Over the past three decades, the acceptance of higher risk thresholds within the space industry has facilitated the widespread integration of commercial off-the-shelf (COTS) components into avionics and payloads, leading to a remarkable transformation in the design of space missions. This transformation has led to the emergence of the New Space Economy and the widespread adoption of lean or small satellites in general, particularly CubeSats. CubeSats are now widely used in commercial, scientific, and research applications due to their versatility, affordability, simplicity of development, and accelerated development timelines. On-board computing plays a crucial role in the design of CubeSat missions, as increasingly high-performance computational requirements are needed to meet the challenges of future missions. This paper systematically reviews the state-of-the-art of CubeSat Command and Data Handling (C&DH) sub-system, covering both hardware components and flight software (FSW) development frameworks. It presents an analysis of the key features and recent developments of on-board computers (OBCs) in commercial and academic institutional projects funded by governments, agencies and public institutions. It further examines the effects of space radiation on avionics components and discusses the main fault-tolerance techniques used in CubeSat platforms. Finally, this paper highlights trends and hazards for future CubeSat avionics and identify potential directions for future developments in high-performance on-board computing. By synthesizing contemporary research and industry insights, this paper aims to shed light on CubeSat OBC design, providing an overview of the existing technology landscape and the challenges to be addressed for next-generation mission needs.

We present the implementation of four FPGA-accelerated convolutional neural network (CNN) models for onboard cloud detection in resource-constrained CubeSat missions, leveraging Xilinx’s Vitis AI (VAI) framework and Deep Learning Processing Unit (DPU), a programmable engine with pre-implemented, parameterizable IP cores optimized for deep neural networks, on a Zynq UltraScale+ MPSoC. This study explores both pixel-wise (Pixel-Net and Kernel-Net) and image-wise (U-Net and Patch-Net) models to benchmark trade-offs in accuracy, latency, and model complexity. Applying channel pruning, we achieved substantial reductions in model parameters (up to 98.6%) and floating-point operations (up to 90.7%) with minimal accuracy loss. Furthermore, the VAI tool was used to quantize the models to 8-bit precision, ensuring optimized hardware performance with negligible impact on accuracy. All models retained high accuracy post-FPGA integration, with a cumulative maximum accuracy drop of only 0.6% after quantization and pruning. The image-wise Patch-Net and U-Net models demonstrated strong real-time inference capabilities, achieving frame rates per second of 57.14 and 37.45, respectively, with power consumption of around 2.5 W, surpassing state-of-the-art onboard cloud detection solutions. Our approach underscores the potential of DPU-based hardware accelerators to expand the processing capabilities of small satellites, enabling efficient and flexible onboard CNN-based applications.

The context of this study is the geolocation of signal emitters on the Earth’s surface through satellite platforms able to perform Angle of Arrival (AOA) measurements. This paper provides the theoretical framework to solve the optimization problem for the orbital deployment of the satellites minimizing the variance on the position error estimation with constraints on the line of sight (LOS). The problem is theoretically formulated for an arbitrary number of satellites in Low Earth Orbit (LEO) and target pointing attitude, focusing on minimizing the Position Dilution of Precision (PDOP) metric, providing a methodology for translating mission design requirements into problem formulation. An exemplary numerical application is presented for the operative case of the placement of a second satellite after a first one is launched. Simulation results are on angles of true anomaly, right ascension of the ascending node, and spacing angle, while accounting for orbital radius and emitter latitude. New insights on trends, parameter dependencies, and properties of symmetry and anti-symmetry are presented. The topic is of interest for new technological demonstrators based on CubeSats with AOA payload. Civil applications of interest are on interceptions of non-cooperative signals in activities of spectrum monitoring or search and rescue.

The framework of the article is a theoretical investigation of the Doppler effect on electromagnetic signals. The target application is relevant to satellite missions with resource-constrained systems based on low-cost hardware platforms and the innovation of the paper is on the error compensation strategy and on the proposal of an approximate polynomial model. In particular, the scenario of interest considers a non-cooperative emitter of electromagnetic waves on the surface of the Earth and a receiver able to perform Angle of Arrival measurements embarked on a satellite in Low Earth Orbit. Assuming the problem of geolocation is solved, and starting from the kinematics of the satellite and its relative motion with respect to the emitter, the correction on the frequency is derived with respect to the orbital parameters, which is a common representation of data for satellite systems. The purpose of the paper is not only to provide a reference model to predict the necessary correction on the frequency of the signal due to the Doppler effect, but also to present a real-time strategy for compensation in the pipeline of signal processing after its geolocation. The main goal of the paper is to give real-time estimation and compensation strategy for the Doppler factor considering only the current instantaneous kinematic information, without the information on the history of the signal over time. Furthermore, data fitted from simulations show that a first degree polynomial model has a maximum error prediction on the order of less than 0.01 percent around Nadir pointing and can be appealing for fast computation of this correction with low-cost hardware based on Field Programmable Gate Array.

This study focused on determining the conditions leading to variance in the measurements of an antenna array capable of measuring the direction of electromagnetic waves. The payload of the study is a cross-array of antennas that is able to measure direction through array beamforming and angle of arrival (AOA) technology. Starting from the modeling of satellite kinematics (in terms of the satellite’s position and attitude combined with its relative position with respect to an electromagnetic wave emitter located on Earth’s surface), this study provides the mathematical fundamentals to identify potential cases that lead to divergence in the estimation variance for the position of a signal emitter. The numerical and analytical predictions, conducted through an evaluation of the Cramér–Rao lower bound (CRLB) metrics, were on the azimuth, elevation, and broadside angles through the generation of errors in the attitude with Monte Carlo simulations. Recent advancements in the miniaturization of electronics make these studies of particular interest for a new set of technological demonstrators equipped with payloads composed of antenna arrays. Applications of interest include Earth-scanning missions, with exemplary cases of search-and-rescue operations or the spectrum monitoring of jamming in the E1/L1 band for the GNSS.