This PDF file contains the front matter associated with SPIE Proceedings Volume 8752 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Research experience has shown that it is expensive to design, build and test a RF/microwave system that is comprised of various RF/microwave components. In order to mitigate the problem, High-Fidelity Modeling and Simulation (HFM&S) is a practical approach that includes high-fidelity behavioural model (HFBM) of the receiving system and scenarios embedded with different Concept of Operations (CONOPS). HFM&S is also an essential way to develop receiving system specifications that can be used for system validation and verification. This paper presents the HFBM of a wideband digital receiver using Matlab/Simulink® and the RF Toolbox™/SimRF™, and the use of the model to build a multi-channel receiving system that has a linear antenna array. The receiving system is installed on an UAV to intercept a ground-based emitter signal in a scenario that is built in Systems Tool Kit®. Through the design and build of the UAV receiver and its deployment in a scenario, this paper demonstrates the following: what is meant by HFBM of a system and how it can simulate real hardware; how signal integrity in the HFM&S can be and should be retained; why amplitude and phase are important for signal waveform level M&S; why the modern high performance computing technology should be used for signal waveform level M&S; an

The Environmental Awareness for Sensor and Emitter Employment (EASEE) software, being developed by the U. S. Army Engineer Research and Development Center (ERDC), provides a general platform for predicting sensor performance and optimizing sensor selection and placement in complex terrain and weather conditions. It incorporates an extensive library of target signatures, signal propagation models, and sensor systems. A flexible object-oriented design supports efficient integration and simulation of diverse signal modalities. This paper describes the integration of modeling capabilities for radio-frequency (RF) transmission and radar systems from the U. S. Navy Electromagnetic Propagation Integrated Resource Environment (EMPIRE), which contains nearly twenty different realistic RF propagation models. The integration utilizes an XML-based interface between EASEE and EMPIRE to set inputs for and run propagation models. To accommodate radars, fundamental improvements to the EASEE software architecture were made to support active-sensing scenarios with forward and backward propagation of the RF signals between the radar and target. Models for reflecting targets were defined to apply a target-specific, directionally dependent reflection coefficient (i.e., scattering cross section) to the incident wavefields.

While conducting a cutting-edge research in a specific domain, we realize that (1) requirements clarity and correctness are crucial to our success [1], (2) hardware is hard to change, most work is in software requirements development, coding and testing [2], (3) requirements are constantly changing, so that configurability, reusability, scalability, adaptability, modularity and testability are important non-functional attributes [3], (4) cross-domain knowledge is necessary for complex systems [4], and (5) if our research is successful, the results could be applied to other domains with similar problems. In this paper, we propose to use model-driven requirements engineering (MDRE) to model and guide our requirements/development, since models are easy to understand, execute, and modify. The domain for our research is Electronic Warfare (EW) real-time ultra-wide instantaneous bandwidth (IBW¹) signal simulation. The proposed four MDRE models are (1) Switch-and-Filter architecture, (2) multiple parallel data bit streams alignment, (3) post-ADC and pre-DAC bits re-mapping, and (4) Discrete Fourier Transform (DFT) filter bank. This research is unique since the instantaneous bandwidth we are dealing with is in gigahertz range instead of conventional megahertz.

This paper describes the development, capabilities and utility of the NASA Operational Simulator (NOS), a generic software-only simulation architecture developed for NASA missions. NOS was developed by the NASA’s Independent Verification and Validation (IV&V) Independent Test Capability (ITC) team and is primarily utilized by software developers and (independent) testers to verify the functionality of a spacecraft’s flight software from a system-wide perspective. NOS was initially developed in support of a software-only simulator for the Global Precipitation Measurement (GPM) mission to support verification and validation activities for NASA’s IV&V Program. Due to the successes of the GPM simulator (GO-SIM), the NOS architecture is being reused to develop a simulation environment in support of the James Webb Space Telescope (JWST). While NOS has primarily been utilized on NASA missions, its generic architecture can be easily applied across domains to support V&V of complex systems.

An artificial intelligence-controlled robot (AICR) operating in close proximity to humans poses risk to these humans. Validating the performance of an AICR is an ill posed problem, due to the complexity introduced by the erratic (noncomputer) actors. In order to prove the AICR’s usefulness, test cases must be generated to simulate the actions of these actors. This paper discusses AICR’s performance validation in the context of a common human activity, moving through a crowded corridor, using test cases created by an AI use case producer. This test is a two-dimensional simplification relevant to autonomous UAV navigation in the national airspace.

Fighter pilots are exposed to the risk of getting hit by enemy fire when flying missions with ground-to-air threats. A tactical support system including a survivability model could aid the pilot to assess and handle this risk. The survivability model presented here is a Markov model with five states; Undetected, Detected, Tracked, Engaged and Hit. The output from the model is the probabilities that the aircraft is in these states during the mission. The enemy's threat systems are represented with sensor and weapon areas and the transitions between the states depend on whether or not the aircraft is within any of these areas. Contrary to previous work, the model can capture the behaviors that the enemy's sensor systems communicate and that the risk of getting hit depends on the enemy's knowledge regarding the aircraft's kinematics. The paper includes a discussion regarding the interpretation of the states and the factors that influence the transitions between the states. Further developments are also identified for using the model to aid fighter pilots and operators of unmanned aerial vehicles with planning and evaluating missions as well as analyzing the situation during flight.

Free Space Optical (FSO) wireless data links are attractive alternatives to RF communications. This technology could enable vision around corners or barriers, and allow covert, secure, and wireless distribution of scope images to other squad helmet mounted displays (HMDs), with minimal additional hardware to the current equipment. A major design challenge for FSO links in personnel applications is ensuring line of sight (LoS) between transmitter and receiver. This work captures warfighter helmet and gun movement using video motion tracking in a typical application for FSO data links. A method to simulate transmitter and receiver on the warfighter helmet and gun scope and analyze LoS and FOV is presented. This method allows optimization of FSO data link placement and provides requirements for future FSO technology. The initial results suggest that to meet 100% of the threshold requirements, the vertical FOV of a receiver must be 80° and the horizontal FOV must be 60° and oriented 10° in pitch and -7.5° in yaw. Simulating a FSO link with horizontal and vertical FOV of 60° shows expected performance using a visual method from a helmet mounted camera. Additionally, the FOV of the transmitter and receiver can be visualized with arbitrary FOV, position, and orientation.

Unmanned Aircraft Systems (UAS) have been used in many military and civil applications, particularly surveillance. One of the best ways to use the capacity of a UAS imaging system is by constructing a mosaic or panorama of the recorded video. This paper presents a novel algorithm for the construction of super-resolution mosaicking. The algorithm is based on the Conjugate Gradient (CG) method. Geman -McClure prior is used together with four different cliques to deal with the ill-conditioned inverse problem and to preserve edges. We present the results with synthetic and real UAS surveillance data, resulting in a great improvement of the visual resolution. For the case of synthetic images, we obtained a PSNR of 47.0 dB, as well as a significant increase in the details visible for the case of real UAS frames in only ten iterations.

EM Photonics has been investigating the application of massively multicore processors to a key problem area: Computational Fluid Dynamics (CFD). While the capabilities of CFD solvers have continually increased and improved to support features such as moving bodies and adjoint-based mesh adaptation, the software architecture has often lagged behind. This has led to poor scaling as core counts reach the tens of thousands. In the modern High Performance Computing (HPC) world, clusters with hundreds of thousands of cores are becoming the standard. In addition, accelerator devices such as NVIDIA GPUs and Intel Xeon Phi are being installed in many new systems. It is important for CFD solvers to take advantage of the new hardware as the computations involved are well suited for the massively multicore architecture. In our work, we demonstrate that new features in NVIDIA GPUs are able to empower existing CFD solvers by example using AVUS, a CFD solver developed by the Air Force Research Labratory (AFRL) and the Volcanic Ash Advisory Center (VAAC). The effort has resulted in increased performance and scalability without sacrificing accuracy. There are many well-known codes in the CFD space that can benefit from this work, such as FUN3D, OVERFLOW, and TetrUSS. Such codes are widely used in the commercial, government, and defense sectors.

Over the last three decades, CPUs have continued to produce large performance improvements from one generation to the next. However, CPUs have recently hit a performance wall and need parallel computing to move forward. Parallel computing over the next decade will become increasingly defined by heterogeneous computing, involving the use of accelerators in addition to CPUs to get computational tasks done. In order to use an accelerator, software changes must be made. Regular x86-based compilers cannot compile code to run on accelerators without these needed changes. The amount of software change required varies depending upon the availability of and reliance upon software tools that increase performance and productivity. Writing software that leverages the best parallel computing hardware, adapts well to the rapid pace of hardware updates, and minimizes developer muscle is the industry’s goal. OpenCL is the standard around which developers are able to achieve parallel performance. OpenCL itself is too difficult to program to receive general adoptions, but productive high-performing software libraries are becoming increasingly popular and capable in delivering lasting value to user applications.

In this paper, we aim to quantify the performance gains of dynamic parallelism. The newest version of CUDA, CUDA 5, introduces dynamic parallelism, which allows GPU threads to create new threads, without CPU intervention, and adapt to its data. This effectively eliminates the superfluous back and forth communication between the GPU and CPU through nested kernel computations. The change in performance will be measured using two well-known clustering algorithms that exhibit data dependencies: the K-means clustering and the hierarchical clustering. K-means has a sequential data dependence wherein iterations occur in a linear fashion, while the hierarchical clustering has a tree-like dependence that produces split tasks. Analyzing the performance of these data-dependent algorithms gives us a better understanding of the benefits or potential drawbacks of CUDA 5’s new dynamic parallelism feature.

Military operations are executed in an increasingly complex electromagnetic environment. Over the last decade, All nations focused on improving their signal intelligence capabilities which has significantly improved intelligence support to EW. Today, there are many evolving facets of electromagnetic battlespace that include not only Electronic Attack (EA) but also improved situational awareness via information sharing and optimal positioning of Electronic Support (ES) systems.The aim is to use EW resources more intelligently in the battlespace and, by extension, improve their effectiveness. Another benefit is to provide tactical commanders with a timelier and accurate Common Operational Picture (COP) of the electromagnetic battlespace. In this study, the importance of controlling EM spectrum will be explained. The platforms and weapon systems which are used and being planned to use in the future for EM environment will be expressed. The main purpose is to help the planners who will execute the EW plan regarding to EM spectrum. The alghoritma can give planners to dynamic COP with effected sectors from enemy's EA platforms and systems. So that, planners can transmit it to friendly platforms at the same time. In addition, they can get the all needed informations related to enemy. In this way planners can easily send a message to friendly EA platforms to change the sector in order to affect the enemy. For all these reasons, it should have authority to give an order to friendly platforms about changing sector and frequency as well.

The reticle systems which are considered as the classical approach for determining the angular position of radiating targets in infrared band are widely used in early generation surface-to-air and air-to-air infrared guided missile seekers. One of the cost-effective ways of protecting aircrafts against these missiles is to dispense flare decoys from the countermeasure dispensing system (CMDS) integrated into the aircraft platform. Although this counter-measuring technique seems very simple, if not optimized carefully, it may not be effective for protecting the aircraft. Flares should be dispensed in accordance with a specific dispensing program which determines the number of flares to be dispensed from each dispenser of the CMDS and timing sequence of dispensing. Optimizing the parameters of the dispensing program is not trivial. It requires a good understanding of the operating principle of the threat seeker, operational capabilities of own platform and engagement scenario between them. In the present paper, we propose a complete simulation-based procedure to form an effectiveness boundary of flare dispensing programs against the spin-scan and conical-scan reticle seekers. The region of effectiveness is determined via Gaussian mixture models. The raw data is collected via extensive number of simulations using a MATLAB-coded simulator which models reticle-based seeker, aircraft radiation, aircraft motion, aircraft CMDS system, flare motion and flare radiation.

Guided projectile engagement scenarios often involve impact area constraints, in which it may be less desirable to incur miss distance on one side of a target or within a specified boundary near the target area. Current projectile guidance schemes such as impact point predictors cannot handle these constraints within the guidance loop, and may produce dispersion patterns that are insensitive to these constraints. In this paper, a new projectile guidance law is proposed that leverages real-time Monte Carlo impact point prediction to continually evaluate the probability of violating impact area constraints. The desired aim point is then adjusted accordingly. Real-time Monte Carlo simulation is enabled within the feedback loop through use of graphics processing units (GPU’s), which provide parallel pipelines through which a dispersion pattern can routinely be predicted. The result is a guidance law that can achieve minimum miss distance while avoiding impact area constraints. The new guidance law is described and formulated as a nonlinear optimization problem which is solved in real-time through massively-parallel Monte Carlo simulation. An example simulation is shown in which impact area constraints are enforced and the methodology of stochastic guidance is demonstrated. Finally, Monte Carlo simulations are shown which demonstrate the ability of the stochastic guidance scheme to avoid an arbitrary set of impact area constraints, generating an impact probability density function that optimally trades miss distance within the restricted impact area. The proposed guidance scheme has applications beyond smart weapons to include missiles, UAV’s, and other autonomous systems.

The Environmental Awareness for Sensor and Emitter Employment (EASEE) software models the impacts of terrain and weather on a diverse range of battlefield sensing systems. The goal is to provide mission planning tools that realistically capture complex environmental factors impacting sensor performance, yet are simple enough for users with little specialized training. This paper describes incorporation of infrared (IR) modeling into EASEE, and the subsequent challenges of supporting imaging sensors within a framework that had previously evolved primarily for non-imaging sensors, such as acoustic and seismic. The design requires independently interchangeable modules for signature generation, propagation, and signal processing. Sensor performance metrics, such as probability of detection, are characterized statistically rather than through simulation of actual images. Some key enhancements needed to support imaging sensors were: (1) geometric models for targets, (2) packaging of multiple attributes representing target image properties (radiance, projected area, and spatial spectrum), (3) explicitly distinguishing between signals for the background, target of interest, and nuisance targets, and (4) calculation of apparent temperature differences (as opposed to incoherent energy summation). Target signatures are generated using MuSES (Multi-Service Electro-optic Signature), whereas the IR background properties are generated using FASST (Fast All-Season Soil STrength) and numerical weather prediction models. Propagation is handled primarily with MODTRAN (MODerate resolution atmospheric TRANsmission), although simpler models such as a line-of-sight calculation can also be employed. The Johnson criteria were added to the available library of detection algorithms.

We present a framework for Bayesian analysis of test articles. For test articles comprised of two datasets, we derive analytical distribution models from the histograms of datasets. We introduce a novel alternative to traditional hypothesis testing that compares the analytical Cumulative Density Functions (CDF). This comparison between two datasets yields the probability that the two datasets are equivalent. If the test articles are two simulations, we derive trigonometric polynomial Data Models of the output and the inputs. Principal component analysis (PCA) reduces the number of modes used to reconstruct the simulation input or output to only the significant contributor. Kolmogorov-Gabor (KG) polynomial Data Models of the reduced mode simulation output are derived as a function of the reduced set of simulation input modes. These KG Data Models are analyzed to determine critical, sensitive, and key parameters. When the simulations have similar structure or when the range, standard deviation, and expectation are within similar ranges, the simulations are labeled as similar. If the test articles consist of a dataset and a simulation, the output from the simulation is first recorded as a dataset and compared to the second dataset test article using the CDF method.

The method of constructing a dynamic and self-adaptive virtual network is suggested to puzzle adversaries, delay and divert attacks, exhaust attacker resources and collect attacking information. The concepts of Honeypot and Honeyd, which is the frame of virtual Honeypot are introduced. The techniques of network scanning including active fingerprint recognition are analyzed. Dynamic virtual network system is designed and implemented. A virtual network similar to real network topology is built according to the collected messages from real environments in this system. By doing this, the system can perplex the attackers when Hackers attack and can further analyze and research the attacks. The tests to this system prove that this design can successfully simulate real network environment and can be used in network security analysis.