A Review of Machine Learning in Embedded Space Systems: Challenges, Techniques, and Future Directions

Abstract

In the quest for advanced autonomy and real-time data analysis, microsatellites such as CubeSats increasingly rely on embedded machine learning (ML) to meet demanding mission objectives. However, the limited power, memory, and processing resources on these platforms introduce significant challenges to algorithm design, performance, and reliability. This review provides a comprehensive examination of the state of the art in ML for microsatellites, highlighting the constraints inherent in low-power hardware and identifying strategies to overcome them. We begin by exploring model compression, lightweight architectures, and specialized software frameworks—including reusable, flight-software-integrated deployment approaches—that enable efficient onboard inference. Commercial off-the-shelf (COTS) hardware options—ranging from microcontrollers and Field-Programmable Gate Arrays (FPGAs) to System-on-Chip (SoC) devices with integrated Graphics Processing Units (GPUs)—are then discussed, emphasizing their relative merits and trade-offs under Size, Weight, and Power (SWaP) constraints. Drawing on a survey of key missions and research efforts, we outline the most compelling current and future applications, including autonomous navigation, Earth observation, and data filtering. We conclude with a timeline of notable ML-enabled CubeSat missions, synthesizing lessons learned and pinpointing open research issues, such as resilience to radiation, extreme model compression, and standardization. By unifying these insights, the review illuminates practical paths for designing robust, energy-efficient, and mission-ready ML solutions for space environments.