Session
Constellations
Location
Salt Palace Convention Center, Salt Lake City, UT
Abstract
The Starling Extended Mission has demonstrated the transformative potential of software-defined architectures in multi-spacecraft swarms. Building on the original Starling mission, which validated autonomous coordination and networking among small satellites, the extended phase focused on adaptability—updating onboard payload software and software-defined radios (SDRs) to enable entirely new experiments without hardware modifications. This capability signals a new approach: spacecraft swarms can evolve post-launch to meet emerging scientific and operational objectives.
Central to this extension was the deployment of new software enabling two-way ranging and “reverse GPS” source identification using raw radio frequency (RF) signals. Traditionally, ranging and navigation rely on pre-integrated systems with fixed functionality. In contrast, Starling’s SDR-based architecture allowed engineers to upload new algorithms that repurposed existing hardware for advanced RF-based experiments. By leveraging the SDRs aboard three spacecraft, the swarm performed two-way time-of-flight ranging measurements, enabling precise inter-satellite distance estimation. This two-way ranging capability is critical for future missions requiring tight formation control without relying on GPS measurements.
The “reverse GPS” experiment further illustrates the adaptability of the swarm. The Starling spacecraft served as proof-of-concept for a GPS alternative to position determination on Earth. Rather than having a ground-based GPS receiver perform power intensive signal processing, requiring relatively large and heavy batteries and electronics, a small, lightweight RF transmitter emits a signal that is received by multiple satellites, hence reversing the usual RF direction for Earth asset localization. The RF signals are received by multiple on-orbit spacecraft and processed to recover time-of-arrival (TOA) information to determine the location of the transmitting source. This inversion of the traditional navigation paradigm required sophisticated signal processing and coordination among multiple nodes. By updating the onboard SDRs to record raw RF signals from a ground source, the swarm demonstrated the feasibility of this method of source localization—a capability with implications for space situational awareness, animal migration tracking, search-and-rescue operations, and beyond Earth planetary exploration.
To further advance autonomy, the mission applied a software patch that introduced a battery voltage-based reactive operations capability that enabled an extension to the Distributed Spacecraft Autonomy (DSA) experiment. Using onboard planners, DSA software could dynamically schedule and manage tasks, including control of cross-link radios and spacecraft attitude, based on real-time power availability. This means the entire swarm can adapt its communication and experiment cadence to manage energy, replacing the previous reliance on coarse, predetermined charging flight rules that limited experiment duration. By integrating resource-aware planning at the swarm level, Starling demonstrated how adaptive power management can unlock greater operational flexibility and resilience for future distributed missions.
These capabilities were made possible by Starling’s software-defined approach, which decouples mission functionality from hardware constraints. Rather than designing spacecraft for a fixed set of objectives, engineers can treat spacecraft swarms as dynamic platforms, reconfigurable through software updates. This flexibility extends mission life and could enable opportunistic scientific or technological experiments. These lessons inform future architectures for adaptive swarms, ensuring responsiveness to new objectives—scientific, commercial, or defense-related—and enabling distributed space systems to meet the unpredictable demands of exploration and operations in an increasingly dynamic space environment.
Document Type
Event
Software Defined Swarms
Salt Palace Convention Center, Salt Lake City, UT
The Starling Extended Mission has demonstrated the transformative potential of software-defined architectures in multi-spacecraft swarms. Building on the original Starling mission, which validated autonomous coordination and networking among small satellites, the extended phase focused on adaptability—updating onboard payload software and software-defined radios (SDRs) to enable entirely new experiments without hardware modifications. This capability signals a new approach: spacecraft swarms can evolve post-launch to meet emerging scientific and operational objectives.
Central to this extension was the deployment of new software enabling two-way ranging and “reverse GPS” source identification using raw radio frequency (RF) signals. Traditionally, ranging and navigation rely on pre-integrated systems with fixed functionality. In contrast, Starling’s SDR-based architecture allowed engineers to upload new algorithms that repurposed existing hardware for advanced RF-based experiments. By leveraging the SDRs aboard three spacecraft, the swarm performed two-way time-of-flight ranging measurements, enabling precise inter-satellite distance estimation. This two-way ranging capability is critical for future missions requiring tight formation control without relying on GPS measurements.
The “reverse GPS” experiment further illustrates the adaptability of the swarm. The Starling spacecraft served as proof-of-concept for a GPS alternative to position determination on Earth. Rather than having a ground-based GPS receiver perform power intensive signal processing, requiring relatively large and heavy batteries and electronics, a small, lightweight RF transmitter emits a signal that is received by multiple satellites, hence reversing the usual RF direction for Earth asset localization. The RF signals are received by multiple on-orbit spacecraft and processed to recover time-of-arrival (TOA) information to determine the location of the transmitting source. This inversion of the traditional navigation paradigm required sophisticated signal processing and coordination among multiple nodes. By updating the onboard SDRs to record raw RF signals from a ground source, the swarm demonstrated the feasibility of this method of source localization—a capability with implications for space situational awareness, animal migration tracking, search-and-rescue operations, and beyond Earth planetary exploration.
To further advance autonomy, the mission applied a software patch that introduced a battery voltage-based reactive operations capability that enabled an extension to the Distributed Spacecraft Autonomy (DSA) experiment. Using onboard planners, DSA software could dynamically schedule and manage tasks, including control of cross-link radios and spacecraft attitude, based on real-time power availability. This means the entire swarm can adapt its communication and experiment cadence to manage energy, replacing the previous reliance on coarse, predetermined charging flight rules that limited experiment duration. By integrating resource-aware planning at the swarm level, Starling demonstrated how adaptive power management can unlock greater operational flexibility and resilience for future distributed missions.
These capabilities were made possible by Starling’s software-defined approach, which decouples mission functionality from hardware constraints. Rather than designing spacecraft for a fixed set of objectives, engineers can treat spacecraft swarms as dynamic platforms, reconfigurable through software updates. This flexibility extends mission life and could enable opportunistic scientific or technological experiments. These lessons inform future architectures for adaptive swarms, ensuring responsiveness to new objectives—scientific, commercial, or defense-related—and enabling distributed space systems to meet the unpredictable demands of exploration and operations in an increasingly dynamic space environment.
