Shadows in the Sky: Thai Perspectives on Drone Warfare

: The first and only corps unmanned aerial vehicle (UAV) program, Hunter, was terminated in January 1996, leaving all but one corps commander without the ability to shape the battle space. After completing the advanced concept technology demonstration, the Army released its high altitude endurance Predator UAV to the U.S. Air Force. Today, the Army's focus is on the brigade commanders' Tactical UAV - a system ill suited for corps imagery intelligence requirements. At issue is the gap in coverage and attendant loss of precision targeting capability created by the termination of the Hunter program. The Tactical UAV supports requirements out to 50 kilometers while Predator supports theater requirements down to 300 kilometers. The Army needs a Hunter or similar system to fill the gap between 50 and 300 kilometers, providing corps with the ability to shape the battle space in accordance with Army doctrine. This study identifies required capabilities and unique characteristics of corps requirements, and illuminates the tremendous risk incurred by not having parallel growth in UAV capability congruent with the transformed force. During Operation Allied Force, Task Force Hunter displayed to the Joint Force and V Corps Commanders the necessity for a corps level UAV.

Fuselage weight Estimation of an Unmanned Aerial Vehicle (UAV) during the conceptual design phase has been demonstrated to be an important challenge because of its unconventional configuration. Investigation of the development of a new suggested formula for fuselage weight estimation of UAV is done for UAVs having takeoff weight ranging from 100 to 500 kg, and which have similar characteristics. Based on statistical trends, obtained from analyzed existing UAVs, takeoff weight is estimated from mission specification, and it is derived for a tactical unmanned aerial vehicle (TUAV). Software tools are developed in MATLAB to facilitate takeoff and component weight calculations. The least square method is applied to analyze statistical data in order to develop trend functions which correlate TUAVs takeoff weight. Existing formulas, developed for general aviation, for fuselage and takeoff weight estimations are applied to TUAV and promising one are selected and adjusted to TUAV conceptual design phase. Fuselage weight estimation is related to geometrical parameters and takeoff weight of TUAVs.

Purpose This article aims to enhance the performance of a piston-prop tactical unmanned aerial vehicle (TUAV) by simultaneously and stochastically redesigning its autopilot, powerplant and vertical tail. The performance includes both autonomous flight performance (AFP) and general flight performance (GFP). GFP captures maximum range, maximum endurance and ceiling altitude. Design/methodology/approach A TUAV is manufactured in our Erciyes University Unmanned Aerial Vehicle Laboratory (ERU UAVL). Its autopilot’s PID controllers’ gains, powerplant and vertical tail can be varied before starting flight. Autopilot, powerplant and vertical tail parameters are simultaneously and stochastically redesigned to achieve optimal performance using a stochastic optimization methodology. The results obtained are used during the simulations of autonomous TUAV flight. Findings The use of simultaneous and stochastic redesign for a piston-prop TUAV having a changing powerplant system, vertical tail and autopilot causes enhancement of both AFP and GFP. Research limitations/implications Approval of Directorate General of Civil Aviation in Republic of Türkiye is vital for carrying out UAV flight tests. Originality/value Generating an innovative solution to enhance both AFP and GFP of a piston-prop TUAV and also composing an innovative algorithm for the use of simultaneous and stochastic TUAV’s powerplant system, vertical tail and autopilot redesign.

The US armed forces will plan and conduct operational maneuvers from the sea and sustained operations ashore. These deployments will require accurate target acquisition and designation beyond line-of-sight, under limited visibility, and during day or night engagements. A laser target designator/rangefinder boresighted to a FLIR or day camera can perform accurate ranging and target acquisition from an unmanned aerial vehicle (UAV) platform. To meet the mission needs of the US Marine Expeditionary Warfighters, the US Army's and Navy's need for precision targeting for Hellfire, Copper Head, and other laser guided ordinance, the UAV Joint Project Office is evaluating NDI laser designator payloads for UAV application. During the upcoming laser designator evaluation on the Hunter UAV, critical laser parameters will be measured: beam dispersion, energy level, pulse width, stability, amplitude, jitter and payload autotrack performance. The purpose of this and future demonstrations/evaluations is to characterize the laser designators/rangefinders performance on various targets at various ranges. These tests will determine the maximum effective range versus target types and UAV stability parameters. Test data will provide valuable information for future laser designator/rangefinder demonstrations on the Tactical UAV, Predator, and Pioneer.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Unmanned aerial vehicle (UAV) reconnaissance is very stealthy and can break through enemy air defense system to conduct close-in reconnaissance. In particular, small and micro tactical UAVs are light in structure and have low requirements on operating environment. Besides, they are not afraid of casualties, flexible and highly intelligent in operational styles, and have derived many new combat methods and operational styles. Laser can effectively interfere or blind photoelectric sensor, is an important means to counter reconnaissance UAV. In this paper, laser damage research is carried out for typical reconnaissance UAVs, its weakness is analyzed, and the main methods of laser countermeasure reconnaissance UAVs are summarized.

Planning long duration missions for autonomous unmanned aerial vehicles (UAVs) in dynamic environments has proven to be a challenging problem. Autonomous UAVs must be able to reason about how to best accomplish mission objectives in the face of evolving mission conditions and environmental uncertainty. For example, tactical UAV missions consist of executing multiple interdependent tasks such as: locating, identifying, and prosecuting targets; responding to dynamic (i.e. pop-up) threats; motion planning with kinematic and dynamic constraints; and/or acting as a communication relay. The resulting planning problem is then one over a large and stochastic state space due to the size of the mission environment and the number of (un)known objects within that environment. The world state is also only partially observable due to the inherent limitations and stochastic nature of sensing and actuation. This requires us to plan in the belief space, which is a probability distribution over all possible states. Some a priori contextual world knowledge, like terrain maps, target locations, and threats, is available via satellite imagery based maps. But it is likely threat and target information will be “old” data by execution time in a rapidly changing mission environment. This makes classic approaches to a priori task, or symbolic, planning a poor choice of tool in that task dependent decisions are made without full knowledge of the mission environment. In addition, task planners traditionally do not have methods for tightly coupling geometric planning problems with the high level task planning, as most approaches treat these as separate planning problems. However, modern belief space geometric planning tools, like Partially Observable Markov Decision Problem (POMDP) based formulations, become intractable for large state spaces, such as the tactical UAV mission discussed in this paper. Simply developing a set of ordered tasks, as those produced by purely symbolic approaches like hybrid hierarchical task networks, provides a means of generating high-level plans in domains where many different types of actions are possible, as in our domain. But, those methods do not take into account the geometric constraints that may arise while trying to perform a mission task (e.g. fly a path from point A to point B subject to our knowledge regarding potential threats in the world). Unstructured and partially observable mission environments with unexpected events create the need for a tactical UAV to reason in both the domain geometry as well as the task space. For example, if a UAV is tasked with locating a target, it must ensure that there are no threats preventing the completion of this task and monitor this condition throughout the flight. If a threat does pop-up, it must be determined if its location endangers the mission with some probability. If it does, then the threat must be avoided or additional actions must be planned to neutralize the danger with some certainty. All of these potential responses, or additional actions, require motion planning, thus requiring both probabilistic symbolic and geometric reasoning. Geometric planning quickly becomes intractable in partially observable domains, or belief spaces, with long planning horizons. However, recent tools in the domain of robotic manipulation have approached

This paper addresses the fundamental aerodynamics, stability, and control analysis of a solar power Unmanned Aerial Vehicle (UAV) as a part of the preliminary design stage. The tactical solar-powered UAV addressed in this paper is primarily developed for intelligence, surveillance, and reconnaissance (ISR) missions at low altitude. Moreover, such design also has a dual-use capability, which could be utilized in both military and civilian domains. The aerodynamic characteristics of the UAV are obtained from both computational fluid dynamics analysis technique and the wind-tunnel testing at the design operating Reynolds number ranging from 1x105 – 4.5x105. The fundamental aerodynamics coefficient consists of lift, drag, and pitching moment variations versus the angle of attack. The stability and control analysis was carried out based on the small disturbance theory in correlation with the XFLR5 software. The results show that the tactical solar power UAV design could achieve high aerodynamic efficiency at a 4-degree angle of attack which is corresponding to the lift-to-drag ratio of 20.05. Also, the analysis results confirm that the design possesses positive static and dynamic stability at the design cruise flight condition.

Optimized charging-station placement and UAV trajectory for enhanced uncertain target detection in intelligent UAV tracking systems

The article explores the integration of Unmanned Aerial Vehicles (UAVs) within the Crisis Management System (CMS), highlighting their potential to enhance crisis response and management through operational efficiency. It examines how UAVs can be effectively employed in crisis situations, presenting updates to the CMS that incorporate UAV capabilities. The article details the functions and key components of the CMS, which spans multiple levels of governance, from central to local authorities and executive agencies. The focus is on the preventative role of UAVs in crisis scenarios. Our research demonstrates how UAVs can be utilized to prevent and mitigate the impact of natural hazards such as floods, volcanic eruptions, hurricanes, tornadoes, heavy snowfall, snow avalanches, extreme heat, and frost. The findings suggest that integrating UAVs into the CMS framework can significantly enhance the system’s ability to manage and control crises effectively and rationally. The deployment of drones in emergency situations holds significant potential to enhance and optimize relief efforts and operational processes. The article examines how UAVs can play a crucial role in modern crisis management environments, emphasizing their ability to improve emergency response. It also addresses the intersection of CMS with emerging threats such as cyber-terrorism, which is increasingly relevant in the context of cybersecurity and the digital landscape. In summary, we believe that emergency response groups can significantly benefit from the existing capabilities of UAVs. The integration of UAVs into crisis management systems offers substantial value both to affected populations and areas, as well as to disaster responders. UAVs provide a unique network for collecting and sharing critical disaster data, thereby enhancing the efficiency and effectiveness of emergency operations.

Paper discusses the modeling and control methodologies of a tactical unmanned aerial vehicle (TUAV) of coaxial rotor type with ducted fan via introduction of two-rate control and neural network methods in order to achieve more stabilized flight control. The effectiveness of the proposed single-rate and two-rate control strategies was illustrated and confirmed by numerical simulations of flight maneuvers using programming environment Simulink for chosen model of TUAV. At that earlier developed decomposition methodologies have been applied and adjusted for the actual unmanned aerial vehicle. As the TUAVs are becoming one of the most important information collection devices for the modern situation awareness (SA) systems, the achieved improvements in stabilization of flight trajectories and relevant energy economy enhancements will help to design better SA systems for search, rescue and situation reconnaissance operations.

: Existing Unmanned Aerial Vehicle (UAV) systems exhibit shortcomings in providing continuous, responsive, timely, and detailed information and targeting support to Army tactical commander's combat operations in an Army XXI battlespace. To synchronize Tactical UAV (TUAV) missions with supported operations, time is the critical element. Anything that can reduce TUAV planning time, while maintaining plan effectiveness, will expedite execution of a TUAV1s mission. Autonomous flight with some ability to avoid and evade certain threats would increase survivability further. This paper begins by presenting some of the recent successes achieved by artificial intelligence (Al) planners and schedulers on complex real-world problems. It then attempts to show how NASA's demonstrated utility of a dynamic Al planning system prototype for conducting autonomous distributed planning and execution for a team of rovers engaged in missions to achieve science goals during planetary operations can be generalized and applied to a team of TUAVs. Last, it discusses some data collection opportunities that should appear due to the ability to place increasingly more processing and data storage capabilities onboard TUAVs and some of the key challenges to use those capabilities to produce more timely and immediately usable interpretations.

PurposeThe aim of this paper is to advance autonomous flight performance of a piston-prop tactical unmanned aerial vehicle (TUAV) by simultaneously and stochastically redesigning its vertical tail and autonomous flight control system (AFCS).Design/methodology/approachA TUAV is produced in the Erciyes University Unmanned Aerial Vehicle Laboratory. Its vertical tail can be changed before flight. AFCS parameters and vertical tail parameters are simultaneously and stochastically redesigned for maximizing autonomous flight performance index using a stochastic optimization strategy. Obtained results are benefitted during simulation of autonomous flight.FindingsApplying simultaneous and stochastic design procedure for a piston-prop TUAV owing varying vertical tail and AFCS, autonomous flight performance is maximized.Research limitations/implicationsPermission of Directorate General of Civil Aviation in Republic of Turkey is crucial for flight tests of unmanned air vehicles.Originality/valueCreating a novel solution for recovering autonomous flight performance of a piston-prop TUAV and also creating a novel algorithm for application of simultaneous and stochastic TUAV’s vertical tail and its AFCS design.

Planning long duration missions for unmanned aerial vehicles (UAVs) in dynamic environments has proven to be a very challenging problem. Tactical UAVs must be able to reason about how to best accomplish mission objectives in the face of evolving mission conditions. Examples of UAV missions consist of executing multiple tasks such as: locating, identifying, and prosecuting targets; avoiding dynamic (i.e. pop-up) threats; geometric path planning with kinematic and dynamic constraints; and/or acting as a communication relay. The resulting planning problem is then one over a large and stochastic state space due to the size of the mission environment and the number of objects within that environment. The world state is also only partially observable due to sensor noise, and requires us to plan in the belief space, which is a probability distribution over all possible states. Some a priori contextual knowledge, like target and threat locations, is available via satellite imagery based maps. However, it is possible this will be "old" data by execution time. This makes classic approaches to a priori task, or symbolic, planning a poor choice of tool. In addition, task planners traditionally do not have methods for handling geometric planning problems as they focus on high level tasks. However, modern belief space geometric planning tools become intractable for large state spaces, such as ours. Recent tools in the domain of robotic manipulation have approached this problem by combining symbolic and geometric planning paradigms. One in particular, Hierarchical Planning-in-the-Now in belief space (BHPN) is a hierarchical planning technique that tightly couples geometric motion planning in belief spaces with symbolic task planning, providing a method for turning large-scale intractable belief space problems into smaller tractable ones.

On the basis of research on characteristics of downlink channel of tactical UAV (unmanned aerial vehicle) and the influence of CP to system performance, an improved adoptive CP-OFDM is introduced to overcome the decline of bit rate and waste of power efficiency caused by traditional CP-fixed OFDM when applied to tactical UAV downlink transmission. Theoretical analysis and simulation results show that, the method reduces the power loss of 0.5dB 1dB compared with CP-fixed OFDM, 0dB 0.5dB compared with original adoptive CP-OFDM, the method improves the transmission rate of 0~25% compared with CP-fixed OFDM, the improvement is 0~10% higher than original adoptive CP-OFDM.

The paper presents the concept of planning the optimal trajectory of fixed-wing unmanned aerial vehicle (UAV) of a short-range tactical class, whose task is to recognize a set of ground objects as a part of a reconnaissance mission. Tasks carried out by such systems are mainly associated with an aerial reconnaissance using Electro-Optical/Infrared (EO/IR) systems and Synthetic Aperture Radars (SARs) to support military operations. Execution of a professional reconnaissance of the indicated objects requires determining the UAV flight trajectory in the close neighborhood of the target, in order to collect as much interesting information as possible. The paper describes the algorithm for determining UAV flight trajectories, which is tasked with identifying the indicated objectives using the sensors specified in the order. The presence of UAV threatening objects is taken into account. The task of determining the UAV flight trajectory for recognition of the target is a component of the planning process of the tactical class UAV mission, which is also presented in the article. The problem of determining the optimal UAV trajectory has been decomposed into several subproblems: determining the reconnaissance flight method in the vicinity of the currently recognized target depending on the sensor used and the required parameters of the recognition product (photo, film, or SAR scan), determining the initial possible flight trajectory that takes into account potential UAV threats, and planning detailed flight trajectory considering the parameters of the air platform based on the maneuver planning algorithm designed for tactical class platforms. UAV route planning algorithms with time constraints imposed on the implementation of individual tasks were used to solve the task of determining UAV flight trajectories. The problem was formulated in the form of a Mixed Integer Linear Problem (MILP) model. For determining the flight path in the neighborhood of the target, the optimal control algorithm was also presented in the form of a MILP model. The determined trajectory is then corrected based on the construction algorithm for determining real UAV flight segments based on Dubin curves.