.
Approach
The authors introduce a hierarchical planning framework that uses long-range geostationary satellite data to generate a coarse observation blueprint, which is then refined in real-time using short-range onboard sensor data.
Key results
- Up to 41% performance improvement over traditional planners
- Effective scaling to handle exponentially larger lookahead horizons
- Highest gains for sparsely distributed dynamic targets
- Validated across cloud avoidance and storm hunting scenarios
Why it matters
This method enables future Earth observation missions to maximize scientific utility by intelligently leveraging external data without exceeding strict onboard computational constraints.
Abstract
The Dynamic Targeting (DT) mission concept is an approach to satellite observation in which a lookahead sensor gathers information about the upcoming environment and uses this information to intelligently plan observations. Previous work has shown that DT has the potential to increase the science return across several applications. However, DT mission concepts must address challenges such as the limited spatial extent of onboard lookahead data and instrument mobility, data throughput, and onboard computation constraints. In this work, we show how the performance of DT systems can be improved by using supplementary data streamed from geostationary satellites that provide lookahead information up to 35 minutes ahead of time rather than the 1 minute latency from an onboard lookahead sensor. While there is a greater volume of geostationary data, the search space for observation planning explodes exponentially with the size of the horizon. To address this, we introduce a hierarchical planning approach in which the geostationary data is used to plan a long-term observation blueprint in polynomial time, then the onboard lookahead data is leveraged to refine that plan over short-term horizons. We compare the performance of our approach to that of traditional DT planners relying on onboard lookahead data across four different problem instances: three cloud avoidance variations and a storm hunting scenario. We show that our hierarchical planner outperforms the traditional DT planners by up to 41% and examine the features of the scenarios that affect the performance of our approach. We demonstrate that incorporating geostationary satellite data is most effective for dynamic problem instances in which the targets of interest are sparsely distributed throughout the overflight.