This PDF file contains the front matter associated with SPIE Proceedings Volume 12546, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

The emerging field of Space 2.0, which challenges the Space 1.0 practices used since Sputnik’s first launch, is moving these expectational goal posts. With changes occurring in this area, so too does the trade space that the Military must operate within. This presentation will discuss Space 1.0 and 2.0 attributes, how they may impact Multidomain communications in the future, and the wide array sensors being considered by the commercial sector.

Commercial Hyperspectral Imaging (HSI) systems capable of both fine spatial ground resolution and high signal to noise ratio pose challenges to the integration and testing process. The following will describe methods developed for a Low Earth Orbit (LEO) system having an effective focal length of approximately one meter and a ~f/2 optical speed. Internal subsystem testing is outlined as it relates to full-system performance. A sub-aperture testing routine is described to establish baseline performance estimates for spatial and spectral characteristics at a system level. Analysis and testing results are presented as measured during the integration process.

Hypersonic glide vehicles can glide several thousand kilometers through the Earth’s atmosphere at hypersonic Mach numbers. Their inherent characteristics make them difficult to detect by both ground-based and spacebased sensors. The Universitat der Bundeswehr M¨unchen aims to demonstrate the capability of the Multispectral Object Sensing by Artificial Intelligence-processed Cameras (MOSAIC) experiment to detect and track radiation signatures of hypersonic glide vehicles from Low Earth Orbit on-board the small-satellite ATHENE-1. The payload consists of a cryo-cooled mid-wave infrared sensor with a pixel count of more than 1MPx. The use of variable optics increases flexibility with regards to spatial and spectral resolution. Spectral filter selection is based on atmospheric simulations. A 25MPx color imager sensitive in the visible spectrum is implemented together with a fixed focal length lens for complementary cross validation of detection by postprocessing onground. Both imagers are processed on-board the satellite by a high-performance computer that accelerates artificial intelligence image processing methods. All components used are commercial-off-the-shelf components that are tested and modified for their application in space at the Universit¨at der Bundeswehr M¨unchen. Two test set-ups are developed to characterize the modulation transfer function of the optical systems. Besides an ISO 12233 approach in the visible spectrum, an infinite-conjugate back-illuminated set-up is implemented for the infrared system. This study outlines design drivers and their specific solution approaches for an early warning system for a small-satellite application. A brief analysis of the radiometric budget forms the basis for a further preliminary design. Finally, test set-ups for performance validation are outlined.

This paper outlines our approach to solving the amplitude modulation-to-amplitude modulation (AM-AM), and amplitude modulation-to-phase modulation (AM-PM) distortions caused by the onboard high-power amplifier (HPA) operating at the saturation point. The approach employs machine learning and artificial intelligence (ML-AI) to predistort the input signal such that the output of the post-HPA pre-distorted signal is identical to the original. The proposed ML-AI approach utilizes an existing MATLAB reinforcement learning technique using Deep Deterministic Policy Gradient (DDPG). The bulk of the research was to incorporate the proposed DDPG pre-distorter into the newly developed GNSS Single Side Band-Multi-Carrier Broadband Waveforms (SSB-MCBBW) and tune the pre-distorter’s hyper-parameters. The fine-tuning process was achieved efficiently by utilizing the parallel computing offered by a computer cluster at California State University Fullerton (CSUF) and has produced promising results in our simulated environment. The performance results of the proposed ML-AI pre-distorter using MATLAB DDPG algorithm are compared with the ideal pre-distorter for various HPA input back-off power (IPBO).

Rise of unmanned vehicles and autonomous robots has been accompanied by study of path planning, navigation, and decision-making algorithms. Current state-of-the-art employs deep neural nets to extract the required features. This technology, though successful in most cases, fails where the training has not been done for unseen conditions. For such unlabeled training data, transfer learning approaches have been proposed. A major drawback of using transfer learning approaches is that the actions and/or state spaces are reactive only to present circumstances. A truly intelligent autonomous operation has to consider a subordinate-to-a-human approach for its mission risks that vary with topography, path planning as well as mission goals. To address these complex combinatorial problems, DARPA has initiative a novel Explainable AI (XAI) technology in the past few years. In XAI, machine learning is paired with human intervention to make decisions by generating textual explanations of all the available relevant information / decision. In this paper, we propose to use available information along with human intelligence in a feedback loop for helping the unlabeled data to be trained and generate cost functions which were previously not programmed. We study this context/situation awareness to generate list of decision available from explanations on combinatorial tasks. Moreover, we employ this approach to a quadruped robot to learn its environment and the AI model starts in its infancy to mimic human cognitive architecture. We show that the learning process can be improved in a way that suits a particular mission in mind.

This paper details the design, fabrication, and development of an improved Nanosatellite Attitude Control Simulator (NACS). The NACS consists of a mock 1U CubeSat (MockSat), tabletop air-bearing, and automatic balancing system (ABS). The MockSat employs a reaction wheel array to exchange momentum with the rigidlyattached air bearing platform, and an inertial measurement unit to obtain orientation and angular velocity estimates. The ABS tunes the Simulator’s center of gravity to coincide with the air bearing’s center of rotation in an effort to minimize gravitational torques. This paper presents the majority of the mechanical design process, as well as future insights into the ABS control system. The NACS will be used to build numerous data sets for the development and training of new machine learning algorithms, as well as to benchmark, test, and compare different estimation and control strategies.

The rapid growth of the demand for mobile sensing makes it difficult to process all sensing tasks on a single mobile device. Therefore, the concept of distributed computing was proposed, in which the computation tasks are distributed to all available devices in the same edge network to achieve faster data processing. However, in some critical scenarios, in which the network condition of the edge is poor, the bandwidth of the edge network is limited, and the connection is unstable, which can significantly affect the performance of distributed computing. To overcome such issues, we propose a resilient mobile distributed computing framework adopting an integrated solution combining Coded Computing (CC) and Named Data Networking (NDN). With NDN, the network traffic and information sharing within the edge network is optimized dynamically to adapt to the timevarying network condition. The CC technique can recover some of the missing computation results when an edge node is failed or disconne

In wireless sensor networks, sensor nodes are deployed in open environments to gather and send collected data to a remote server. During the data transmission process, the collected data is vulnerable to security risks, making it crucial to encrypt the data to prevent it from falling into the wrong hands. This paper proposes a new approach that integrates homomorphic encryption (HE) algorithms into the sensor devices, allowing the data collected by the sensors to be encrypted before transmission. By utilizing HE in the sensor networks, the server can execute calculations on the encrypted data without requiring decryption. To implement the HE function, we use the Simple Encrypted Arithmetic Library (SEAL), which the Microsoft Cryptography Research Group developed. We evaluated the HE-based data collection scheme in IoT sensor networks with various network topologies to assess network lifetime, communication distance, and data collection latency.

This paper presents a proportional–integral–derivative (PID)-based automatic gain control (AGC) approach for satellite communications attacked by partial-time partial-band additive white Gaussian noise (AWGN) jamming. The analysis based on the stochastic model predictive control (SMPC) shows that the AGC performance depends on the accurate characterization of the jammed signal in the future time instants. However, such characterization is generally unavailable. To overcome the limitations of the existing AGC schemes without considering the future trend of the signal amplitude tracking errors (i.e., the difference between the average amplitude and the desired amplitude), the proposed approach uses the derivative term of signal amplitude tracking errors for anticipatory control and the integral term in the PID control to eliminate steady-state errors. Furthermore, different block sizes of the sampled signals are used for computing and selecting gain control values to achieve a good trade-off between fast response and robustness to noise/jamming. Extensive simulations of a system based on the typical satellite transponder link using Quadrature Phase Shift Keying (QPSK) modulated input signals and AWGN noise/jamming demonstrate that the proposed approach can achieve better control performance for maintaining the desired signal amplitude range and smaller bite error rate (BER) in the case of AWGN jamming, as compared with the existing AGC schemes.

The goal of space domain awareness (SDA) is to maintain the safety of communication and navigational processes within the space environment. This includes the tracking of space debris targets which possess high enough velocities to destroy satellites and jeopardize the lives of astronauts. Current technologies tasked with space debris monitoring employ large, quasi-monostatic arrays using S-band probe signals. To reduce cost, alleviate spectrum congestion, and expediate deployment, this paper proposes a self-mixing passive radar technique applied to multiple targets and sensors for identifying the Doppler signature from received signals. It was found that the self-mixing technique can isolate the Doppler signal in the single target case, and the Doppler signals along with Doppler difference signals in the multi-target and multisensor cases. In addition, a discrete, integer relationship exists between the Doppler signature at different receivers and is shown to occur when one of the receivers loses Doppler resolution.

Interference of satellite communications is a frequent and ongoing concern for both DoD and civilian enterprises. Geolocation of the interfering source is an essential step in mitigating or eliminating the interference and restoring the operation of the communications service. Doppler information is useful for the passive geolocation of ground-based EMI sources. This paper proposes a cross-correlation-based method to blindly estimate the EMI Doppler information from a single satellite. In the proposed blind estimation method, the carrier and waveforms of the EMI sources are relaxed. Simulated numerical results demonstrate the effectiveness and accuracy of cross-correlation-based blind Doppler estimation

Space debris in the 1-10 cm diameter regime presents a particular hazard to future operations in low Earth orbit (LEO). These objects are too small to be reliably detected by terrestrial radar or telescopes, but too large for their impacts to be mitigated with Whipple shielding. This paper addresses this hazard by presenting a methodology for space-based detection and active debris removal (ADR) via novel intelligent collaborative sensing and efficient decentralized data fusion. In simulation, we demonstrate that a small heterogeneous constellation of co-orbiting satellites using attentional neural networks can autonomously share information and maneuver in a cooperative game to de-orbit space debris (e.g. with a mechanical arm or pulsed laser) while minimizing total energy consumption and collision risk in the constellation. Particular focus is given to the robustness and effectiveness of the developed collaborative sensing system with respect to sensor uncertainties and transient changes in observability between nodes.

Earth observation satellites, such as those responsible for monitoring the effects of climate change, require rigorous calibration protocols to account for on-orbit sensor degradation. An increasingly dependable method to address this issue uses the Moon as a reference light source for in-situ calibration. The airborne lunar spectral irradiance (air-LUSI) mission aims to improve the utility of the Moon as an on-orbit calibration target for remote sensing instruments, by tying the currently accepted lunar model to the SI and establishing lunar irradiance on an absolute scale. To this end, air-LUSI collects SI-traceable measurements of lunar irradiance at visible to nearinfrared wavelengths with unprecedented accuracy. A non-imaging telescope is flown at an altitude of 21 km, aboard NASA’s high-altitude ER-2 aircraft, which places the instrument above 95% of the Earth’s atmosphere for clean, minimally obstructed lunar spectra. To fix the optical axis on the Moon during flight, an autonomous control system is required to compensate for aircraft motion and track the Moon across its celestial transit. In this paper, we present an overview of the robotic subsystem used to track the Moon on more than ten high-altitude flights, and the valuable lessons learned from those campaigns. From this insight, a preliminary design for a second-generation robotic telescope mount is presented. Referred to as the HAAMR, it will supplant the current robotics system on future air-LUSI Operational Flight Campaigns, with the nearest field deployment slated for January 2024. We show how this new system is poised to offer a more reliable, accurate, and responsive platform for the air-LUSI instrument to continue collecting data that will ultimately help to improve our understanding of the Earth’s climate.

This study presents the development of a new filter, the sequential sliding innovation filter (SSIF), designed for estimating quantities of interest from noisy measurements. The SIF is formulated in a sequential manner, allowing for multiple updates of estimates, making it well-suited for systems with multiple measured states. The filter is applied to an unmanned ground vehicle (UGV) maneuvering in 2-D path in this study, and the results demonstrate that the SSIF outperforms conventional filter and Kalman Filter (KF) in terms of accuracy and efficiency. The SSIF has the potential for use in signal processing, tracking, and surveillance, making it a valuable tool in various fields.

Sharjah-Sat-2 is a 6U Earth Observation (EO) CubeSat currently being developed by the Sharjah Academy for Astronomy, Space Sciences, and Technology (SAASST) and the University of Sharjah (UoS). The 6U CubeSat is currently being designed and integrated with two payloads on board: (1) a High-Resolution Hyperspectral Imager with less than 5 Meters of Ground Sampling distance (GSD) and (2) an experimental payload consisting of a GNSS receiver. The mission's primary scientific objective is to capture High-Resolution Hyperspectral images of the United Arab Emirates to provide a constant stream of reliable data that can be utilized to improve the country's infrastructure and resource management. The secondary mission objective is to monitor and validate the integrity of GNSS signals received by CubeSat and compare them to calibrated ground based GNSS receivers. This paper will provide insight into the GNSS Payload onboard Sharjah-Sat-2 and how its data could be utilized to measure and validate the signal integrity of the observed GNSS satellites. Also, we will compare the observations with those made at the groundbased reference GNSS station available at our research facility.

Sharjah-Sat-1 is the first CubeSat to be designed and integrated at the Sharjah Academy for Astronomy, Space Sciences and Technology (SAASST), a research institute under the University of Sharjah (UoS) in the United Arab Emirates, with an active collaboration with Istanbul Technical University and Sabanci University in Turkey. The mission is due to launch in December 2022. Sharjah-Sat-1 hosts a primary payload of an improved X-Ray Detector (iXRD). The iXRD utilizes a CdZnTe crystal as an active detector to detect and measure bright and hard X-Ray sources and a tungsten collimator. The instrument’s detection range is 20-200 KeV at a spectral resolution of 6 Kev at 60 KeV [1]. The detector will be able to measure the flux of ionizing x-ray around the south Atlantic anomaly, the data of which will be shared to provide space situational awareness for other satellite operators to perform any preventative maneuvers to protect their space assets. This paper will discuss how the improved X-Ray Detector (iXRD) on-board the Sharjah-Sat-1 CubeSat can be utilized to provide space situational awareness.

This paper discusses specific SRI CMOS imagers that use for optimization a relatively new imaging performance parameter called the Contrast Detection Figure of Merit (CDFM). The large backside illuminated (BSI) stitched imagers come in two primary formats: a NMOS 10 um pixel x Mk x Nk imager where M x N are whole numbers and a PMOS 4um quad pixel x 4k x 4k and 8k x 8k. We briefly describe their pixel design and performance in addition to comparing NMOS and PMOS technologies discussing their advantages and disadvantages. Next we describe the CDFM technique where we join TCAD device and Monte Carlo simulations which together produce the modulation transfer function (MTF) and Quantum Efficiency (QE) as a function of photon wavelength, silicon epitaxial (epi) thickness and substrate bias given the pixel size. The two parameters are then multiplied together producing CDFM. The results show that quantum efficiency (QE) and the modulation transfer function (MTF) are competing variables which lead to an optimum epi thickness for maximum CDFM and contrast signal to noise (CSN) performance. The CDFM curves can also be expanded to MTFxQExBB where BB is the working Black Body radiation spectrum (BB) leading us to absolute performance given the strength of incoming radiation as a function of wavelength. Lastly, the paper compares CDFM modeling and testing results using standard measurement techniques (MTF, QE and Photon Transfer).

Here we present an original primarily CH4 and secondarily CO2 detection payload, based on an optically simple, compact, light and low energy consumption architecture, which is compatible with integration in a small cubesat of <2U size or with a typical microsatellite secondary payload. It is based on a custom multispectral optical filter working in the spectral range 1.6-1.7 μm integrated with a commercial lens and InGaAs detector. The filter contains 5 bands of 10 nm fwhm each, best trade-off between fine spectral resolution and robust SNR for a typical micro/nanosatellite capacity. The filter has one band for the CO2 detection and two bands for CH4 detection, they have been chosen to maximize each gas absorption in the SWIR range 1600-1700 nm. An original albedo retrieval method has been developed, based on a linear interpolation between the 2 albedo bands, spectrally closely correlated to the CH4 and CO2 bands.

Single sensor, such as 3D LiDAR camera, has relatively limited perception performance of providing comprehensive environmental information though the perception results from single sensor is accurate. Therefore, multiple sensors are perferred for surveillant tasks in either tactical or civilian scenarios. Cooperative perception is one of the solution to enable sensors to share sensory information with other sensors and infrastructure, extending coverage and enhancing the detection accuracy of surrounding objects for better safety and path planning. However, an efficient management of the large volume of sensory data across multiple sensors in the wirelss network is needed to maintain real-time sensing. In this work, we design a complete cooperative perception framework with varies networking, image processing and data fusion technologies integrated to enhance the situational awareness performance with multiple sensors. The framework uses information-centric networking and deep reinforc

The scarcity and finite nature of the wireless spectrum drives technology development for spectrum utilization. With the increased complexity of the radio-access environment and susceptibility to interference disruption, challenges exist which demand advanced interference suppression techniques. Recently, the advance of artificial intelligence (AI) promotes technology for data-driven modeling of complicated relationships, which provides numerous tools and techniques for signal processing and analysis. This paper develops a deep learning-based radio signal interference suppression method by leveraging the adaptive features and Convolutional Neural Network (CNN) based Denoising autoencoder (DAE). By simulating the communication system with stochastic channel effects (AWGN channel), the proposed Suppression of Interference DEA (S-IDEA) method is validated using the original signals and the corrupted signals through channel effects. The results show that S-IDEA can effectively perform interference suppression from AWGN channel at different SNR levels and achieve excellent SNR improvement.

State estimation strategies play an essential role in the effective operation of dynamic systems by extracting relevant information about the system’s state when faced with limited measurement capability, sensor noise, or uncertain dynamics. The Kalman filter (KF) is one of the most commonly used filters and provides an optimal estimate for linear state estimation problems. However, the KF lacks robustness as it does not perform well in the face of modelling uncertainties and disturbances. The sliding innovation filter (SIF) is a newly proposed filter that uses a switching gain and innovation term, and unlike the KF, it only results in a sub-optimal estimate. However, the SIF has been proven to be robust to modelling uncertainties, disturbances, and ill-conditioned problems. In this work, we propose an adaptive SIF and KF (SIF-KF) estimation algorithm that can detect faulty or uncertain conditions and switch between the KF and SIF gain in the absence or presence of such conditions, respectively. A fault detection mechanism based on the normalized innovation squares (NIS) metric is also presented, which is responsible for triggering the activation of the respective gain in the proposed SIF-KF strategy. Experimental simulations are carried out on a simple harmonic oscillator subject to a fault to demonstrate the proposed SIF-KF’s effectiveness over traditional approaches.

It is crucial to investigate how the channel influences communication in a satellite environment, especially under changing environmental circumstances. Only in conditions of channel variability the benefits of the Adaptive Coding and Modulation (ACM) and the related dynamic adaptations of the different Modulation&Codings (MODCODs) in a Digital Video Broadcasting (DVB) standard can be understood. In this study, the model of a satellite channel that incorporates rain attenuation is the main topic of discussion. Our modeling approach is based on a model proposed by International Telecommunication Union (ITU) and was developed using Matlab tool. It is composed of two parts: the first allows for the extraction of the state transition matrix for a given longitude and latitude using a Markov chains approach; the second, using the previously extracted matrices, tries to discover a closed formula for the matrix probabilities as functions of latitude and longitude through the polynomial regression technique.

In this work, a well-known mechanism that is referred to as piston lever mechanism is design to control the wing’s flange. The design targets the mechanism components and their location while maintaining the minimum oil level required to lift the flanges from 0 to 45 degrees. The design is considered a multi-objective optimization problem; in which we propose the Giza Pyramid Algorithm (GPA) to optimize. GPA is a newly developed metaheuristic technique of type ancientinspired that was developed in 2021. The GPA shall obtain the best design while satisfying four constrains. The algorithm will be tested in Monte Carlo simulation to show its effectiveness in terms of stability and robustness. The performance is then compared to the particle swarm optimization. The proposed method shows a superior result compared to the PSO. Keywords: Giza pyramid, metaheuristic, optimization, design.