Dynamic Targeting of Satellite Observations Using Supplemental Geostationary Satellite Data and Hierarchical Planning
Authors: Akseli Kangaslahti, Itai Zilberstein, Alberto Candela, Steve Chien
Organizations: Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA · Harvard University, Cambridge, MA · Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA
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 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.
Figures & tables
Figure 1: DT uses environmental information collected by a lookahead sensor to intelligently plan primary sensor observations. Figure adapted from [ Candela et al., 2023 ] .
Figure 2: Example simulated orbit trajectories for each type of simulation data. The red line represents an orbit trajectory for the MODIS Cloud Mask data, while the blue line represents an orbit trajectory for the IMERG data.
Figure 3: Examples of the CA (top left), CAPD (top right), CART (bottom left), and SH (bottom right) utility models applied to parts of the MODIS Cloud Mask and IMERG datasets. Axes denote pixels, which have 1 km and 10 km spatial resolutions for MODIS-based and IMERG-based utility models, respectively. All images cover an area of about 260 km × 260 km. Note that the spatial resolution of the MODIS Cloud Mask dataset is finer than that of the IMERG dataset, which is why the SH utility model example appears coarser.
Figure 4: A visualization of the primary sensor slewing capability. The right edge of the figure represents where the lookahead sensor is sensing new data. The satellite is traveling towards the right edge of the figure. The primary sensor is current pointed at nadir, which is indicated by the red dot. The yellow area shows the reachable set of observation targets that the primary sensor can be slewed to within one cycle (4 seconds) of slewing time. The turquoise blue area shows the entire primary sensor range, which is determined by the primary sensor’s 15° maximum off-nadir. The purple area represents observation targets that have been sensed by the lookahead sensor but could not be reached during this cycle regardless of the initial position of the primary sensor.
Figure 5: Average predictive performance of the GOES Clear Sky product, with the MODIS Cloud Mask as ground truth.
Algorithm
MODIS
IMERG
CA 1
CAPD 2
CART 3
SH 4
NO
41.9%
9.6%
4.8%
24.4%
G
63.8%
14.8%
11.9%
27.1%
GH
88.2%
20.4%
25.0%
24.7%
U
97.5%
49.9%
44.7%
47.5%
P
–
52.0%
64.9%
–
Table 1: Aggregate Utility Accrued by Each Planning Algorithm Across Different Simulation Scenarios.
Offline reference trajectories for active target tracking are needed both for building multi-modal tracking datasets and for benchmarking online tracking planners under repeatable conditions. We present Track A star (TA star), an offline search-based trajectory planner that targets the visibility-aware target tracking objective on a discretized four-dimensional spatio-temporal grid (x, y, z, t). TA star combines a layered Directed Acyclic Graph (DAG) search with three engineering optimizations: cross-time obstacle distance caching against a Bounding Volume Hierarchy (BVH), per-layer beam pruning, and a configurable multi-ray visibility evaluator. TA star employs a beam-pruned heuristic search on this discrete graph to efficiently find high-quality tracking trajectories. While it trades strict theoretical optimality for practical scalability, our empirical results demonstrate robust, near-baseline visibility performance at a fraction of the computational cost. On a 1000-scenario stress test across eight CARLA Optimized maps, TA star converges on all scenarios and completes in 45 s using 32 workers; on a 248-scenario controlled comparison against an unoptimized priority-queue A star baseline (BinaryHeap implementation) under identical scenario inputs and a 5 x 10^6 expansion cap, TA star reduces mean planning time by 23.0x and worst-case planning time by 11.8x, while raising convergence from 56.9% to 100%. On the n=141 baseline-converged subset, TA star changes average visibility by only -0.15 percentage points (pp), with no scenario exceeding a 5 pp drop. We position TA star as a practical offline reference planner under these specific conditions, with limitations and failure cases discussed for environments such as Town07 dense vegetation.
Large low-Earth-orbit (LEO) Earth-observation (EO) constellations offer frequent access to geographically dispersed ground targets, but emergency requests may arrive after committed routine-plan execution has begun. The resulting dynamic emergency observation scheduling problem (DEOSP) requires urgent tasks to be inserted under intermittent ground contact without excessive routine-plan disruption. To address DEOSP, we propose a task-driven three-layer distributed scheduling (T3L-DS) method, which represents task demand and sensor footprints on a common geographic grid and forms temporary clusters from observation capabilities and current inter-satellite links. For intra-cluster coordination, T3L-DS introduces onboard dual-plan bidding and joint marginal evaluation. It also designs an inter-cluster coordination mechanism for unresolved demand. Extensive computational experiments compare T3L-DS with centralised simulated annealing (SA), an adapted selective time-variant better reply process (A-SeTVBRP), and a conventional contract-net protocol (CNP). T3L-DS achieves the highest emergency coverage among the distributed methods, with average relative improvements of approximately 2.8% and 17.1% over A-SeTVBRP and CNP, respectively. Its average relative gap from SA is approximately 7.1%. Under conflict-enhanced loads, it reduces routine-coverage loss by approximately 57.9% and 87.7% relative to A-SeTVBRP and CNP, respectively. The ablation study confirms the contribution of the proposed coordination enhancements. Overall, the results show that T3L-DS provides an effective distributed approach to DEOSP.
Qian Yin, Xinwei Wang, Guohua Wu
Central South University, Changsha, China · Queen Mary University of London, London, UK
Generating trajectories for synthetic aperture radar (SAR)-equipped aircraft poses significant challenges due to terrain constraints, and the need for straight-flight segments to ensure high-quality imaging. Related works usually focus on trajectory optimization for predefined straight-flight segments that do not adapt to the target visibility, which depends on the 3D terrain and aircraft orientation. In addition, this assumption does not scale well for the multi-target problem, where multiple straight-flight segments that maximize target visibility must be defined for real-time operations. For this purpose, this paper presents a multi-stage planning system. First, the waypoint sequencing to visit all the targets is estimated. Second, straight-flight segments maximizing target visibility according to the 3D terrain are predicted using a novel neural network trained with deep reinforcement learning. Finally, the segments are connected to create a trajectory via optimization that imposes 3D Dubins curves. Evaluations demonstrate the robustness of the system for SAR missions since it ensures high-quality multi-target SAR image acquisition aware of 3D terrain and target visibility, and real-time performance.
Daniel Fuertes, Carlos R. del-Blanco, Fernando Jaureguizar +2
Grupo de Tratamiento de Im´agenes, Information Processing and Telecommunications Center, ETSI Telecomunicaci´on, Universidad Polit´ecnica de Madrid, 28040 Madrid, Spain · Airbus Defence and Space, 28906 Madrid, Spain