An introduction to radar Automatic Target Recognition (ATR) technology in ground-based radar systems

This paper presents a brief examination of Automatic Target Recognition (ATR) technology within ground-based radar systems. It offers a lucid comprehension of the ATR concept, delves into its historical milestones, and categorizes ATR methods according to different scattering regions. By incorporating ATR solutions into radar systems, this study demonstrates the expansion of radar detection ranges and the enhancement of tracking capabilities, leading to superior situational awareness. Drawing insights from the Russo-Ukrainian War, the paper highlights three pressing radar applications that urgently necessitate ATR technology: detecting stealth aircraft, countering small drones, and implementing anti-jamming measures. Anticipating the next wave of radar ATR research, the study predicts a surge in cognitive radar and machine learning (ML)-driven algorithms. These emerging methodologies aspire to confront challenges associated with system adaptation, real-time recognition, and environmental adaptability. Ultimately, ATR stands poised to revolutionize conventional radar systems, ushering in an era of 4D sensing capabilities

Applied Analysis and Synthesis of Complex Systems: Proceedings of the IIASA-Kyoto University Joint Seminar, June 28-29, 2004

This two-day seminar aimed at introducing the new development of the COE by Kyoto University to IIASA and discussing general modeling methodologies for complex systems consisting of many elements, mostly via nonlinear, large-scale interactions. We aimed at clarifying fundamental principles in complex phenomena as well as utilizing and synthesizing the knowledge derived out of them. The 21st Century COE (Center of Excellence) Program is an initiative by the Japanese Ministry of Education, Culture, Science and Technology (MEXT) to support universities establishing discipline-specific international centers for education and research, and to enhance the universities to be the world's apex of excellence with international competitiveness in the specific research areas. Our program of "Research and Education on Complex Functional Mechanical Systems" is successfully selected to be awarded the fund for carrying out new research and education as Centers of Excellence in the field of mechanical engineering in 2003 (five-year project), and is expected to lead Japanese research and education, and endeavor to be the top in the world. The program covers general backgrounds in diverse fields as well as a more in-depth grasp of specific branches such as complex system modeling and analysis of the problems including: nonlinear dynamics, micro-mesoscopic physics, turbulent transport phenomena, atmosphere-ocean systems, robots, human-system interactions, and behaviors of nano-composites and biomaterials. Fundamentals of those complex functional mechanical systems are macroscopic phenomena of complex systems consisting of microscopic elements, mostly via nonlinear, large-scale interactions, which typically present collective behavior such as self-organization, pattern formation, etc. Such phenomena can be observed or created in every aspect of modern technologies. Especially, we are focusing upon; turbulent transport phenomena in climate modeling, dynamical and chaotic behaviors in control systems and human-machine systems, and behaviors of mechanical materials with complex structures. As a partial attainment of this program, IIASA and Kyoto University have exchanged Consortia Agreement at the beginning of the program in 2003, and this seminar was held to introduce the outline of the COE program of Kyoto University to IIASA researchers and to deepen the shared understandings on novel complex system modeling and analysis, including novel climate modeling and carbonic cycle management, through joint academic activities by mechanical engineers and system engineers. In this seminar, we invited a distinguished researcher in Europe as a keynote speaker and our works attained so far in the project were be presented by the core members of the project as well as by the other contributing members who participated in the project. All IIASA research staff and participants of YSSP (Young Scientist Summer Program) were cordially invited to attend this seminar to discuss general modeling methodologies for complex systems

Aeronautical Engineering: A continuing bibliography with indexes (supplement 207)

This bibliography lists 484 reports, articles and other documents introduced into the NASA scientific and technical information system in November 1986

Optimization of non-linear control aerodynamic systems using metaheuristic algorithm Optimisation des commandes non linéaires des systèmes aérodynamiques par les méthodes méta-heuristiques

This thesis is part of the project "modelisation and control dynamic systems" carried by the laboratory of LMSE. This project aims to develop and optimize new control approaches for the UAV quadrotor tracking control. This thesis consisted of the modelling of the quadrotor, and then analysing, designing and implementing new optimal control strategies based on the model-free concept. In this context, the aim of the thesis is to propose new control strategies based on the model-free concept. The proposed strategies help to compensate the disturbances and model uncertainties. Regarding our work, we have proposed different control techniques for quadrotor control. First, an optimal model-free backstepping control law applied to a quadrotor UAV has been proposed. In addition to this work, the dynamic system has been estimated through a new proposed fuzzy strategy and merged with the BC under the model-free concept. Finally, an optimal fuzzy model-free control has been designed based on decentralized fuzzy control. The objective of these control strategies is to achieve the best tracking with unknown nonlinear dynamics and external disturbances. These proposed approaches are validated through analytical and experimental procedures and the effectiveness checked and compared with regard to the related controllers in the presence of disturbances and model uncertainties

An intelligent navigation system for an unmanned surface vehicle

Merged with duplicate record 10026.1/2768 on 27.03.2017 by CS (TIS)A multi-disciplinary research project has been carried out at the University of Plymouth to design and develop an Unmanned Surface Vehicle (USV) named ýpringer. The work presented herein relates to formulation of a robust, reliable, accurate and adaptable navigation system to enable opringei to undertake various environmental monitoring tasks. Synergistically, sensor mathematical modelling, fuzzy logic, Multi-Sensor Data Fusion (MSDF), Multi-Model Adaptive Estimation (MMAE), fault adaptive data acquisition and an user interface system are combined to enhance the robustness and fault tolerance of the onboard navigation system. This thesis not only provides a holistic framework but also a concourse of computational techniques in the design of a fault tolerant navigation system. One of the principle novelties of this research is the use of various fuzzy logic based MSDF algorithms to provide an adaptive heading angle under various fault situations for Springer. This algorithm adapts the process noise covariance matrix ( Q) and measurement noise covariance matrix (R) in order to address one of the disadvantages of Kalman filtering. This algorithm has been implemented in Spi-inger in real time and results demonstrate excellent robustness qualities. In addition to the fuzzy logic based MSDF, a unique MMAE algorithm has been proposed in order to provide an alternative approach to enhance the fault tolerance of the heading angles for Springer. To the author's knowledge, the work presented in this thesis suggests a novel way forward in the development of autonomous navigation system design and, therefore, it is considered that the work constitutes a contribution to knowledge in this area of study. Also, there are a number of ways in which the work presented in this thesis can be extended to many other challenging domains.DEVONPORT MANAGEMENT LTD, J&S MARINE LTD AND SOUTH WEST WATER PL

Model Identification and Robust Nonlinear Model Predictive Control of a Twin Rotor MIMO System

PhDThis thesis presents an investigation into a number of model predictive control (MPC) paradigms for a nonlinear aerodynamics test rig, a twin rotor multi-input multi-output system (TRMS). To this end, the nonlinear dynamic model of the system is developed using various modelling techniques. A comprehensive study is made to compare these models and to select the best one to be used for control design purpose. On the basis of the selected model, a state-feedback multistep Newton-type MPC is developed and its stability is addressed using a terminal equality constraint approach. Moreover, the state-feedback control approach is combined with a nonlinear state observer to form an output-feedback MPC. Finally, a robust MPC technique is employed to address the uncertainties of the system. In the modelling stage, analytical models are developed by extracting the physical equations of the system using the Newtonian and Lagrangian approaches. In the case of the black-box modelling, artificial neural networks (ANNs) are utilised to model the TRMS. Finally, the grey-box model is used to enhance the performance of the white-box model developed earlier through the optimisation of parameters using a genetic algorithm (GA) based approach. Stability analysis of the autonomous TRMS is carried out before designing any control paradigms for the system. In the control design stage, an MPC method is proposed for constrained nonlinear systems, which is the improvement of the multistep Newton-type control strategy. The stability of the proposed state-feedback MPC is guaranteed using terminal equality constraints. Moreover, the formerly proposed MPC algorithm is combined with an unscented Kalman filter (UKF) to formulate an output-feedback MPC. An extended Kalman filter (EKF) based on a state-dependent model is also introduced, whose performance is found to be better compared to that of the UKF. Finally, a robust MPC is introduced and implemented on the TRMS based on a polytopic uncertainty that is cast into linear matrix inequalities (LMI)

Unmanned Vehicle Systems & Operations on Air, Sea, Land

Unmanned Vehicle Systems & Operations On Air, Sea, Land is our fourth textbook in a series covering the world of Unmanned Aircraft Systems (UAS) and Counter Unmanned Aircraft Systems (CUAS). (Nichols R. K., 2018) (Nichols R. K., et al., 2019) (Nichols R. , et al., 2020)The authors have expanded their purview beyond UAS / CUAS systems. Our title shows our concern for growth and unique cyber security unmanned vehicle technology and operations for unmanned vehicles in all theaters: Air, Sea and Land – especially maritime cybersecurity and China proliferation issues. Topics include: Information Advances, Remote ID, and Extreme Persistence ISR; Unmanned Aerial Vehicles & How They Can Augment Mesonet Weather Tower Data Collection; Tour de Drones for the Discerning Palate; Underwater Autonomous Navigation & other UUV Advances; Autonomous Maritime Asymmetric Systems; UUV Integrated Autonomous Missions & Drone Management; Principles of Naval Architecture Applied to UUV’s; Unmanned Logistics Operating Safely and Efficiently Across Multiple Domains; Chinese Advances in Stealth UAV Penetration Path Planning in Combat Environment; UAS, the Fourth Amendment and Privacy; UV & Disinformation / Misinformation Channels; Chinese UAS Proliferation along New Silk Road Sea / Land Routes; Automaton, AI, Law, Ethics, Crossing the Machine – Human Barrier and Maritime Cybersecurity.Unmanned Vehicle 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. Unmanned Vehicle (UV) Systems & Operations On Air, Sea, Land discusses state-of-the-art technology / issues facing U.S. UV system researchers / designers / manufacturers / testers. We trust our newest look at Unmanned Vehicles in Air, Sea, and Land will enrich our students and readers understanding of the purview of this wonderful technology we call UV.https://newprairiepress.org/ebooks/1035/thumbnail.jp

On the Complete Automation of Vertical Flight Aircraft Ship Landing

The current study focuses on developing an autonomous vertical flight aircraft ship landing system by directly automating the established Navy helicopter ship landing procedure. The central idea involves visually tracking a gyro-stabilized horizon bar installed on most Navy ships to approach and land vertically independent of deck motions. This was accomplished through the development of a rotorcraft flight dynamics modeling framework and vision-based control systems as well as conducting simulations and flight tests. The framework, named Texas A&M Rotorcraft Analysis Code (TRAC), was developed as a modular tool that could model any rotorcraft configuration at a low computational cost. A UH-60 helicopter was modeled as a baseline aircraft and validated using the US Army flight test data. A linear quadratic regulator (LQR) controller was utilized to stabilize and control the helicopter during autonomous ship landing simulations. The vision system was developed to obtain the visual information that a pilot perceives during ship approach and landing. It detects the ship at long-distance by utilizing machine/deep learning-based detection and at close range, it utilizes uniquely developed vision algorithms to detect the horizon bar to precisely estimate the aircraft position and orientation relative to the bar. It demonstrated 250 meters of detection range for a 6 x 6 ft sub-scale ship platform, which translates to a range of 17.3 kilometers for a full-scale 50 x 50 ft typical small ship. The distance and attitude estimations were validated using the measurements from an accurate 3D motion capture system (VICON), which demonstrated sub-centimeter and sub-degree accuracy. To control the aircraft based on the perceived visual information, both nonlinear control and deep reinforcement learning control strategies were developed. The nonlinear controller demonstrated robust tracking capability even with 0.5 seconds of time delay and estimation noise. When flight-tested in 5 m/s wind gust, the deep reinforcement learning control demonstrated superior disturbance rejection capability, with 50% reduced drift at a 3 times faster rate compared to conventional control systems. Both vision and control systems were implemented on a quadrotor unmanned aircraft and extensive flight tests were conducted to demonstrate accurate tracking in challenging conditions and safe vertical landing on a translating ship platform with 6 degrees of freedom motions

A review: On path planning strategies for navigation of mobile robot

This paper presents the rigorous study of mobile robot navigation techniques used so far. The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap. The classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (APF); reactive approaches such as genetic algorithm (GA), fuzzy logic (FL), neural network (NN), firefly algorithm (FA), particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization (BFO), artificial bee colony (ABC), cuckoo search (CS), shuffled frog leaping algorithm (SFLA) and other miscellaneous algorithms (OMA) are considered for study. The navigation over static and dynamic condition is analyzed (for single and multiple robot systems) and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches. It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm. Hence, reactive approaches are more popular and widely used for path planning of mobile robot. The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics

Artificial Intelligence Applications for Drones Navigation in GPS-denied or degraded Environments

L'abstract è presente nell'allegato / the abstract is in the attachmen