This PDF file contains the front matter associated with SPIE Proceedings Volume 11017, including the Title Page, Copyright information, Table of Contents, Author and Conference Committee lists.

Open System Architecture (OSA) is a buzzword in the commercial and government communities. The attraction of OSAs is their ability to enable interoperability and reduce the cost of procuring sensor systems. The Sensor Open Sensor Architecture (SOSA) Consortium is a consensus based community of government and industry partners working together to develop the next generation of sensors that have well-defined interfaces for software, hardware, and electrical/mechanical components. Having well-defined interfaces brings about system modeling and simulation opportunities that would otherwise not be possible with proprietary designs. This paper intro- duces a system modeling and simulation approach that takes advantage of the open interfaces the SOSA Consortium provides, with the end goal being to design a sensor system in software. This system modeling and simulation approach demonstrates the efficacy of designing future sensor systems using the SOSA Technical Standard by providing design examples for sensor systems and networks of sensor systems. Two different use cases are modeled and analyzed in this paper. The first use case demonstrates how a ground vehicle can transmit science data to a nearby sensor that can then forward the data to a ground station using the SOSA Technical Standard. The second use case demonstrates how a network of sensors can use the SOSA Technical Standard to interoperate and establish communication with one or multiple ground stations. Together, these two use cases provide a solid framework for demonstrating the effectiveness of the SOSA Technical Standard for sensor system applications, whereas, the system modeling and simulation approach demonstrates its ability to enable rapid testing of new designs and algorithms for various components of a sensor.

We report an image processing method which is able to break the diffraction limit for single frame images. If an image has a circularly symmetric point spread function (PSF), its spatial resolution can be enhanced by a new image processing method, the nearest neighbor pixel deconvolution (NNPD), as long as its pixel size is much smaller than distribution of the PSF. This method rewrites the PSF in terms of the nearest pixel groups, applies the Shift Theorem of the Fourier Transform to modify the PSF in the Fourier domain, and achieves a higher spatial resolution with the inverse Fourier Transform. The results of our experiment show that this method could enhance image resolution beyond the diffraction limit. We applied this method to two space images, higher resolution with some new features were shown in the processed images.

Compact satellite swarms enable future space mission concepts including cellular architecture, disaggregation, orbital construction, and cooperative inspection. Such swarms can cooperatively determine position and orientation (known as pose) of their constituent satellites by collecting and sharing relative pose measurements. This paper presents sensor selection strategies which reduce the number of computationally expensive vision-based pose measurements which must be collected while still maintaining accurate localization. The first method accounts for the impact of relative viewing geometry and solar angle on relative pose measurements. The second method uses uniformly sampled random spanning trees. The third method randomly selects pose measurements. Selected relative pose measurements are then fused in a Multiplicative Extended Kalman Filter for decentralized cooperative localization. Localization results using simulated measurement data and rendered stereo images are presented.

This paper presents a hidden chamber detector (HCD) using radio frequency (RF) signals’ penetration and reflection characteristics. The sensor of the chamber detector is a linear frequency modulated continuous wave (LFMCW) time division multiple access (TDMA) multiple input multiple output (MIMO) synthetic aperture radar (SAR). The basic idea of the sensor system is to scan the target wall and form a 2-D image of the wall with a high depth resolution and a proper angular resolution (cross range resolution). When there is no hidden chamber behind the wall, the radar will receive reflected signals from the room wall. While if there is a hidden chamber behind the room wall, there will be reflected signals from the room wall and the hidden chamber walls. Thus, the hidden chamber can be detected using the reflected singles from the chamber walls. The gated LFMCW TDMA MIMO SAR is configured with multiple transmitting antennas and multiple receiving antennas. Each transmitting and receiving antenna pair constructs an equivalent virtual array element. All virtual array elements construct the virtual antenna array (or synthetic aperture). The virtual array oversees the angular resolution along the array direction (vertical direction), and the narrow antenna beam is in charge of the angular resolution in the cross-array direction (horizontal direction). The high depth resolution is obtained using the gated LFMCW TDMA radar. Simulations show that the hidden chamber detector can detect chambers larger than 20cm by 20cm by 20cm at a distance of 0.3m away from the room wall.

Advancements in artificial intelligence, information communication, and systems design are potential for autonomous systems emerging for space situation awareness (SSA) architectures. Examples of architecture designs are autonomy in motion (AIM) for dynamic data assessment systems (e.g., robotics) and autonomy at rest (AAR) for static data collection systems (e.g., surveillance). However, there is a need for data architectures which are tailored to the SSA missions, which necessitates autonomy in use (AIU). AIU requires pragmatic use of message passing and data flow architectures, contextual and theoretic modeling, and user and information fusion. Information fusion provides methods for data aggregation, correlation, and temporal assessment and awareness. Together, AIU accesses the dynamic data for autonomy in change (AIC), information fusion from AAR in order to make AIM real-time decisions. The paper discusses issues for space situation awareness directions focusing on autonomy in use.

Space superiority requires space protection and space situational awareness (SSA), which rely on rapid and accurate space object behavioral and operational intent discovery. The presence of adversaries in addition to real-time and hidden information constraints greatly complicates the decision-making process in controlling both ground-based and spacebased surveillance assets. This paper develops and implements a solution called Adaptive Markov Inference Game Optimization (AMIGO) for rapid discovery of satellite behaviors. AMIGO is an adaptive feedback game theoretic approach. AMIGO gets information from sensors about the relations between the resident space objects (RSOs) of interest and ground and space surveillance assets (GSAs). The relations are determined by both the RSOs and GSAs. Therefore, AMIGO represents the situation as a game instead of a control problem. The game reasoning utilizes data level fusion, stochastic modeling/propagation, and RSO detection/tracking to predict the future RSOs-GSAs relations. The game engine also supports optional space pattern dictionary/semantic rules for adaptive transition matrices in the Markov game. If no existing pattern dictionary is available, AMIGO builds an initial one and revises it during the game reasoning. The outputs of the AMIGO reasoning include two kinds of control methods: processing of GSA measurements and localization of RSOs. The two sets form a game equilibrium, one for surveillance asset management and the other for the estimation of RSO behaviors. Numerical simulations and visualizations demonstrate the performance of AMIGO.

Reducing spacecraft and structure mass and volume can significantly reduce overall mission cost. The use of 3D printing provides one solution to this challenge. This paper analyzes ways to optimize support and infill structures by minimizing material usage while maintaining structural integrity to reduce the amount of material required to complete the 3D print and reduce overall cost. Specifically, a software method for generating 3D infill and support structures for 3D printing is proposed. The implementation of the method and the theory behind it are discussed. Results are presented from simulated and physical tests conducted on the method’s generated structures.

Developing onboard abilities to control and task unmanned vehicles (UxVs) and swarms of UxVs is key to both wider use of systems. Herein, the authors develop and apply a probabilistic reasoning framework for UxVs. The reasoning system considers tasks, in this case search and rescue, based on both prior knowledge and sensor feedback. The approach considered is an imperative program to generate situation de-scriptions and decision problems as probabilistic, declarative programs. This operation replaces human tasking of UxVs. Results indicate a significant decrease in swarm fuel usage when compared to manned tasking of assets for the same task..

Laser Communication (lasercom) systems whose beam paths traverse the Earth’s atmosphere, in whole or in part, are subject to the effects of optical turbulence: the random variation in the index of refraction due to small temperature variations. These effects include beam tilt, wander, and spreading, as well as scintillation: the variation in intensity across the receiver’s aperture plane. These effects can result in fades on the order of milli-seconds or longer, and so are important considerations in the design of lasercom links and systems. Stochastic analysis of optical turbulence has been developed since the 1940’s, but formal courses are still relatively rare, particularly courses for the professional lasercom engineer. Much of the literature has been devoted to the effects on imaging and directed energy systems, where analysis of mean effects often suffices. This differs considerably from lasercom, where low probability events, leading to fades, are important. The intent of this paper is to provide a concise but reasonably thorough tutorial on the mathematical theory regarding this subject, presenting the background necessary to analyze the effects of turbulence on lasercom systems. Measures for the correlation of phase and intensity across the receiver’s aperture will be developed, as well as the evolution in time. Analytic equations are only valid for mild turbulence, and so wave-optics simulation, a method for predicting the effects of moderate and strong turbulence, will also be discussed. In Part 2, we will consider specific lasercom geometries and architectures designed to mitigate fades due to turbulence.

Laser Communication (lasercom) systems whose beam paths traverse the Earth’s atmosphere, in whole or in part, are subject to the effects of optical turbulence: the random variation in the index of refraction due to small temperature variations. These effects include beam tilt, wander, and spreading, as well as scintillation: the variation in intensity across the receiver’s aperture plane. These effects can result in fades on the order of milli-seconds or longer, and so are important considerations in the design of lasercom links and systems. In Part 1 we developed the stochastic theory necessary to analyze the effects of turbulence on lasercom links. In Part 2 we analyze several specific link geometries, predicting the effects and discussing methods of mitigating fades associated with those effects. Specifically, we will look at the requirements for tip-tilt systems necessary in many systems to correct received beam tilt, and to pre-correct pointing for a transmitted beam. We will then consider how adaptive optics may benefit certain lasercom systems. The concept of “beamlets” will be introduced, and the benefits of mitigating the effects of turbulence on ground to space uplinks will be analyzed.

We have successfully tested 10 GHz bandwidth, uncooled, linear InGaAs optical receivers, coupled with a standard single mode fiber for proton and gamma rays. These devices find multiple applications in space for inter-satellite optical communication links, rapid Doppler shift lidar, as well as inter-planetary and Earth-to-Moon communication links. Nine InGaAs PIN photodiode and GaAs transimpedance amplifiers (TIA) were irradiated with 100 MeV protons with a fluence level of 1.6 × 10¹¹ cm^-2 corresponding to a total dose of 19.1 krad (water). Devices were also subjected to 30 MeV protons, six each with fluence levels of 4.9 × 10¹⁰ cm^-2 , 9.8 × 10¹⁰ cm^-2 , and 1.6 × 10¹¹ cm^-2 . Additionally, another nine InGaAs optical receivers were irradiated with 662 keV gamma rays, three devices each for a dose of 15 krad (water), 30 krad (water), and 50 krad (water). Pre- and post-radiation results were measured for (1) dark current vs. voltage for the InGaAs photodiodes, (2) responsivity (quantum efficiency) for the photodiodes, (3) optical return loss at 1550 nm wavelength, (4) drive current of the TIA, and (5) bandwidth of the PIN + TIA. All devices were found to be fully functional at the normal operating conditions and at room temperature.

Due to the increasing demand on bandwidth, and the demonstrated success of wireless optical communications in providing broadband applications, interest has grown into further expanding the deployment of wireless optical transmission. Lasers used in free space optical communication (FSOC) operate in optical bands that are not regulated, which shows an enormous advantage in terms of bandwidth. However, laser signals fading tends to occur due to atmospheric turbulence and many other environmental factors. Current methods for representation of laser propagation mainly focus on straightforward statistical models, where their parametrization has to be carried out from experimental data. The existing empirical models are typically obtained by using data collected by laser sensors. These sensors detect photons of light, which are capable of recording intensities of the laser beam at a certain rate. However, simple, common distributions, in some instances, cannot fully describe the dynamic of the received optical signals, especially in the battlefield scenarios that involve various terrain and weather conditions. They lack the generality and rigor of a basic physical-level formulation, i.e., a model specific for one application or scenario cannot be applied to any other case. To overcome the shortcoming of the aforementioned statistical models, a physics-based FSOC propagation is proposed to simulate and represent multipath effects properly and efficiently. In this paper, we consider a large number of factors that may affect the actual FSOC measurement including absorption, scattering, impact of weather, geometric loss, and optical turbulence, etc. The simulation results demonstrate that our proposed FSOC propagation model achieves high-fidelity prediction accuracy.

With the explosive growth of network communication technologies, nowadays global positioning system (GPS) successfully provides the worldwide navigation service for militaries and civilians. Sailors, aviators, and car drivers rely heavily on the accuracy of navigation and position estimation provided by GPS. Instead of using conventional radio frequency (RF) crosslinks, the reliability and efficiency of information transmissions can be significantly enhanced with the aid of optical crosslinks between satellites. In this paper, we develop a toolchain-based hybrid implementation (TBHI) by designing and integrating multiple platforms and software to evaluate the performance of the next-generation global position system (GPS) with potential configuration of optical transmission crosslinks between satellites. A distributed, multi-simulation tool chain is developed in both the front-end and back-end to conduct a real-time evaluation of optical crosslinks. A comprehensive assessment is provided for the receiving or transmission chain, along with traffic loading evaluation of satellite crosslinks from both physical layer and network layer emulation. To further evaluate the effectiveness of our developed TBHI, we investigate five base-line traffic models which can cover most applications in the satellite communication, including single-time transmission, periodical transmission, regular data transmission with randomness, and small data transmission. For each model, we specify a group of parameters that can determine the statistical distribution used to generate the traffic loading. Experiments using real-world traffic traces are used to evaluate the effectiveness of our proposed TBHI framework. Our simulation validates that it can effectively and accurately visualize the GPS satellite communications.

In satellite communication (SATCOM) system, a simple “bent-pipe” transponder is widely adopted to convert uplink carrier frequencies to downlink carrier frequencies for transmission of information without having on-board processing capability. The transponders are equipped with high power amplifiers (HPAs), which like other amplifier modules in communication systems, cause nonlinear distortions to transmitted signals, when HPAs are operated at or close to their saturation points to maximize power efficiency. These nonlinearities can be characterized as amplitude modulation-toamplitude modulation (AM-AM), and amplitude modulation-to-phase modulation (AM-PM) effects, which degrade the transmission performance of the system. Therefore, additional processing techniques such as predistortion (PD) has applied to maximize the transponder throughput along with the HPA power efficiency. In this paper, we first propose an accurate HPA modelling method, which leads to an outstanding agreement with the measured HPA AM-AM and AM-PM characteristics data. Then, a close-form PD is derived with respect to the power and phase compensation for the corresponding output signals of HPA. Finally, simulation results are provided to evaluate and verify the bit error rate (BER) improvement for the considered SATCOM system by applying our proposed PD technique.

In many military and law-enforcement covert missions, wireless communication links need to remain undetected. In this paper, a novel spectrum spread technique based on noise modulated (NM) transmission is proposed and a feasibility study was conducted. In NM transmissions a reference pseudo-noise signal is generated and is superposed with the information signal, with a time delay. The pseudo-random noise sequence is transmitted separately and used to recover the signal from the noise modulated information signal. The transmitted information signal is noise like making it difficult to detect or decode by an adversary. If an adversary does discover the transmission, decoding is difficult without the pseudo-random noise sequence and time delay between noise and signal. Conventional NM uses polarized antennas to orthogonally transmit the noise modulated information signal and pseudo-noise signal. However, the two polarized antennas are rarely, if ever, completely isolated in practice making signal recovery difficult if not impossible. In this paper a feasibility study was performed on a novel multi-frequency NM scheme for NM communications with a single polarized antenna. A universal software radio peripheral (USRP) software defined radio (SDR) testbed was used to demonstrate that multi-frequency NM transmission masks a QPSK signal from an adversary and the signal can be successfully recovered by a friendly receiver.

The OpenOrbiter I mission has changed significantly since its conceptualization, as the small spacecraft industry has evolved – rapidly – around it. The spacecraft design has iterated through multiple concepts as changing policies and technical realities prevented various initial design decisions from being brought to fruition. Communications licensing issues, power consumption and the physical footprint of a licensable radio and structural changes are among the many challenges that the project faced. The initial goals of the mission are reviewed and an update is provided on its current status as it heads through the final steps in preparation for launch. The mission concept and revised concept of operations are reviewed. In particular, changes to the power and communications systems are discussed. The new power system and communications systems and the impact that these have on the spacecraft and overall mission are presented and discussed in detail. The paper reviews the student learning that has occurred throughout the mission development process and the impact of the required changes and associated processes on the student learning mission objectives. It revisits the question of build versus buy, in light of the pathway taken (build), the challenges that have been encountered and the results. The paper extracts, from the program’s activities and results, lessons and best practices that can be applied by others within the small spacecraft domain. It concludes with a discussion of what the mission has demonstrated to-date, what will be demonstrated by on-orbit operations and the team’s plans for future work in this area.

Face recognition technology has been widely adopted in many mission-critical applications as a means of human identification, controlled admission, E-bank authentication, and mobile device access. Security surveillance is also a growing application for face recognition techniques; however, challenges exist from low resolution (LR) and high noise, multi-angle and multi-distance changes, and different light conditions. In comparison, algorithms applied to cell phone imagery or other specific camera devices mainly function on high resolution images with fixed angles and small changes of illumination. As face recognition in security surveillance becomes more important in the era of dense urbanization, it is essential to develop algorithms that are able to provide satisfactory performance in processing the video frames generated by low resolution surveillance cameras. In this paper, we propose a novel face recognition method that is suitable for low resolution surveillance cameras. The technique is demonstrated on a face dataset generated from real-world surveillance scenarios, from which an end-to-end approach is taken to match high resolution (HR) images with low resolution (LR) images from the surveillance video. The experimental results validate the effectiveness of the novel method that improves the accuracy of face recognition in surveillance security scenarios.

Connected societies require reliable measures to assure the safety, privacy, and security of members. Public safety technology has made fundamental improvements since the first generation of surveillance cameras were introduced, which aims to reduce the role of observer agents so that no abnormality goes unnoticed. While the edge computing paradigm promises solutions to address the shortcomings of cloud computing, e.g., the extra communication delay and network security issues, it also introduces new challenges. One of the main concerns is the limited computing power at the edge to meet the on-site dynamic data processing. In this paper, a Lightweight IoT (Internet of Things) based Smart Public Safety (LISPS) framework is proposed on top of microservices architecture. As a computing hierarchy at the edge, the LISPS system possesses high flexibility in the design process, loose coupling to add new services or update existing functions without interrupting the normal operations, and efficient power balancing. A real-world public safety monitoring scenario is selected to verify the effectiveness of LISPS, which detects, tracks human objects and identify suspicious activities. The experimental results demonstrate the feasibility of the approach.

Video Surveillance Systems (VSS) have become an essential infrastructural element of smart cities by increasing public safety and countering criminal activities. A VSS is normally deployed in a secure network to prevent the access from unauthorized personnel. Compared to traditional systems that continuously record video regardless of the actions in the frame, a smart VSS has the capability of capturing video data upon motion detection or object detection, and then extracts essential information and send to users. This increasing design complexity of the surveillance system, however, also introduces new security vulnerabilities. In this work, a smart, real-time frame duplication attack is investigated. We show the feasibility of forging the video streams in real-time as the camera’s surroundings change. The generated frames are compared constantly and instantly to identify changes in the pixel values that could represent motion detection or changes in light intensities outdoors. An attacker (intruder) can remotely trigger the replay of some previously duplicated video streams manually or automatically, via a special quick response (QR) code or when the face of an intruder appear in the camera field of view. A detection technique is proposed by leveraging the real-time electrical network frequency (ENF) reference database to match with the power grid frequency.

In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to automatically classify signal modulation more efficiently, which can further help in radio frequency modeling and pattern recognition problem solving. Three different approaches Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) have been deployed and evaluated in the signal modulation classification. In this paper, the signals for training and validation are generated using our MATLAB based RF signal generator, which can simulate various types of modulated signal according to the configuration specification. The numerical results show that CNN network can outperform the DNN and RNN in terms of the signal modulation classification accuracy.

With the development of modern information technology (IT), a smart grid has become one of the major components of smart cities. To take full advantage of the smart grid, the capability of intelligent scheduling and planning of electricity delivery is essential. In practice, many factors have an impact on electricity consumption, which necessitates information fusion technologies for a thorough understanding. For this purpose, researchers have investigated methodologies for collecting electricity consumption related information and variant multi-factor power consumption forecasting models. In addition, conducting a comprehensive analysis and obtaining an accurate evaluation of power consumption are the premise and basis for a more robust and efficient power grid design and transformation. Therefore, it is meaningful to explore forecasting models that are able to reflect the power consumption changes and internal relations within fusional information effectively. Making electricity consumption forecasting based on the neural network has been a popular research topic in recent years, and the back-propagation neural network (BPNN) algorithm has been recognized as a mature and effective method. In this paper, BPNN is adopted to forecast the electricity consumption using Pecan Street, a community with a relatively large-scale smart grid, as a case study, and takes multiple factors into account, such as weather condition, weekend and holidays. The influences of each factor have been evaluated for a deeper insight. We hope this work will inspire more discussion and further study to guide the design of future smart grids.

This paper presents a games-in-games approach to provide design guidelines for mosaic command and control that enables the secure and resilient multi-domain operations. Under the mosaic design, pieces or agents in the network are equipped with flexible interoperability and the capability of self-adaptability, self-healing, and resiliency so that they can reconfigure their responses to achieve the global mission in spite of failures of nodes and links in the adversarial environment. The proposed games-in-games approach provides a system-of-systems science for mosaic distributed design of large-scale systems. Specifically, the framework integrates three layers of design for each agent including strategic layer, tactical layer, and mission layer. Each layer in the established model corresponds to a game of a different scale that enables the integration of threat models and achieve self-mitigation and resilience capabilities. The solution concept of the developed multi-layer multi-scale mosaic design is characterized by Gestalt Nash equilibrium (GNE) which considers the interactions between agents across different layers. The developed approach is applicable to modern battlefield networks which are composed of heterogeneous assets that access highly diverse and dynamic information sources over multiple domains. By leveraging mosaic design principles, we can achieve the desired operational goals of deployed networks in a case study and ensure connectivity among entities for the exchange of information to accomplish the mission.