Optimal Channel-Switching Strategies in Multi-channel Wireless Networks.

The dual nature of scarcity and under-utilization of spectrum resources, as well as recent advances in software-defined radio, led to extensive study on the design of transceivers that are capable of opportunistic channel access. By allowing users to dynamically select which channel(s) to use for transmission, the overall throughput performance and the spectrum utilization of the system can in general be improved, compared to one with a single channel or more static channel allocations. The reason for such improvement lies in the exploitation of the underlying temporal, spatial, spectral and congestion diversity. In this dissertation, we focus on the channel-switching/hopping decision of a (group of) legitimate user(s) in a multi-channel wireless communication system, and study three closely related problems: 1) a jamming defense problem against a no-regret learning attacker, 2) a jamming defense problem with minimax (worst-case) optimal channel-switching strategies, and 3) the throughput optimal strategies for a group of competing users in IEEE 802.11-like medium access schemes. For the first problem we study the interaction between a user and an attacker from a learning perspective, where an online learner naturally adapts to the available information on the adversarial environment over time, and evolves its strategy with certain payoff guarantee. We show how the user can counter a strong learning attacker with knowledge on its learning rationale, and how the learning technique can itself be considered as a countermeasure with no such prior information. We further consider in the second problem the worst-case optimal strategy for the user without prior information on the attacking pattern, except that the attacker is subject to a resource constraint, which models its energy consumption and replenishment process. We provide explicit characterization for the optimal strategies and show the most damaging attacker, interestingly, behaves randomly in an i.i.d. fashion. In the last problem, we consider a group of competing users in a non-adversarial setting. We place the interaction among users in the context of IEEE 802.11-like medium access schemes, and derive decentralized channel allocation for overall throughput improvement. We show the typically rule-of-thumb load balancing principle in spectrum resource sharing can be indeed throughput optimal.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108949/1/qingsi_1.pd

Multi-Agent Pursuit of a Faster Evader with Application to Unmanned Aerial Vehicles

Robotic applications like search and rescue missions, surveillance, police missions, patrolling, and warfare can all be modeled as a Pursuit-Evasion Game (PEG). Most of these tasks are multi-agent problems, often including a cooperation between team members and a conflict between adversarial teams. In order to realize such a situation with robots, two major problems have to be solved. Initially, a decomposition of the PEG has to be performed for getting results in reasonable time. Present embedded computers lack the computational power enabling them to process the highly complex solution algorithm of the non-decomposed game fast enough. Secondly, a framework has to be defined, enabling the computation of optimal actions for both the pursuers and the evaders when a cooperation within the teams is possible. It is intended to develop strategies, that allow the team of pursuers to capture a faster evader in a visibility-based PEG setup due to cooperation. For tackling the first problem a game structure is sought, aiming to considerably reduce the time complexity of the solution process. The first step is the decomposition of the action space, and the second is the change of the game structure itself. The latter is reached by defining a two-pursuer one-evader PEG with three different game structures, which are the Non-Decomposed Game, the Multiple Two-Player Game Decomposition (MTPGD) game, and the Team-Subsumption Two-Player Game (TSTPG). Several simulation results demonstrate, that both methods yield close results in respect to the full game. With increasing cardinality of each player’s strategy space, the MTPGD yields a relevant decrease of the run-time. Otherwise, the TSTPG does not minimize the time complexity, but enables the use of more sophisticated algorithms for two-player games, resulting in a decreased runtime. The cooperation within a team is enabled by introducing a hierarchical decomposition of the game. On a superordinate collaboration level, the pursuers choose their optimal behavioral strategy (e.g. pursuit and battue) resulting in the case of a two-pursuer one-evader PEG in a three-player noncooperative dynamic game, which is solved in a subordinate level of the overall game. This structure enables an intelligent behavior change for the pursuers based on game-theoretical solution methods. Depending on the state of the game, which behavioral strategy yields the best results for the pursuers within a predefined time horizon has to be evaluated. It is shown that the pursuer’s outcome can be improved by using a superordinate cooperation. Moreover, conditions are presented under which a capture of a faster evader by a group of two pursuers is possible in a visibility-based PEG with imperfect information. Since Unmanned Aerial Vehicles (UAVs) are increasingly a common platform used in the aforementioned applications, this work focuses only on PEGs with multi-rotor UAVs. Furthermore, the realization of the concepts in this thesis are applied on a real hex rotor. The feasibility of the approach is proven in experiments, while all implementations on the UAV are running in real-time. This framework provides a solution concept for all types of dynamic games with an 1-M or N-1 setup, that have a non-cooperative and cooperative nature. At this stage a N-M dynamic game is not applicable. Nevertheless, an approach to extend this framework to the N-M case is proposed in the last chapter of this work

Toward a Framework for Systematically Categorizing Future UAS Threat Space

Title from PDF of title page, viewed September 21, 2022Dissertation advisor: Travis FieldsVitaIncludes bibliographical references (pages 241-270)Dissertation (Ph.D.)--Department of Civil and Mechanical Engineering, Department of Electrical and Computer Engineering. University of Missouri--Kansas City, 2021The development of unmanned aerial vehicles (UAVs) is occurring as fast or faster than any other innovation throughout the course of human history. Building an effective means of defending against threats posed by malicious applications of novel technology is imperative in the current global landscape. Gone are the days where the enemy and the threat it poses are well defined and understood. Defensive technologies have to be modular and able to adapt to a threat technology space which is likely to recycle several times over during the course of a single defense system acquisition cycle. This manuscript wrestles with understanding the unique threat posed by UAVs and related technologies. A thorough taxonomy of the problem is given including projections for how the defining characteristics of the problem are likely to change and grow in the near future. Next, a discussion of the importance of tactics related to the problem of a rapidly changing threat space is provided. A discussion of case studies related to lessons learned from military acquisition programs and pivotal technological innovations in the course of history are given. Multiple measures of success are proposed which are designed to allow for meaningful comparisons and honest evaluations of capabilities. These measures are designed to facilitate discussions by providing a common, and comprehensible language that accounts for the vast complexity of the problem space without getting bogged down by the details. Lastly, predictions for the future threat space comprising UAVs is given. The contributions of this work are thus threefold. Firstly, an analytic framework is presented including a detailed parameterization of the problem as well as various solution techniques borrowed from a variety of fields. Secondly, measures of success are presented which attempt to compare the effectiveness of various systems by converting to expected values in terms of effective range, or extending the popular concept of kill chain and collapsing effectiveness into units of time. A novel technique for measuring effectiveness is presented whereby effectiveness is composed of various individual probabilities. Probabilities and associated distributions can be combined according to the rules of joint probabilities and distributions and allows performance against a probabilistic threat to be measured succinctly and effectively. The third contribution concerns predictions made with respect to the UAS threat space in the future. These predictions are designed to allow for defensive systems to be developed with a high expected effectiveness against current and future threats. Essentially this work comprises a first attempt toward developing a complete framework related to engagement and mission level modeling of a generic defensive system (or combination of systems) in the face of current and future threats presented by UAS.Introduction -- Literature review -- War gaming -- Measures of success -- Conclusion

Air Force Institute of Technology Research Report 2020

This Research Report presents the FY20 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

Collision Free Navigation of a Multi-Robot Team for Intruder Interception

In this report, we propose a decentralised motion control algorithm for the mobile robots to intercept an intruder entering (k-intercepting) or escaping (e-intercepting) a protected region. In continuation, we propose a decentralized navigation strategy (dynamic-intercepting) for a multi-robot team known as predators to intercept the intruders or in the other words, preys, from escaping a siege ring which is created by the predators. A necessary and sufficient condition for the existence of a solution of this problem is obtained. Furthermore, we propose an intelligent game-based decision-making algorithm (IGD) for a fleet of mobile robots to maximize the probability of detection in a bounded region. We prove that the proposed decentralised cooperative and non-cooperative game-based decision-making algorithm enables each robot to make the best decision to choose the shortest path with minimum local information. Then we propose a leader-follower based collision-free navigation control method for a fleet of mobile robots to traverse an unknown cluttered environment where is occupied by multiple obstacles to trap a target. We prove that each individual team member is able to traverse safely in the region, which is cluttered by many obstacles with any shapes to trap the target while using the sensors in some indefinite switching points and not continuously, which leads to saving energy consumption and increasing the battery life of the robots consequently. And finally, we propose a novel navigation strategy for a unicycle mobile robot in a cluttered area with moving obstacles based on virtual field force algorithm. The mathematical proof of the navigation laws and the computer simulations are provided to confirm the validity, robustness, and reliability of the proposed methods

USING VIRTUAL REALITY TO INVESTIGATE ‘PROTEAN’ ANTI-PREDATOR BEHAVIOUR

Prey animals have evolved a wide variety of behaviours to combat the threat of predation, many of which have received considerable empirical and theoretical attention and are generally well understood in terms of their function and mechanistic underpinning. However, one of the most commonly observed and taxonomically widespread antipredator behaviours of all has, remarkably, received almost no experimental investigation: so-called ‘protean’ behaviour. This is defined as ‘behaviour that is sufficiently unpredictable to prevent a predator anticipating in detail the future position or actions of its prey’. In this thesis, I have elucidated the mechanisms that allow protean behaviour to be an effective anti-predatory response. This was explored with two approaches. Firstly, through the novel and extremely timely use of virtual reality to allow human ‘predators’ to attack and chase virtual prey in three-dimensions from a first-person perspective, thereby bringing the realism that has been missing from previous studies on predator-prey dynamics. Secondly through the three-dimensional tracking of protean behaviour in a highly tractable model species, the painted lady butterfly (Vanessa cardui). I explored this phenomenon in multiple contexts. Firstly, I simulated individual protean prey and explored the effects of unpredictability in their movement rules with respect to targeting accuracy of human ‘predators’ in virtual reality. Next, I examined the concept of ‘protean insurance’ via digitised movements of the painted lady butterfly, exploring the qualities of this animals’ movement paths related to human targeting ability. I then explored how the dynamics of animal groupings affected protean movement. Specifically, I investigated how increasing movement path complexity interacted with the well-documented ‘confusion effect’. I explored this question using both an experimental study and a VR citizen science game disseminated to the general public via the video game digital distribution service ‘Steam’. Subsequently, I explored another phenomenon associated with groupings of prey items; the ‘oddity effect’, which describes the preferential targeting of phenotypically odd individuals by predators. Typically, this phenomenon is associated with oddity of colouration or size. In this case, I investigated whether oddity of protean movement patterns relative to other group members could induce a ‘behavioural oddity effect’. Finally, I used a specialised genetic algorithm (GA) that was driven by human performance with respect to targeting prey items. I investigated the emergent protean movement paths that resulted from sustained predation pressure from humans. Specifically, I examined the qualities of the most fit movement paths with respect to control evolutions that were not under the selection pressure of human performance (randomised evolution). In the course of this thesis, I have gained a deeper understanding of a near ubiquitous component of predator prey interactions that has until recently been the subject of little empirical study. These findings provide important insights into the understudied phenomenon of protean movement, which are directly applicable to predator –prey dynamics within a broad range of taxa

Unmanned Aircraft Systems in the Cyber Domain

Unmanned Aircraft Systems are an integral part of the US national critical infrastructure. The authors have endeavored to bring a breadth and quality of information to the reader that is unparalleled in the unclassified sphere. This textbook will fully immerse and engage the reader / student in the cyber-security considerations of this rapidly emerging technology that we know as unmanned aircraft systems (UAS). The first edition topics covered National Airspace (NAS) policy issues, information security (INFOSEC), UAS vulnerabilities in key systems (Sense and Avoid / SCADA), navigation and collision avoidance systems, stealth design, intelligence, surveillance and reconnaissance (ISR) platforms; weapons systems security; electronic warfare considerations; data-links, jamming, operational vulnerabilities and still-emerging political scenarios that affect US military / commercial decisions. This second edition discusses state-of-the-art technology issues facing US UAS designers. It focuses on counter unmanned aircraft systems (C-UAS) – especially research designed to mitigate and terminate threats by SWARMS. Topics include high-altitude platforms (HAPS) for wireless communications; C-UAS and large scale threats; acoustic countermeasures against SWARMS and building an Identify Friend or Foe (IFF) acoustic library; updates to the legal / regulatory landscape; UAS proliferation along the Chinese New Silk Road Sea / Land routes; and ethics in this new age of autonomous systems and artificial intelligence (AI).https://newprairiepress.org/ebooks/1027/thumbnail.jp