This PDF file contains the front matter associated with SPIE Proceedings Volume 10184 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.
Unmanned aerial systems (UAS) are increasing in flight times, ease of use, and payload sizes. Detection, classification, tracking, and neutralization of UAS is a necessary capability for infrastructure and facility protection. We discuss test and evaluation methodology developed at Sandia National Laboratories to establish a consistent, defendable, and unbiased means for evaluating counter unmanned aerial system (CUAS) technologies. The test approach described identifies test strategies, performance metrics, UAS types tested, key variables, and the necessary data analysis to accurately quantify the capabilities of CUAS technologies. The tests conducted, as defined by this approach, will allow for the determination of quantifiable limitations, strengths, and weaknesses in terms of detection, tracking, classification, and neutralization. Communicating the results of this testing in such a manner informs decisions by government sponsors and stakeholders that can be used to guide future investments and inform procurement, deployment, and advancement of such systems into their specific venues.
Instantaneous personnel location presents a challenge in Department of Defense applications where high levels of security restrict real-time tracking of crew members. During emergency situations, command and control requires immediate accountability of all personnel. Current radio frequency (RF) based indoor positioning systems can be unsuitable due to RF leakage and electromagnetic interference with sensitively calibrated machinery on variable platforms like ships, submarines and high-security facilities. Infrared light provide a possible solution to this problem. This paper proposes and evaluates an indoor line-of-sight positioning system that is comprised of IR and high-sensitivity CMOS camera receivers. In this system the movement of the LEDs is captured by the camera, uploaded and analyzed; the highest point of power is located and plotted to create a blueprint of crewmember location. Results provided evaluate accuracy as a function of both wavelength and environmental conditions. Research will further evaluate the accuracy of the LED transmitter and CMOS camera receiver system. Transmissions in both the 780 and 850nm IR are analyzed.
Pattern recognition, a branch of machine learning, involves classification of information in images, sounds, and other digital representations. This paper uses pattern recognition to identify which kind of ammunition was used when a bullet was fired based on a carefully constructed set of gunshot sound recordings. To do this task, we show that texture features obtained from the wavelet transform of a component of the gunshot signal, treated as an image, and quantized in gray levels, are good ammunition discriminators. We test the technique with eight different calibers and achieve a classification rate better than 95%. We also compare the performance of the proposed method with results obtained by standard temporal and spectrographic techniques
In this work, we describe a rapid-innovation challenge to combat and deal with the problem of internal, insider physical threats (e.g., active shooters) and associated first-responder situation awareness on military installations. Our team’s research and development effort described within focused on several key tech development areas: (1) indoor acoustical gunshot detection, (2) indoor spatial tracking of first responders, (3) bystander safety and protection, (4) two-way mass alerting capability, and (5) spatial information displays for command and control. The technological solutions were specifically designed to be innovative, low-cost, and (relatively) easy-to-implement, and to provide support across the spectrum of possible users including potential victims/bystanders, first responders, dispatch, and incident command.
Shooter localization systems have been subject of a growing attention lately owing to its wide span of possible applications, e.g., civil protection, law enforcement, and support to soldiers in missions where snipers might pose a serious threat. These devices are based on the processing of electromagnetic or acoustic signatures associated with the firing of a gun. This work is concerned with the latter, where the shooter’s position can be obtained based on the estimation of the direction-of-arrival (DoA) of the acoustic components of a gunshot signal (muzzle blast and shock wave). A major limitation of current commercially available acoustic sniper localization systems is the impossibility of finding the shooter’s position when one of these acoustic signatures is not detected. This is very likely to occur in real-life situations, especially when the microphones are not in the field of view of the shockwave or when the presence of obstacles like buildings can prevent a direct-path to sensors. This work addresses the problem of DoA estimation of the muzzle blast using a planar array of sensors deployed in a drone. Results supported by actual gunshot data from a realistic setup are very promising and pave the way for the development of enhanced sniper localization systems featuring two main advantages over stationary ones: (1) wider surveillance area; and (2) increased likelihood of a direct-path detection of at least one of the gunshot signals, thereby adding robustness and reliability to the system.
The identification of important nodes is a ubiquitous problem in the analysis of social networks. Centrality indices (such as degree centrality, closeness centrality, betweenness centrality, PageRank, and others) are used across many domains to accomplish this task. However, the computation of such indices is expensive on large graphs. Moreover, evolving graphs are becoming increasingly important in many applications. It is therefore desirable to develop on-line algorithms that can approximate centrality measures using memory sublinear in the size of the graph. We discuss the challenges facing the semi-streaming computation of many centrality indices. In particular, we apply recent advances in the streaming and sketching literature to provide a preliminary streaming approximation algorithm for degree centrality utilizing CountSketch and a multi-pass semi-streaming approximation algorithm for closeness centrality leveraging a spanner obtained through iteratively sketching the vertex-edge adjacency matrix. We also discuss possible ways forward for approximating betweenness centrality, as well as spectral measures of centrality. We provide a preliminary result using sketched low-rank approximations to approximate the output of the HITS algorithm.
Computer security vulnerabilities span across large, enterprise networks and have to be mitigated by security engineers on a routine basis. Presently, security engineers will assess their “risk posture” through quantifying the number of vulnerabilities with a high Common Vulnerability Severity Score (CVSS). Yet, little to no attention is given to the length of time by which vulnerabilities persist and survive on the network. In this paper, we review a novel approach to quantifying the length of time a vulnerability persists on the network, its time-to-death, and predictors of lower vulnerability survival rates. Our contribution is unique in that we apply the cox proportional hazards regression model to real data from an operational IT environment. This paper provides a mathematical overview of the theory behind survival analysis methods, a description of our vulnerability data, and an interpretation of the results.
The present article discusses novel improvements in nonlinear signal processing made by the prime algorithm developer, Dr. Albert H. Nuttall and co-authors, a consortium of research scientists from the Naval Undersea Warfare Center Division, Newport, RI. The algorithm, called the Nuttall-Wiener-Volterra or 'NWV' algorithm is named for its principal contributors , ,[ 3] . The NWV algorithm significantly reduces the computational workload for characterizing nonlinear systems with memory. Following this formulation, two measurement waveforms are required in order to characterize a specified nonlinear system under consideration: (1) an excitation input waveform, x(t) (the transmitted signal); and, (2) a response output waveform, z(t) (the received signal). Given these two measurement waveforms for a given propagation channel, a 'kernel' or 'channel response', h= [h0,h1,h2,h3] between the two measurement points, is computed via a least squares approach that optimizes modeled kernel values by performing a best fit between measured response z(t) and a modeled response y(t). New techniques significantly diminish the exponential growth of the number of computed kernel coefficients at second and third order and alleviate the Curse of Dimensionality (COD) in order to realize practical nonlinear solutions of scientific and engineering interest.
We present a new microwave photonic signal processor architecture, where a thin film attenuating counter propagating optical phase locked loop (ACP-OPLL) is integrated with a novel finite impulse response (FIR) optical phase modulator/ processors. The realization of the processor requires heterogeneous photonic integration between LiNbO3 thin film electro-optic modulators, SiN ultra-low loss optical waveguides, and III-V high power photodetectors. The microwave photonic signal processor architecture can perform analog signal processing functions with a superior dynamic range performance and frequency response.
Knowledge of the distance between a ship and other objects on the horizon has important implications for safety on the open ocean. Determining this distance can be challenging. RADAR sensors, which represent the standard solution to this problem, require a great deal of cost and power. A structure-from-motion computer vision technique is presented here to integrate known camera motion and a single pixel track on an imaged object to estimate range to that object. This approach leverages inherent wave motion of the camera platform along with a global exponentially stable observer to incrementally converge upon a range estimate. This method is detailed herein, and presented with both simulated and real-world results. This provides a passive and inexpensive solution to the open ocean ranging problem.
In this paper, an advanced wireless mobile collaborative sensing network will be developed. Through properly combining wireless sensor network, emerging mobile robots and multi-antenna sensing/communication techniques, we could demonstrate superiority of developed sensing network. To be concrete, heterogeneous mobile robots including unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) are equipped with multi-model sensors and wireless transceiver antennas. Through real-time collaborative formation control, multiple mobile robots can team the best formation that can provide most accurate sensing results. Also, formatting multiple mobile robots can also construct a multiple-input multiple-output (MIMO) communication system that can provide a reliable and high performance communication network.
The introduction of the System-on-Chip (SoC) technology has brought exciting new opportunities for the development of smart low cost embedded systems spanning a wide range of applications. Currently available SoC devices are capable of performing high speed digital signal processing tasks in software while featuring relatively low development costs and reduced time-to-market. Unmanned aerial vehicles (UAV) are an application example that has shown tremendous potential in an increasing number of scenarios, ranging from leisure to surveillance as well as in search and rescue missions. Video capturing from UAV platforms is a relatively straightforward task that requires almost no preprocessing. However, that does not apply to audio signals, especially in cases where the data is to be used to support real-time decision making. In fact, the enormous amount of acoustic interference from the surroundings, including the noise from the UAVs propellers, becomes a huge problem. This paper discusses a real-time implementation of the NLMS adaptive filtering algorithm applied to enhancing acoustic signals captured from UAV platforms. The model relies on a combination of acoustic sensors and a computational inexpensive algorithm running on a digital signal processor. Given its simplicity, this solution can be incorporated into the main processing system of an UAV using the SoC technology, and run concurrently with other required tasks, such as flight control and communications. Simulations and real-time DSP-based implementations have shown significant signal enhancement results by efficiently mitigating the interference from the noise generated by the UAVs propellers as well as from other external noise sources.
Radiofrequency (RF) Through-the-Wall Mapping (TWM) employs techniques originally applied in X-Ray Computerized Tomographic Imaging to map obstacles behind walls. It aims to provide valuable information for rescuing efforts in damaged buildings, as well as for military operations in urban scenarios. This work defines a Finite Element Method (FEM) based framework to allow fast and accurate simulations of the reconstruction of floors blueprints, using Ultra High-Frequency (UHF) signals at three different frequencies (500 MHz, 1 GHz and 2 GHz). To the best of our knowledge, this is the first use of FEM in a TWM scenario. This framework allows quick evaluation of different algorithms without the need to assemble a full test setup, which might not be available due to budgetary and time constraints. Using this, the present work evaluates a collection of reconstruction methods (Filtered Backprojection Reconstruction, Direct Fourier Reconstruction, Algebraic Reconstruction and Simultaneous Iterative Reconstruction) under a parallel-beam acquisition geometry for different spatial sampling rates, number of projections, antenna gains and operational frequencies. The use of multiple frequencies assesses the trade-off between higher resolution at shorter wavelengths and lower through-the-wall penetration. Considering all the drawbacks associated with such a complex problem, a robust and reliable computational setup based on a flexible method such as FEM can be very useful.
The effectiveness of the developed front-end on blocking the communication link of a commercial drone vehicle has been demonstrated in this work. A jamming approach has been taken in a broadband fashion by using GaN HEMT technology. Equipped with a modulated-signal generator, a broadband power amplifier, and an omni-directional antenna, the proposed system is capable of producing jamming signals in a very wide frequency range between 0.1 - 3 GHz. The maximum RF output power of the amplifier module has been software-limited to 27 dBm (500 mW), complying to the legal spectral regulations of the 2.4 GHz ISM band. In order to test the proof of concept, a real-world scenario has been prepared in which a commercially-available quadcopter UAV is flown in a controlled environment while the jammer system has been placed in a distance of about 10 m from the drone. It has been proven that the drone of interest can be neutralized as soon as it falls within the range of coverage (∼3 m) which endorses the promising potential of the broadband jamming approach.
The work presented within examines the performance of mechanical and electronic anemometers in battlefield applications. The goals of the study were to determine the utility of a local anemometer in quasi-combat engagements for direct fire weapon systems, to observe the limitations of each type of anemometer, and to determine which measurement method results in the most accurate ballistic correction. These goals are accomplished by combining a ballistic trajectory model, a turbulent wind field model, a sensor response model, and a fire control model into a single larger scale simulation that utilizes a Monte Carlo approach. The results of this effort showed that utilizing either a mechanical anemometer or an electronic anemometer with a relatively long averaging window produced the most accurate ballistic wind correction.
A mathematical model is developed to describe the thermal response of a temperature sensor located within a gun barrel, which accounts for the time-constant of the sensor and a measurement bias. The model is inversely solved to estimate the total heat flux applied to the bore surface as well as the transient history of the applied heat flux for a given thermal response of a temperature sensor. A parametric study is conducted to determine the influence of sensor time-constant, sensor location within the gun barrel, and measurement bias on the accuracy of the estimated heat flux as applied to a 155mm gun barrel. It is found that the accuracy of the estimated heat flux improves as the time-constant of the sensor decreases, the sensor is located closer to the bore surface, and the measurement bias decreases. A regression model is provided to estimate that accuracy and it is shown how a typical thermocouple would perform at various locations through the thickness of the gun barrel.
The continuing proliferation of improvised explosive devices is an omnipresent threat to civilians and members of military and law enforcement around the world. The ability to accurately and quickly detect explosive materials from a distance would be an extremely valuable tool for mitigating the risk posed by these devices. A variety of techniques exist that are capable of accurately identifying explosive compounds, but an effective standoff technique is still yet to be realized. Most of the methods being investigated to fill this gap in capabilities are laser based. Raman spectroscopy is one such technique that has been demonstrated to be effective at a distance. Spatially Offset Raman Spectroscopy (SORS) is a technique capable of identifying chemical compounds inside of containers, which could be used to detect hidden explosive devices. Coherent Anti-Stokes Raman Spectroscopy (CARS) utilized a coherent pair of lasers to excite a sample, greatly increasing the response of sample while decreasing the strength of the lasers being used, which significantly improves the eye safety issue that typically hinders laser-based detection methods. Time-gating techniques are also being developed to improve the data collection from Raman techniques, which are often hindered fluorescence of the test sample in addition to atmospheric, substrate, and contaminant responses. Ultraviolet based techniques have also shown significant promise by greatly improved signal strength from excitation of resonance in many explosive compounds. Raman spectroscopy, which identifies compounds based on their molecular response, can be coupled with Laser Induced Breakdown Spectroscopy (LIBS) capable of characterizing the sample’s atomic composition using a single laser.
A Commander’s decision making style represents how he weighs his choices and evaluates possible solutions with regards to his goals. Specifically, in the naval warfare domain, it relates the way he processes a large amount of information in dynamic, uncertain environments, allocates resources, and chooses appropriate actions to pursue. In this paper, we describe an approach to capture a Commander’s decision style by creating a cognitive model that captures his decisionmaking process and evaluate this model using a set of scenarios using an online naval warfare simulation game. In this model, we use the Commander’s past behaviors and generalize Commander's actions across multiple problems and multiple decision making sequences in order to recommend actions to a Commander in a manner that he may have taken. Our approach builds upon the Double Transition Model to represent the Commander's focus and beliefs to estimate his cognitive state. Each cognitive state reflects a stage in a Commander’s decision making process, each action reflects the tasks that he has taken to move himself closer to a final decision, and the reward reflects how close he is to achieving his goal. We then use inverse reinforcement learning to compute a reward for each of the Commander's actions. These rewards and cognitive states are used to compare between different styles of decision making. We construct a set of scenarios in the game where rational, intuitive and spontaneous decision making styles will be evaluated.
As a highly reliable positioning and orientation equipment, the redundant inertial navigation system (INS) is widely used in aerospace and other fields. For INS, high-precision calibration is the basis of high-precision navigation. Different from the calibration error modeling method of traditional orthogonal system, the nonorthogonal redundant ring laser gyro INS is installed with multi-device obliquely, and with the complexity of the configuration, the difficulty of separating the calibration parameters is also increased. Therefore, it is very significant to find a high precision calibration scheme for the non-orthogonal redundant INS. In this paper, the high precision calibration of non-orthogonal redundant INS in laboratory is studied, and a new calibration model of redundant system is summarized. A regular tetrahedral configuration prototype consisting of four Ring Laser Gyro and four Quartz Accelerometer is designed, and the calibration error modeling method and calibration accuracy are verified.
Inertial navigation system has been the core component of both military and civil navigation systems. Dual-axis rotation modulation can completely eliminate the inertial elements constant errors of the three axes to improve the system accuracy. But the error caused by the misalignment angles and the scale factor error cannot be eliminated through dual-axis rotation modulation. And discrete calibration method cannot fulfill requirements of high-accurate calibration of the mechanically dithered ring laser gyroscope navigation system with shock absorbers. This paper has analyzed the effect of calibration error during one modulated period and presented a new systematic self-calibration method for dual-axis rotation-modulating RLG-INS. Procedure for self-calibration of dual-axis rotation-modulating RLG-INS has been designed. The results of self-calibration simulation experiment proved that: this scheme can estimate all the errors in the calibration error model, the calibration precision of the inertial sensors scale factor error is less than 1ppm and the misalignment is less than 5″. These results have validated the systematic self-calibration method and proved its importance for accuracy improvement of dual -axis rotation inertial navigation system with mechanically dithered ring laser gyroscope.
There are the scale factor error of LDV (laser Doppler velocimeter) and the misalignment between the SINS (Strapdown inertial navigation system) and the vehicle in a SINS/LDV integrated navigation system. In this paper, the effects of these errors on the attitude, velocity and position of dead reckoning are derived, and a new online calibration method aiming to calibrate the scale factor of LDV and the misalignment between the SINS and the vehicle for the integrated system is put forward. This method, which is utilize the velocity and position of the Global Position System (GPS) as references, use the velocity error and position error of dead reckoning to estimate these errors. Through simulation and experiment, the validity and feasibility of the method are verified. The results show that the scale factor and the misalignment can be calibrated with satisfying accuracy, and the related research can provide technical support for high precision navigation of SINS/LDV integrated navigation systems.