This PDF file contains the front matter associated with SPIE Proceedings Volume 13062, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Upcoming space missions are expected to go farther from Earth and be more autonomous and self-sufficient. Most man-made satellites are controlled from Earth-based ground stations that also perform guidance and navigation functions. Onboard star trackers and GPS units are commonplace on satellites and part of the guidance, navigation and control systems, permitting in situ measurement and update to the guidance solution. However, without an extension of the network, GPS units are not expected to operate in deep space, making them suitable for spaceflight in the near-Earth orbits only. Star trackers, which use an optical payload, permit accurate pointing of the satellite via the methods of astrometry, but do not provide a full guidance and navigation solution. In this paper we explore characteristics of a generation-after-next satellite navigational sensor concept where, using optical or infrared spectral measurements in addition to the typical techniques of astrometry for locating stars, onboard autonomous computation of a navigational solution is possible. Spectral measurements allow estimation of stellar velocities, in addition to relative locations. We hypothesize that recent space missions have generated the star catalogs, with both position and velocity measurements, necessary to anchor measurements of the new conceptual sensor.

The space sector's rapid growth, coupled with increased accessibility to space, has led to the popularity of miniaturized satellites known as CubeSats. These cost-effective and agile nanosatellites have gained international recognition in government, education, and private sectors. CubeSats, standardized at 10 cm x 10 cm x 10 cm, come in various sizes (1U, 2U, 3U, and 6U) and are preferred by the GIS/RS community for earth observation capabilities. Sharjah Academy for Astronomy, Space Science and Technology (SAASST) in the UAE has established a CubeSat laboratory, launched the Sharjah-Sat-1 (3U+) and now embarking on the Sharjah-Sat-2 mission. Sharjah-Sat-2 is a 6U CubeSat equipped with an advanced high-definition hyperspectral camera, Hyperscape100, to enhance infrastructure projects and establish an early warning system for environmental phenomena. This paper will discuss advancements in spaceborne hyperspectral imagers, compare nanosatellites to larger satellites, highlight the Sharjah-Sat-2 project, and explore its positive impact on the GIS/RS community.

Corning Incorporated has leveraged its industry leading space based hyperspectral technology to create an advanced Low Earth Orbit (LEO) Satellite Payload. We outline the specifications, performance, and capabilities of this new standard in LEO Hyperspectral Imaging (HSI). Corning’s new product platform is capable of ⪅8m GSD imaging across the 400nm-2500nm spectral band with high dispersion. It has onboard computing, storage, and processing capabilities which enhance its exceptional optical and sensor performance. Corning’s design exceeds the launch stress requirements of standard LEO Transporter vehicles, such as the SpaceX Falcon-9, and has been proven on multiple successful LEO missions. The product platform contains a flexible electro-mechanical interface design suitable for a variety of host bus platforms and functions.

Optical systems are frequently used in non-laboratory environments. Extra-orbit launch accelerations, gunfire vibrations, and temperature fluctuations can put dangerous stresses on lenses. In addition to general mechanical ruggedization, Kapton® tape was added to optical surfaces as a go-between for the glass/crystal and the metal structure components. The thought behind this was that using a softer surface interface would act as a sort of cushion for the optics. Kapton® tape has a CTE nearly matching that of Aluminum 6061-T6 and has an effective temperature range of -269°C to 400°C, which makes it an ideal material for use in systems both at or outside of room temperature values. Hand calculations formulated from equations postulated by experts Paul Yoder and R.J. Roark were used in conjunction with Finite Element Analysis via ANSYS. Practical examples used in the field were cited as well.

Applications such as LIDAR, ranging/ sensing, and optical communications all require photonic components, such as sources, detectors, and modulators, to be integrated into a single system. For spaceborne applications, SWaP (size, weight and power) is a key consideration: a monolithic indium phosphide (InP) Photonic Integrated Circuit (PIC) can integrate many components onto a chip with a footprint of a few square mm. Photonic Wirebonding (PWB) enables seamless integration of best-in-class optical devices from disparate materials. Connecting and mode-matching different photonic components enables versatility and functionality unachievable by other methods, facilitating co-packaging. PICs and PWBs do not yet have spaceflight heritage: demonstrating increased Technology Readiness Level (TRL) is a key step toward use in orbital and spaceborne missions. Freedom Photonics presents our first hermetic photonic wirebonded PIC package, alongside recent environmental testing results demonstrating that our PIC and PWB technologies are suitable for the harsh conditions of launch and spaceflight: shock, vibration, radiation, and temperature cycling.

Space-based optical communication links incorporating high speed photoreceivers, i.e. photodiodes integrated with Transimpedance Amplifiers (TIA), are required for multiple platforms, from low earth orbit satellite communication constellations to inter-planetary links and deep space missions. Our prior studies have demonstrated that InP/InGaAs photodiodes are resilient to radiation induced displacement and ionization damage when irradiated with a wide variety of ions. It is also necessary to qualify TIAs that may exhibit latch ups due to Single Event Effect (SEE) when irradiated with heavy ions having high Linear Energy Transfer (LET). We present a balanced InGaAs photoreceiver, i.e. a matched pair of photodiodes followed by a Silicon CMOS TIA, with automatic gain control mode that supports coherent and direct detection optical communication links with a symbol rate up to 25 Gbaud and aggregate data rate up to 100 Gbps and beyond. These devices were subjected to 76 MeV/n, 96 MeV/n, and 154 MeV/n Bismuth Ions up to a fluence of 1E7 ions/cm² for each ion energy. The ion energies were chosen with the objective of achieving LET-Si of ⪆70 MeV-cm² /mg. During the radiation runs, the TIAs were biased and their drive currents and RF output noise spectra were continuously recorded. The in-situ data was complemented by detailed analog and digital characterization of these devices before and after irradiation, including photodiode dark current, TIA drive current, RF response, RF return loss, noise spectrum, 25 Gbps Amplitude Shift Keyed (ASK) eye diagrams and bit error ratio, and 10.709 Gbps Return to Zero Differential Phase Shift Keyed (RZ-DPSK) eye diagrams and bit error ratio. We did not observe any significant impact on these devices due to radiation.

This study aims to improve the rotational motion and inertia parameters estimation performance of an Unscented Kalman Filter model (UKF) for a torque-free tumbling non-cooperative space object using the Gaussian Process (GP). The traditional UKF algorithm which is a physics-based estimation algorithm for non-linear systems is susceptible to the physical process, measurement sampling rate, and filter design. Consequently, slight inaccuracy in the assumed physical models, low sampling rates, or small variations of the filter parameters can result in poor estimation performance. Additionally, the UKF model might not predict the motion and inertia parameters with good accuracy in the absence of sensor measurements, also known as occlusion, a quite common challenge for space missions. To make a UKF model more robust to the factors above, we utilize multi-output GP models with periodic kernels to make long-term predictions of the position and attitude measurements obtained from a Laser Camera System (LCS). These measurement predictions from GP models are used as the sensor measurements for the UKF model. We implement a Fast Fourier Transform on the sensor measurements to determine the initial guess for periodicity hyper-parameters for the periodic kernels. Results from conducted simulations show that the proposed UKF model with GP-predicted measurements (UKF-GP model) performs remarkably well compared to the UKF model under the assumption of long-term occlusion. It is also observed from the results that, the UKF-GP model is more robust to sensor sampling rate, underlying physical process, and filter parameters even with occlusion, compared to the UKF model without occlusion.

Shadow imaging has been used for decades in astronomical observation of distant space objects. Synthetic Aperture Silhouette Imaging applies this technology to space domain awareness to enable fine resolution silhouette images of satellites in the Geosynchronous (GEO) belt to be collected with a linear array of hobby telescopes. As a satellite passes between a star and the observer on the ground, a North-South telescope array can detect the reduced stellar intensity as the shadow of the satellite passes over from West to East. This paper discusses the resolution advantages of collecting and stacking shadow images at multiple wavelengths to arrive at a multispectral improvement factor. A laboratory model is scaled to GEO according to the Fresnel diffraction integral before the silhouette is recovered through a phase retrieval algorithm. The recovered silhouettes are stacked and evaluated against the image of the original laboratory target to determine how closely the images match. The best Percent Difference (PD) between the reconstructed silhouette and the target silhouette is found by scaling the intensity of the diffraction pattern using a look up table to the fourth power. The best PD from a stacked image is using five layers between 475 nm and 675 nm. The five layers produce a resolution of approximately 50 cm. Each additional layer improves resolution from the expected value by approximately 4.23 cm from two layers to six layers.

With the ever-increasing number of satellites orbiting the Earth for the purposes of communication and research, a significant emphasis is placed on Space Domain Awareness (SDA). The orbital environment contains millions of bodies that can endanger the operation and existence of satellites. As a result, companies and governments around the world have built several massive radar arrays tasked with detecting, tracking, and cataloguing tens of thousands of Resident Space Objects (RSOs). A cost-, size-, and spectrum-effective method to achieve space debris tracking is to use passive forward scatter radar. In this radar configuration, target baseline crossing events produce special phenomena in the time- and frequency- domains which may be used for unique target identification. Experimental demonstrations of these effects are explored in this paper.

Single-frequency GNSS users are reliant on estimates of the Total Electron Content (TEC) along lines of sight to navigation satellites to correct for ionospheric propagation delay and the resulting positioning errors. The parametric correction methods in use (Klobuchar’s algorithm for GPS and the NeQuick-G model for Galileo) can compensate for a large fraction of the delay but are hindered by using only a few daily coefficients to describe the ground truth ionosphere state. This loss of state information is particularly detrimental during periods of high deviation from baseline TEC patterns, e.g. solar weather events. This work describes an autoregressive RNN/CNN approach for spatiotemporal TEC forecasting from windowed historical map products, preserving local temporal and geospatial dependence between samples. By leveraging a large dataset spanning from 2000-2020 and applying convolutional transformations over both the temporal and spatial dimensions of the data, this model exhibits improved performance for time horizons up to 48 hours, compared to neural network-based approaches described in the literature to date.

The mobility and versatility of Unmanned Aerial Systems (UASs) make them valuable platforms in Distributed Cooperative Beamforming (DCB) applications, where high-precision time synchronization and Positioning, Navigation, and Timing (PNT) are required. UAS with PNT can quickly respond to changing situations and provide temporary coverage in remote or disaster-affected areas. While the onboard PNT equipment allows UASs to obtain reliable PNT solutions, human presence with supervisory roles (aka human-on-the-loop (HotL)) is almost inevitable in such equipment with automation and multi-level resilience of prevention, response, and recovery functions. This paper employs a meta-model to describe interactions among the human operators and multiple UAS platforms for resilience aware HotL PNT in the DCB scenario. The roles of UASs and humans in the decision-making process of resilient PNT are clarified. Interaction points where humans should collaborate with UASs are identified to augment the autonomy of the UASs. Moreover, requirements are specified for the interaction points. Simulations of a HotL multi-UAS positioning system demonstrate that the requirements modeling facilitates the design of human-machine teaming, and the human presence enhances the resilience of the positioning system.

Face recognition technology has been well investigated in past decades and widely deployed in many real-world applications. However, low-resolution face recognition is still a challenging task in resource-constrained edge computing environment like the Internet of Video Things (IoVT) applications. For instance, low-resolution images are common in surveillance video streams, in which the rare information, variable angles, and light conditions create difficulties for recognition tasks. To address these problems, we optimized the correlation feature face recognition (CoFFaR) method and conducted experimental studies in two data preparation modes, symmetric and exhaustive arranging. The experimental results show that the CoFFaR method achieved an accuracy rate of over 82.56%, and the two-dimensional (2D) feature points after dimension reduction are uniformly distributed in a diagonal pattern. The analysis leads to the conclusion that the data augmentation advantage brought by the method of exhaustive arranging data preparation can effectively improve the performance, and the constraints by making the feature vector closer to its clustering center have no apparent improvement in the accuracy of the model identification.

The volume of remote sensing data is experiencing rapid growth, primarily due to the plethora of space and air platforms equipped with an array of sensors. Due to limited hardware and battery constraints the data is transmitted back to Earth for processing. The large amounts of data along with security concerns call for new compression and encryption techniques capable of preserving reconstruction quality while minimizing the transmission cost of this data back to Earth. This study investigates image compression based on Convolutional Variational Autoencoders (CVAE), which are capable of substantially reducing the volume of transmitted data while guaranteeing secure lossy image reconstruction. CVAEs have been demonstrated to outperform conventional compression methods such as JPEG2000 by a substantial margin on compression benchmark datasets. The proposed model draws on the strength of the CVAE’s capability to abstract data into highly insightful latent spaces and combining it with the utilization of an entropy bottleneck is capable of finding an optimal balance between compressibility and reconstruction quality. The balance is reached by optimizing over a composite loss function that represents the rate-distortion curve.

Apache Storm is a popular open-source distributed computing platform for real-time big-data processing. However, the existing task scheduling algorithms for Apache Storm do not adequately take into account the heterogeneity and dynamics of node computing resources and task demands, leading to high processing latency and suboptimal performance. In this thesis, we propose an innovative machine learning-based task scheduling scheme tailored for Apache Storm. The scheme leverages machine learning models to predict task performance and assigns a task to the computation node with the lowest predicted processing latency. In our design, each node operates a machine learning-based monitoring mechanism. When the master node schedules a new task, it queries the computation nodes obtains their available resources, and processes latency predictions to make the optimal assignment decision. We explored three machine learning models, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Deep Belief Networks (DBN). Our experiments showed that LSTM achieved the most accurate latency predictions. The evaluation results demonstrate that Apache Storm with the proposed LSTM-based scheduling scheme significantly improves the task processing delay and resource utilization, compared to the existing algorithms.

In this study, we introduce an innovative Robot State Estimation (RSE) methodology incorporating a learning-based contact estimation framework for legged robots, which obviates the need for external physical contact sensors. This approach integrates multimodal proprioceptive sensory data, employing a Physics-Informed Neural Network (PINN) in conjunction with an Unscented Kalman Filter (UKF) to enhance the state estimation process. The primary objective of this RSE technique is to calibrate the Inertial Measurement Unit (IMU) effectively and furnish a detailed representation of the robot’s dynamic state. Our methodology exploits the PINN to mitigate IMU drift issues by imposing constraints on the loss function via Ordinary Differential Equations (ODEs). The advantages of utilizing a contact estimator based on proprioceptive sensory data are multifold. Unlike vision-based state estimators, our proprioceptive approach is immune to visual impairments such as obscured or ambiguous environments. Moreover, it circumvents the necessity for dedicated contact sensors—components not universally present on robotic platforms and challenging to integrate without substantial hardware modifications. The contact estimator within our network is trained to discern contact events across various terrains, thereby facilitating resilient proprioceptive odometry. This enables the contact-aided invariant Kalman Filter to produce precise odometric trajectories. Subsequently, the UKF algorithm estimates the robot’s three-dimensional attitude, velocity, and position. Experimental validation of our proposed PINN-based method illustrates its capacity to assimilate data from multiple sensors, effectively reducing the influence of sensor biases by enforcing ODE constraints, all while preserving intrinsic sensor characteristics. When juxtaposed with the employment of the UKF algorithm in isolation, our integrated RSE model demonstrates superior performance in state estimation. This enhanced capability automatically reduces sensor drift impacts during operational deployment, rendering our proposed solution applicable to real-world scenarios.

The advancement of Earth observation satellite research in past decades has demonstrated itself to be productive and increasingly important. Utilized for applications such as climate monitoring, communication, GPS, defense, and space research, our dependence on reliable satellite systems is ever-increasing. The success of satellites in these scenarios is fundamentally the result of its attitude determination system, consisting of control and estimation subsystems, which govern its sensors and actuators. For simple missions, attitude pose determination can be computed onboard the satellite. Typically, however, ground stations or other satellites (i.e. constellations) are involved in a satellite’s operation, processing large amounts of data or complex control algorithms. This information and control cycle is enabled through the application of Networked Control Systems (NCS). The NCS uses a wireless network or communication system as the intermediate line of communication between plant, actuators, sensors, and other systems. This enables relatively fast communication and data transmittance over long distances, as well as the decentralization of navigation and control through system distribution. However, this method is vulnerable to various forms of time delay and packet loss, which ultimately affects the control performance of a satellite. It is demonstrated in literature that the effects of these NCS properties can be mitigated, increasing its viability, through various implementations of smart systems into the satellite framework. Using techniques such as neural networks and reinforcement learning, the satellite can perceive and act based on environmental information, while considering experiential memory and attention allocation. The following comprehensive survey discusses methods for improving the robustness of networked satellite systems from a smart systems perspective, providing an advanced foundation for these concepts.

An inherent property of dynamic systems with real applications is their high degree of variability, manifesting itself in ways that are often harmful to system stability and performance. External disturbances, modeling error, and faulty components must be accounted for, either in the system design, or algorithmically through estimation and control methods. In orbital satellite systems, the ability to compensate for uncertainty and detect faults is vital. Satellites are responsible for many essential operations on Earth, including GPS tracking, radio communication/broadcasting, defense, and climate monitoring. They are also expensive to design and fabricate, to deploy, and currently impossible to fix if suddenly inoperable. In being subjected to unforeseen disturbances or minor system failures, communications with Earth can cease and valuable data can be lost. Researchers have been developing robust estimation and control strategies for several decades to mitigate the effects of these failure modes. For instance, fault detection methods can be employed in satellites to detect deviations in attitude or actuator states such that error or incorrect data does not propagate further across its long life cycle. The Kalman Filter (KF) is an optimal state estimation strategy with sub-optimal nonlinear variations, commonly applied in most dynamic systems, including satellites. However, in the presence of aforementioned uncertainties, these optimal estimators tend to degrade drastically in performance, and must be replaced for more robust methods. The newly developed Sliding-Innovation Filter (SIF) is one such candidate, as it has been demonstrated to perform state estimation robustly in faulty systems. Using an in-lab Nanosatellite Attitude Control Simulator (NACS), an adaptive hybrid formulation of the SIF and EKF is applied to a satellite system to detect faults and disturbances in experiments, based on the Normalized Innovation Squares (NIS) metric. This strategy was demonstrated to improve state estimation accuracy in the presence of multiple faults, compared to conventional methods.

Deployed for purposes of GPS, defense, atmospheric and space research, environmental monitoring, broadcasting, and communication, Earth observation satellites are complex systems that require the design of highly reliable control and estimation algorithms. A satellite’s Attitude Determination and Control System (ADCS) must be able to operate accurately, in a robust manner against unexpected conditions, especially in missions that demand more intricate tasks. The desire for optimal and robust performance in satellites has been the driving factor behind decades of attitude control research. With computers, the performance of spacecraft subject to some mission can be simulated to test new control methods, but the availability of real satellites to researchers for testing these algorithms is very limited. To solve this issue, attitude control simulators have been developed, such that algorithms and hardware can be tested inexpensively in a lab environment, while maintaining a high level of accuracy to the environment it emulates. The Nanosatellite Attitude Control Simulator (NACS) has been developed at McMaster University for this purpose. Consisting of a mock 1U CubeSat, an air-bearing configuration, and an Automatic Balancing System (ABS), rotational attitude control experiments are conducted in-lab without deployment, simulating the zero-gravity of space. The mechanism responsible for environment simulation is the ABS, which minimizes residual torque due to gravity by influencing the center of mass (CoM) of the system, thereby improving control performance and efficiency. The performance of the ABS in a balancing task is presented, where system parameters of inertia and CoM are estimated from response data. Three filtering strategies are investigated for this purpose, providing varying degrees of accuracy and computational cost.

Unmanned Aerial Vehicles (UAV) have been widely adopted in many applications, from surveillance to delivery. More UAV delivery businesses are expected to be launched in the foreseeable future to meet food, goods, and medicine needs for residents living in smart cities, remote areas, or places lacking runways. As the density of UAVs operating in a community increases, collision avoidance becomes critical concerning the safety of personnel, property, and UAVs. In the last decade, many solutions have been suggested for collision avoidance scenarios, where typical solutions require integrated sensing, information exchange, and on-board decision-making. However, including these essential components increases the cost and makes it unaffordable for small-size UAVs in terms of payload weight and power consumption. Inspired by the Metaverse-enabled by Digital Twins, Blockchain, Augmented Reality (AR)/Virtual Reality (VR), and the fifth generation (5G) wireless communication technologies; we propose LoCASM, a low-cost collision avoidance scheme in Microverse, a local-scale Metaverse, for UAV delivery networks. LoCASM only requests position (GPS), altitude, velocity, and direction (PAVAD) information from each UAV; relieving the burden of expensive and energy-consuming components. By mirroring UAVs’ PAVAD information and the city landscape in the Microverse, the computing-intensive tasks, including UAV tracking, trajectory prediction, and collision avoidance management, are migrated to the Microverse server on the ground. A proof-of-concept prototype of the LoCASM system has been built, and the simulation experimental study has validated the design.

Cooperative Augmented Reality (AR) can provide real-time, immersive, and context-aware situational awareness while enhancing mobile sensing capabilities and benefiting various applications. Distributed edge computing has emerged as an essential paradigm to facilitate cooperative AR. We designed and implemented a distributed system to enable fast, reliable, and scalable cooperative AR. In this paper, we present a novel approach and architecture that integrates advanced sensing, communications, and processing techniques to create such a cooperative AR system and demonstrate its capability with HoloLens and edge servers connected over a wireless network. Our research addresses the challenges of implementing a distributed cooperative AR system capable of capturing data from a multitude of sensors on HoloLens, performing fusion and accurate object recognition, and seamlessly projecting the reconstructed 3D model into the wearer’s field of view. The paper delves into the intricate architecture of the proposed cooperative AR system, detailing its distributed sensing and edge computing components, and the Apache Storm-integrated platform. The implementation encompasses data collection, aggregation, analysis, object recognition, and rendering of 3D models on the HoloLens, all in real-time. The proposed system enhances the AR experience while showcasing the vast potential of distributed edge computing. Our findings illustrate the feasibility and advantages of merging distributed cooperative sensing and edge computing to offer dynamic, immersive AR experiences, paving the way for new applications.

Among the most expensive and critical components in the transmit laser in a communication terminal are the multimode pump laser diodes used to amplify the laser signal for secure transmission through space. With multiple active laser components in each optical communication terminal, well-demonstrated reliability is a critical factor in the selection of the pump laser diodes. We present a comprehensive study on the qualification of high-power laser diode components for integration into satellite communications. We detail the design and execution of various tests, such as mechanical shock, thermal cycling, radiation exposure, and vacuum chamber trials, to mimic the challenges of space operation. The results of this study not only provide valuable information for the specific laser component under investigation but also contribute to the broader understanding of qualifying optical components for spaceborne systems.

Laser Power Transfer (LPT) can serve as a potential solution to powering solar cells that are out of contact with the sun. It also has the potential to be combined with communications through beam modulation. This research aimed to integrate LPT and communications into a dual-use system, thus decreasing the Size, Weight, and Power (SWaP) of a rover, which would in turn reduce its cost. The two main focuses of this research were to characterize data rate and power transfer to a solar cell through the modulation of a laser beam by comparing different modulation methods. An off-the-shelf monocrystalline solar cell detected 30kbps of LED modulation with a maximum loss in power of 5.5%, and it detected 2.7kbps of laser modulation with a maximum loss in power of 20.1%.