Intelligence Surveillance Reconnaissance Research Articles

Asymmetric imaging through engineered Janus particle obscurants using a Monte Carlo approach for highly asymmetric scattering media

The advancement of imaging systems has significantly ameliorated various technologies, including Intelligence Surveillance Reconnaissance Systems and Guidance Systems, by enhancing target detection, recognition, identification, positioning, and tracking capabilities. These systems can be countered by deploying obscurants like smoke, dust, or fog to hinder visibility and communication. However, these counter-systems affect the visibility of both sides of the cloud. In this sense, this manuscript introduces a new concept of a smoke cloud composed of engineered Janus particles to conceal the target image on one side while providing clear vision from the other. The proposed method exploits the unique scattering properties of Janus particles, which selectively interact with photons from different directions to open up the possibility of asymmetric imaging. This approach employs a model that combines a genetic algorithm with Discrete Dipole Approximation to optimize the Janus particles' geometrical parameters for the desired scattering properties. Moreover, we propose a Monte Carlo-based approach to calculate the image formed as photons pass through the cloud, considering highly asymmetric particles, such as Janus particles. The effectiveness of the cloud in disguising a target is evaluated by calculating the Probability of Detection (PD) and the Probability of Identification (PID) based on the constructed image. The optimized Janus particles can produce a cloud where it is possible to identify a target more than 50% of the time from one side (PID > 50%) while the target is not detected more than 50% of the time from the other side (PD < 50%). The results demonstrate that the Janus particle-engineered smoke enables asymmetric imaging with simultaneous concealment from one side and clear visualization from the other. This research opens intriguing possibilities for modern obscurant design and imaging systems through highly asymmetric and inhomogeneous particles besides target detection and identification capabilities in challenging environments.

Target Threat Assessment in Air Combat with BP Neural Network for UAV

Target threat assessment is the most important element in command control communication and computer intelligence surveillance reconnaissance (C4ISR) system which is the key function in air combat situation analysis and can select the most valuable target to the commander. A threat assessment model of multi-UAV platform cooperative engagement built based on back propagate neural network (BPNN) is present in this paper, which provides a machine learning method for the threat assessment. The BPNN target threat assessment model has six input nodes, 10 hide nodes and 1 output node, which can fit the complicated nonlinear relationship between threat value and target behavior state. Simulation experiment is executed to verify the accuracy of the proposed BPNN target threat assessment method, and the result shows that the method is effective and can be applied to the real air combat.

Design of hybrid electric heavy fuel MALE ISR UAV enabling technologies for military operations

PurposeThis paper aims to present the main results achieved in the frame of the TIVANO national-funded project which may anticipate, in a stepped approach, the evolution and the design of the enabling technologies needed for a hybrid/electric medium altitude long endurance (MALE) unmanned aerial vehicle (UAV) to perform persistent intelligence surveillance reconnaissance (ISR) military operations.Design/methodology/approachDifferent architectures of hybrid-propulsion system are analyzed pointing out their operating modes to select the more suitable architecture for the reference aircraft. The selected architecture is further analyzed together with its electric power plant branch focusing on electric system architecture and the selected electric machine. A final comparison between the hybrid and standard propulsion is given at aircraft level.FindingsThe use of hybrid propulsion may lead to a reduction of the total aircraft mass and an increase in safety level. However, this result comes together with a reduced performance in climb phase.Practical implicationsThis study can be used as a reference for similar studies and it provides a detailed description of propulsion operating modes, power management, electric system and machine architecture.Originality/valueThis study presents a novel application of hybrid propulsion focusing on a three tons class MALE UAV for ISR missions. It provides new operating modes of the propulsion system and a detailed electric architecture of its powertrain branch and machine. Some considerations on noise emissions and infra-red traceability of this propulsion, at aircraft level.

Cognitive Computing for Anticipatory Risk Analytics in Intelligence, Surveillance, &amp; Reconnaissance (ISR): Model Risk Management in Artificial Intelligence &amp; Machine Learning (Presentation Slides)

Drawing upon insights shared in the MIT: AI & Machine Learning: Management and Leadership learning community of practice, the current Intelligence, Surveillance, Reconnaissance (ISR) presentation advances the focus on collective intelligence of people and computers in the context of Cognitive Computing for Anticipatory Risk Analytics in Intelligence, Surveillance, & Reconnaissance (ISR). It defines as well as distinguishes multi-level Cognitive Computing process engineering frameworks of Artificial Intelligence (AI) & Machine Learning as applied in KM practices of US and worldwide firms, governments, and ISR agencies from Cognitive-Neuromorphic Chips. A recent IEEE Spectrum report also published as Chip's Make-or-Break Moment observes that Neuromorphic Chips Are Destined for Deep Learning—or Obscurity given that the Chip researchers have hitched their wagon to deep learning's star. Drawing upon insights on Model Risk Management and Anticipatory Risk Analytics focus of top Wall Street investment banks and hedge funds beyond the Global Financial Crisis currently guiding national and global Cyber Risk Insurance industry practices, we demonstrate how Model Risk Management (MRM) and Anticipatory Risk Analytics are even more critical in the global and national domains of Intelligence, Surveillance, Reconnaissance (ISR). The first operational use of AI and Deep Learning AI technologies in the Defense Intelligence Enterprise led by Project Maven and Algorithmic Warfare Cross-Functional Team is used as a case study for illustrating how Anticipatory Risk Analytics & MRM assume even greater significance at the intersections of Space and Cyberspace wherein Offensive and Defensive Cybersecurity strategies, such as discussed in the recent 2015 and 2016 presentations at the Princeton Quant Trading Conference, are deployed.

Impact of Instructional Strategies on Motivation and Engagement for Simulation-Based Training of Robot-Aided ISR Tasks

The U.S. Army projects a considerable increase in the number of operational Unmanned Ground Systems (UGS) within the next ten years. There is a need to enhance UGS capabilities to support remote Intelligence, Surveillance, Reconnaissance (ISR) operations involving the identification of High-Value Individuals (HVI). Emerging UGS capability requirements will inevitably result in new or revised training requirements. The U.S. Army identifies Simulation-Based Training (SBT) as a required training platform for robot-aided ISR tasks utilizing UGSs. In order to implement an effective SBT system, there are several factors to consider related to training systems design and trainee needs. Factors addressed in this study include the selection of effective SBT instructional strategies and the impact on trainee motivation and engagement. Results from this study contribute to design and future research recommendations regarding SBT for robot-aided ISR tasks.

차세대 한국형 공용데이터링크 개발을 위한 국·내외 공용데이터링크 기술 동향 분석

현대전의 전쟁 개념은 정보 통신 기술의 발전과 함께 지휘 통제 과정을 네트워크로 연계하여 전투 수행 능력을 향상시키는 네트워크 중심전 (NCW, Network Centric Warfare)으로 변화하고 있다. 이에 따라, 네트워크를 통한 정보의 우위를 점유하는 NCW의 구현에 있어, 감시 정찰 체계로부터 획득한 정보정찰감시 (ISR, Intelligence Surveillance Reconnaissance) 정보의 고속 전송을 위해 개발된 공용데이터링크 (CDL, Common Data Link) 기술이 핵심 요소로 고려되고 있다. 세계 각국에서는 CDL의 전송속도 및 네트워킹 기능을 향상시키기 위한 기술 개발에 주력하고 있으며, 우리 군도 자립적인 CDL 기술 개발의 필요성을 인식하고 MPI-CDL (Multi-Platform Image and Intelligence Common Data Link)을 개발하였다. 하지만 다수 체계 간 대용량 ISR 정보를 신속하게 수집 제공하기에는 최대 전송속도 및 네트워킹 기능 성능이 해외 장비 대비 미흡한 상황으로, 전체적인 성능을 향상시킨 차세대 CDL 개발이 필요한 실정이다. 따라서 본 논문에서는 차세대 한국형 CDL 개발을 위해, 현재까지의 CDL 개발 흐름과 해외 선행 장비의 기술 동향을 살펴보고, 향후 지향해야 할 차세대 CDL 개발 방향에 대해 제시한다. NCW is a new operational concept for modern warfare to improve the ability to conduct combat by connecting command and control system via network. To realize NCW integrating information sharing system between multiple platforms, CDL is considered as a key component for high-speed transmission of ISR data. Accordingly, further studies to enhance the performance of CDL are being performed around the world, and our government has also developed self-reliant CDL, MPI-CDL. However, the maximum data rate and networking performance of current CDL still fall short of the objective to enable multiple systems to share the large ISR data. Therefore, in this paper, we analyze the development flow and international technical trends of CDL, and present an approach for enhancing data rate and networking capacity of the next generation CDL.

An Alenia Aermacchi study and experience on MBSE application

AbstractThis paper describes how traditional systems engineering activities conducted in Concept and Configuration Development phases of Aeronautical Product Life Cycle have evolved for the design of Unmanned Aerial System (UAS) for Intelligence Surveillance Reconnaissance mission, by applying Model Based System Engineering (MBSE) approach. This methodology will make easier the support to other phases of the process, like the Product Development. It has been considered the system modelling language (SysML) as reference model based language.The design process and the findings about the modelling and simulation that has been considered by Alenia Aermacchi are described in the paper.

해상시험 결과를 이용한 RIB의 4자유도 동력학 식별 (I) - 해상시험, 저항·추진 모델

RIB(Rigid Inflatable Boat) is widely used for coastal transportation in the commercial use and for ISR(Intelligence, Surveillance, Reconnaissance) in the military use. Since RIB is around 10 meters in length and over 30 knots in speed, its motion characteristics in waves is quite different from a large scale ship. When it turns, large roll occurs and heeling direction is opposite to the large ship's case. Currently, many countries are developing USV(Unmanned Surface Vehicle) of which type is RIB. In order to develop high performance autopilot and way point controller, it is very important to identify RIB's motion characteristics. In this paper, sea trial test results of a 7-meter RIB such as speed, turning, zig-zag, and way point control tests were represented and its resistance and propulsion model was identified by using sea trial data and Savitsky's formula. In addition, the state space model which will be used in the identification of the four-degree-of-freedom dynamics in the next step was formulated and the identification procedure was proposed.

Toward a Protected Future Force

A significant challenge facing planners of America's future ground forces is the question of how to think about survivability. With a great deal of emphasis placed on strategic mobility--both in speed of deployment and in logistical efficiency--the manned ground vehicles of the US Army's Future Combat Systems (FCS) will be considerably lighter than the tanks and infantry fighting vehicles currently fielded in Army units. This poses a problem: without the ability to wield the armor protection of the Abrams main battle tank, FCS vehicles (1) will have more inherent vulnerability to enemy direct-fire weapons than the vehicles that they will eventually replace. This problem is not just tactical and operational in nature; it has strategic implications, and if it is not addressed effectively, the ability of the Army to meet its required aims will be put at risk. The US Army plans to introduce its next-generation ground force quickly, starting with an experimental battalion by the end of the decade and a full brigade--called a Unit of Action--in 2014. (2) The Future Force, formerly called the Objective Force, is anticipated to be a highly mobile, light armored force with previously unheard-of intelligence-surveillance-reconnaissance (ISR) capabilities, emphasizing maneuver and stand-off precision firepower to eliminate enemy forces from long range. The Army's plan does allow for a conventional armored counterattack corps based on existing tanks and fighting vehicles through at least 2030, but the centerpiece of the Army will be the FCS-equipped Units of Action. The FCS Family of Vehicles--Survivability without Protection? The survivability of the Future depends on two factors: the technological measures taken to protect the force and the methods of employment of the Units of Action. According to the Army's Concepts for the Objective Force white paper: The agility of our formations combined with the common operating picture is critical to maximize survivability. Ground and air platforms will leverage the best combination of low observable, reduced electronic signature, ballistic protection, long-range acquisition, early discrete targeting, shoot first every time, and target destruction every time we pull the trigger. Objective survivability will be linked to its inherently offensive orientation, as well as its speed and lethality. (3) The inherent protection of the 16- to 20-ton FCS manned ground vehicle will be relatively low, primarily the result of its light weight. Future Combat Systems vehicles will be equipped with a variety of technical countermeasures intended to enhance their survivability in the event of contact with the enemy, but the underlying basis for their ability to survive on the battlefield is their ability to locate the enemy in all terrain types and then kill of outmaneuver the enemy without being effectively engaged. The extent to which armor will play a role in FCS survivability was outlined in a presentation given at the 23d Army Science Conference in December 2002. (4) The presentation outlines the Army's vision of a holistic approach to survivability: it intends to employ methods of preventing American vehicles from being effectively engaged through a combination of technical defenses and creative employment of forces. For example, to avoid detection, the Army will maneuver its forces out of contact with the enemy, it will employ vehicles with reduced signatures, and it will attempt to blind the enemy through targeting its command and control and ISR assets. Similar methods will be employed at each of the other levels of engagement--once detected, methods will be used to prevent Army vehicles from being acquired, and then hit, and then penetrated, and then destroyed. This is typically given in a diagram with concentric circles, the outermost being Detected and the innermost being Killed. …

Sensor performance conversions for infrared target acquisition and intelligence–surveillance–reconnaissance imaging sensors

Target acquisition infrared imaging sensors are characterized by their minimum resolvable temperature parameter that is translated to the probability of identification (Pid) performance estimate for a given target. Intelligence-surveillance-reconnaissance (ISR) sensors are characterized by the general image quality equation to give a national imagery interpretability rating scale (NIIRS) performance estimate. Sensors, such as those on Predator and Global Hawk, will soon be used for both ISR and target acquisition purposes. We present a performance conversion that includes both sensor resolution and sensitivity. We also provide the first empirical results to our knowledge ever to be presented that relate NIIRS and Pid for a given set of targets.

Targeting and intelligence electro-optical recognition modeling: a juxtaposition of the probabilities of discrimination and the general image quality equation

The recognition of objects using target acquisition systems is modeled by a sensor's minimum resolvable temperature (MRT), the Johnson criteria, atmospherics, and object specifics. Collectively, these three characteristics provide an acquisition model for estimating the probability of object recognition (and detection, identification) as a func- tion of sensor-to-object range. This technique is called the probabilities of discrimination. When quantifying the performance of intelligence- surveillance-reconnaissance (ISR) systems, object recognition is as- sessed using the National Imagery Interpretability Scale (NIIRS). Each NIIRS level corresponds to a different capacity for object recognition and is defined by a set of recognition criteria. The general image quality equation (GIQE) is the ISR sensor model that determines the expected NIIRS level of a sensor for a given set of sensor parameters. It is impor- tant that electro-optical sensor engineers understand both of these rec- ognition models. The segregation between the target acquisition and ISR sensor communities is becoming less sharp as ISR sensors are begin- ning to be used for target acquisition purposes and visa versa. Network and wireless communication advances are providing the means for dual exploitation of these systems. Descriptions of these two recognition models, probabilities of discrimination, and the GIQE are provided. The two models are applied to example systems. Finally, the two models are compared and contrasted. © 1998 Society of Photo-Optical Instrumentation Engi- neers. (S0091-3286(98)00503-0)