Reconnaissance Missions Research Articles

Design and evaluation of an acoustic liner applied to a UAV ducted rotor in hover

Unmanned aerial vehicles (UAVs) are currently being used for reconnaissance missions, tactical surveillance, and infrastructure inspection. These missions and operations could be performed near inhabited areas, possibly leading to noise complaints. For most UAVs, the rotor blades are the dominant source of noise. While some UAV concepts are equipped with rotor ducts for aerodynamic and safety reasons, few studies focus on the acoustic benefit of rotor ducts with integrated acoustic liners, similar to implementations on turbofan aircraft. The liner concept used in this study, called a Long Elastic Open Neck Acoustic Resonator (LEONAR), achieves good low frequency performance in a limited available space. The LEONAR concept extends the holes of the facesheet perforations into the resonator cavities with hollow tubes. As a result, the columns of air in the facesheet holes are prolongated without increasing facesheet thickness, thereby shifting the resonant frequencies of the liner lower. The objectives of this paper are to describe the simulation of noise radiated from a rotor in a duct with a LEONAR liner, the design of a liner integrated along the duct inner surface to mitigate radiated noise between 1000-5000 Hz, and impedance tube tests of liner samples to validate the predicted impedances.

Tour of Asteroids for Characterization Observations (TACO): A Planetary Defense Asteroid Tour Concept

Asteroid impacts potentially represent a substantial threat to humanity, but one that we can plan for and mitigate. To design an effective asteroid mitigation mission, however, it is important to have as detailed knowledge of the asteroid threat as possible. Our understanding of a newly discovered object will generally derive from our understanding of the near-Earth object population, and in cases where there is no time for a reconnaissance mission prior to deflection or disruption, we may need to lean heavily on any existing data of similar objects. The Tour of Asteroids for Characterization Observations (TACO) mission concept would fill key gaps in the characterization knowledge needed to plan an effective response to an asteroid threat. A tour targeting potentially hazardous asteroids and focused on reconnaissance objectives specifically relevant for planetary defense would also test instruments and technologies (e.g., autonomous navigation, high-rate gimbals) ahead of when they are actually required in response to a threat. Testing these capabilities is identified as a need in the National Near-Earth Object Preparedness Strategy and Action Plan. The TACO tour concept is specifically designed to measure the most important asteroid properties for planetary defense, including mass, size/shape, surface and near-surface structure, presence of satellites, and composition. These measurements can be obtained using a nominal payload, including a narrow-angle camera, a thermal infrared imager, and deployed test masses for gravity science.

An Elite Wolf Pack Algorithm Based on the Probability Threshold for a Multi-UAV Cooperative Reconnaissance Mission

In the task assignment problem of multi-UAV collaborative reconnaissance, existing algorithms have issues with inadequate solution accuracy, specifically manifested as large spatial spans and knots of routes in the task execution of UAVs. To address the above challenges, this paper presents a multi-UAV task assignment model under complex conditions (MTAMCC). To efficiently solve this model, this paper proposes an elite wolf pack algorithm based on probability threshold (EWPA-PT). The EWPA-PT algorithm combines the wandering behavior in the traditional wolf pack algorithm with the genetic algorithm. It introduces an ordered permutation problem to calculate the adaptive wandering times of the detective wolves in a specific direction. During the calling phase of the algorithm, the fierce wolves in the wolf pack randomly learn the task assignment results of the head wolf. The sieging behavior introduces the Metropolis criterion from the simulated annealing algorithm to replace the distance threshold in traditional wolf pack algorithms with a probability threshold, which dynamically changes during the iteration process. The wolf pack updating mechanism leverages the task assignment experience of the elite group to reconstruct individual wolves, thereby improving the individual reconstruction’s efficiency. Experiments demonstrate that the EWPA-PT algorithm significantly improves solution accuracy compared to typical methods in recent years.

Cooperative mission planning based on game theory for UAVs and USVs heterogeneous system in dynamic scenario

PurposeThe purpose of this paper is to present and implement a task allocation method based on game theory for reconnaissance mission planning of UAVs and USVs system.Design/methodology/approachIn this paper, the decision-making framework via game theory of mission planning is constructed. The mission planning of UAVs–USVs is transformed into a potential game optimization problem by introducing a minimum weight vertex cover model. The modified population-based game-theoretic optimizer (MPGTO) is used to improve the efficiency of solving this complex multi-constraint assignment problem.FindingsSeveral simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches.Research limitations/implicationsSeveral simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches.Practical implicationsThe proposed framework and algorithm are expected to be applied to complex real scenarios with uncertain targets and heterogeneity.Originality/valueThe decision framework via game theory is proposed for the mission planning problem of UAVs–USVs and a MPGTO with swarm evolution, and the adaptive iteration mechanism is presented for ensuring the efficiency and quality of the solution.

Research on autonomous decision-making of UAV swarm based on neural network algorithm

This article focuses on typical unmanned aerial vehicle (UAV) cluster autonomous collaborative reconnaissance mission scenarios, constructs UAV classes in Matlab, and effectively constructs UAV autonomous decision-making models using two different algorithms; Using Extreme Learning Machines (ELM) and introducing neural network methods to solve autonomous decision-making problems for unmanned aerial vehicles. Test the performance of the model under multiple neural network algorithms, basically achieving unmanned aerial vehicles to complete flight targets based on multiple sensor parameters in unknown environments, using reinforcement learning algorithms to achieve autonomous decision-making of unmanned aerial vehicles, achieving multi parameter fusion (with parameters greater than 4) decision-making, with a decision-making time of less than 100ms, which can ensure timely decision-making while also considering the rationality of the model.

Joint Intelligence, Surveillance, and Reconnaissance Mission Collaboration with Autonomous Pilots

Joint Intelligence, Surveillance, and Reconnaissance Mission Collaboration with Autonomous Pilots

Australian SASR in Borneo: The strategic contribution of the Australian SAS Regiment (SASR) during the Konfrontasi (1965-1966)

The Konfrontasi (Indonesia-Malaysia Confrontation) signaled the first combat deployment of the Australian Special Air Service Regiment (SASR). Between 1965 and 1966, two SASR squadrons were deployed and involved in secretive Claret Operations. The SASR’s operations were concentrated in the jungles of Borneo, along the porous and mountainous borders of Sarawak and Indonesia’s Kalimantan. The SASR had conducted reconnaissance missions collecting intelligence and track-and-ambush missions inside Indonesia’s territory. The SASR was also involved in ‘hearts and minds’ campaigns, building trust and partnerships with local aborigines in remote jungle areas to serve as friendly informers. This article assesses the tactical and strategic impact of SASR’s operations on the campaign’s overarching grand strategy. The SASR’s strategic value in the Konfrontasi albeit limited at the tactical level, yielded handsome strategic utility with lasting political benefits for Australia and Malaysia. This article provides a balanced evaluation of SASR’s performance and lessons for contemporary special forces operations.

Cooperative networking and information processing system of wireless communication UAV under the background of intelligent service

AbstractIn order to solve the huge impact of the digital information age on many technical and industrial fields, a periodic fast search genetic algorithm is proposed. Based on the reconnaissance mission, this paper introduces the common allocation strategy into mission planning, and constructs the mathematical model of multi unmanned aerial vehicle (UAV) cooperative reconnaissance mission planning decision‐making. The proposed periodic fast search genetic algorithm is used to solve the problem of multi UAV cooperative reconnaissance mission planning. In 2020, the industry growth rate of global UAV technology expenditure was as high as 30.6%, and the compound growth rate of UAV in China reached 63.5%, which is enough to see the great prospect of the integrated development of UAV technology and different industries. The experiment evaluates the log verification module of UAV by comparing the two data structures of Merkle tree and linear, and the time and memory overhead of storing and verifying logs, which shows the effectiveness of the log verification scheme in this paper.

Autonomous Maneuver Planning for Small-Body Reconnaissance via Reinforcement Learning

This paper presents a reinforcement learning (RL) based approach for autonomous maneuver planning of low-altitude flybys for site-specific reconnaissance of small bodies. Combined with Monte Carlo tree search and deep neural networks, the proposed method generates optimal maneuvers, even under complex dynamics and abstractly science goals. Formulating the mission objective as an observability function, the RL issue can be framed in terms of a Markov decision process. The neural network, trained by a novel policy gradient algorithm with a clipped surrogate objective, learns both policy and value functions that map the action and state spaces to the expected long-term return. An adaptive refinement search technique is applied to further enhance the trained policy network, finding optimal maneuvers from the policy distributions. Experiment results on a simulated reconnaissance mission around asteroid Itokawa illustrate the efficiency and robustness of the proposed approach in achieving multitarget observation.

Optimizing ISR Capabilities: A Comparative Analysis of Unmanned Aerial Vehicles

Military drones, or Unmanned Aerial Vehicles (UAVs), have revolutionized modern warfare by offering a safer, more cost-effective alternative to manned aircraft for intelligence, surveillance, and reconnaissance (ISR) missions. This paper reviews three prominent reconnaissance drones: the IAI Heron, the Textron Shadow, and the EADS Barracuda RQ-4. It compares their capabilities, focusing on factors like altitude, endurance, payload capacity, and mission applications. The paper concludes by highlighting the importance of selecting the right drone for specific mission requirements and acknowledges the ongoing development of drone technology and the ethical considerations surrounding their use.

Seismic performance of buildings during the magnitude 7.3 Kermanshah, Iran earthquake

In this paper, the findings and data of a large-scale reconnaissance mission following the November 12, 2017, M7.3 Kermanshah earthquake near the Iran-Iraq border are presented. The earthquake affected multiple cities in both countries and led to widespread damage and notable casualties and fatalities. The highest peak ground acceleration was recorded in Sarpol-e Zahab at 0.7g. As part of two trips, the data on nearly 1200 buildings in eight cities were collected by devising a sampling scheme to maximize the coverage of the survey, which was then employed to compile regional damage statistics. The spatial distribution of damage is presented for the entire region as well as various cities and neighborhoods in each city. The damage statistics are also disaggregated based on structural building, occupancies, and risk categories. The damage sustained by various categories of building components and contents is also assessed. In addition, the damage at the component level is closely examined for the structural and nonstructural components of severely damaged buildings to reveal the underlying causes that root in poor construction practices, lax enforcement of the building code, and inadequate seismic design provisions. Finally, the damage to hospitals in the region is investigated. This data, which can be augmented through a similar approach in the aftermath of probable future earthquakes, is key in calibrating the regional risk and resilience analysis of communities.

Autonomous Navigation UAVs for Enhancing Information Freshness for Reconnaissance

The demand for autonomous navigation UAVs in reconnaissance is steadily increasing. One crucial aspect of these missions is the freshness of reconnaissance information, which serves as a vital indicator of mission effectiveness. However, there is a lack of targeted investigation in the research on autonomous single/multi-UAV missions and joint path planning. Furthermore, the use of visual–inertial odometry (VIO) in rotary-wing UAVs can lead to significant positional drift during flight, which may result in the loss of the UAV and mission failure. This paper investigates joint planning problems in single/multi-UAV reconnaissance missions under both GPS-available and GPS-unavailable scenarios and proposes an integrated data collection and beacon-assisted localization approach. Finally, the numerical results demonstrate the effectiveness of the proposed scheme in enhancing the freshness of reconnaissance information.

RECONNAISSANCE AND STRIKE SYSTEMS IN NETWORK-CENTRIC WARFARE

The article reveals the essence of the concept of network-centric warfare, outlines the main requirements for network-centric military control systems when they are used in modern wars (combat operations) to ensure automated control of high-tech armed forces. This article examines the current state and prospects for the development of intelligence-strike systems. Reconnaissance and strike systems are key elements of modern military operations, providing the ability to conduct reconnaissance missions and conduct strikes against enemy targets. The article analyzes modern reconnaissance and strike systems, including unmanned aerial vehicles, cruise missiles and other means. Their technical characteristics, possibilities and limitations are studied. Also considered are the main challenges and trends that affect the development of intelligence-strike systems, such as the expansion of the use of artificial intelligence and the increase of autonomy of systems. This article examines the role of intelligence-strike systems in the context of network-centric warfare. In modern military conflicts, where information technology and network interaction play a key role, reconnaissance and strike systems are becoming an integral part of strategic planning and combat effectiveness. The article analyzes the impact of network-centric warfare on intelligence and strike systems. The main characteristics and requirements for these systems for effective operation in the conditions of network warfare are considered. The latest technologies that can increase the effectiveness of intelligence-strike systems in such conditions are being investigated, including artificial intelligence, data analytics and autonomy. Based on the analysis, the article provides recommendations for the further development of reconnaissance and strike systems. Ways to improve their effectiveness, to ensure better coordination between reconnaissance and strike functions, and to ensure greater security and resistance to enemy countermeasures are proposed. The presented material should be used both by the management staff and scientists of the armed forces when developing operational-tactical requirements for network-centric management systems of military purpose, as well as by management bodies when evaluating their functioning in order to choose possible ways to implement certain measures to improve their ability to perform tasks as assigned.

Research on Improvement Methods for Driven System of Bio-Inspired Aircraft to Increase Flight Speed

The flapping-wing ornithopter is an aircraft that imitates the flight of birds in nature. It has significant potential and value in various fields such as surveying, search and rescue, military reconnaissance, and unmanned warfare, due to its biomimetic stealth and high efficiency in low Reynolds number flight. However, the cruising speed of current flapping-wing ornithopters is generally lower than that of birds of the same size, which seriously affects biomimicry, mission capability, and wind resistance. Increasing the cruising speed can make the aircraft fly more like a bird, improve the efficiency of reconnaissance missions and wind resistance per unit time, and has important research significance. However, the methods to increase the cruising speed of flapping-wing ornithopters are currently lacking. Firstly, this paper presents improvements to the propulsion system based on the team’s “Dove” aircraft to meet the speed requirements. The actual flapping frequency and rocking arm end torque of the “Dove” aircraft under different voltages are tested. To select and match the motor and gearbox in the propulsion system, a method for matching and selection among the motor, gearbox, and load is proposed. Finally, wind tunnel experiments and flight validations are conducted on the improved flight prototype. The wind tunnel experiments show that the increase in flapping frequency has a significant impact on thrust. The trimmed states at different speeds are obtained. The flight validation demonstrates the sustained high-speed flight capability of the aircraft. At a flapping frequency of approximately 15 Hz, the average flight speed of the aircraft is 13.3 m/s within a 15 min duration, which is close to the actual flight speed of pigeons. The duration of high-speed flight is tripled compared to the initial duration. The speed improvement successfully enhances the biomimicry and efficiency of reconnaissance missions per unit time for the aircraft.

Research on Combat Mission Configuration of Unmanned Aerial Vehicle Maritime Reconnaissance Based on Particle Swarm Optimization Algorithm

In recent years, in the classic battles and armed conflicts around the world, battlefield environment reconnaissance and the collection and processing of operational information play an increasingly critical role in the victory and defeat of the battlefield. Unmanned equipment, especially UAV equipment, is used by more and more countries in the field of combat reconnaissance. Meanwhile, the types of UAV are gradually diversified with the change of operational requirements. UAVs adapted to different combat environments shine brightly on the battlefield. In terms of naval battle field, due to the limitations and deficiencies of reconnaissance methods such as surface radar, UAVs play a more prominent role in combat reconnaissance. There are more scenarios for UAVs to be used in combat reconnaissance in naval battle field and higher requirements for UAVs’ combat effectiveness. Therefore, this paper takes UAVs’ naval battle reconnaissance missions as the research object. By using PSO as the research method, this paper studies the combat reconnaissance task configuration of UAVs, hoping to contribute to the improvement of UAVs’ combat reconnaissance capability and combat effectiveness.

Military Advancement of Chinese Unmanned Aerial Vehicles: Security Implications for India

The advanced Military UAVs have significantly all over the world in different form. The cost-effectiveness and political advantages of UAVs make it as one of the powerful weapon in 21st century. Economically, UAVs offer reduced vehicle costs, operational expenses, and pilot training compared to manned aircraft. Politically, they can lower the threshold for the use of force in various scenarios especially when it is used for grey warfare. Consequently, military UAVs are employed for bold intelligence, surveillance, and reconnaissance (ISR) missions, thereby providing an edge to the users in terms of information superiority, which can bolster influence over disputed territories. The PRC embarked on military UAV development as early as the 1950s, initially emulating technologies from the US and the Soviet Union. Presently, China competes with the US in UAV technology advancements. Employing a government-led top-down approach, China fosters vibrant cooperation among industries, academia, and the military to develop UAV capabilities, with a focus on expanding influence in the surrounding region. Since Xi Jinping assumed office in 2012, aggressive policies have been implemented to assert control over sea territories within the first island chain. The main agenda of Xi Jingping is making China AI enabled in all sectors especially in the area of Military Technologies. This is why military UAVs are anticipated to play a pivotal role in Xi’s Indo-Pacific strategy, where the PRC faces conflicts with neighboring countries. On the other joining hands with Pakistan, China continuously using its high-end drone technology against India to create internal security problems for India in form of drugs smuggling, proliferation of illegal arms and ammunition the This article delves into the significant development of military unmanned aerial vehicles by the People’s Republic of China and their utilization in confrontations with India.

Integrating post-event very high resolution SAR imagery and machine learning for building-level earthquake damage assessment

AbstractEarthquakes have devastating effects on densely urbanised regions, requiring rapid and extensive damage assessment to guide resource allocation and recovery efforts. Traditional damage assessment is time-consuming, resource-intensive, and faces challenges in covering vast affected areas, often limiting timely decision-making. Space-borne synthetic aperture radars (SAR) have gained attention for their all-weather and day-night imaging capabilities. These advantages, coupled with wide coverage, short revisits and very high resolution (VHR), have created opportunities for using SAR data in disaster response. However, most SAR studies for post-earthquake damage assessment rely on change detection methods using pre-event SAR images, which are often unavailable in operational scenarios. Limited studies using solely post-event SAR data primarily concentrate on city-block-level damage assessment, thus not fully exploiting the VHR SAR potential. This paper presents a novel method integrating solely post-event VHR SAR imagery and machine learning (ML) for regional-scale post-earthquake damage assessment at the individual building-level. We first used supervised learning on case-specific datasets, and then introduced a combined learning approach, incorporating inventories from multiple case studies to assess generalisation. Finally, the ML model was tested on unseen study areas, to evaluate its flexibility in unfamiliar contexts. The method was implemented using datasets collected during the Earthquake Engineering Field Investigation Team (EEFIT) reconnaissance missions following the 2021 Nippes earthquake and the 2023 Kahramanmaraş earthquake sequence. The results demonstrate the method’s ability to classify standing and collapsed buildings, achieving up to 72% overall accuracy on unseen regions. The proposed method has potential for future disaster assessments, thereby contributing to more effective earthquake management strategies.

Optimizing time-varying performance and mission aborting policy in resource constrained missions

Intensive efforts have been devoted to mission aborting systems. However, the existing models mostly assumed static performance or failed to consider limited resources (e.g., energy, budget). Motivated by practical applications like unmanned aerial vehicles (UAV), this paper relaxes those assumptions by modeling a resource-constrained system that must complete a required amount of work for a successful mission and accomplish further a return/rescue phase (RP) to survive the system. The operation phase (OP) of the mission may be aborted depending on the number of external shocks (e.g., electromagnetic interferences, radiations) the system has survived and the operation time elapsed, followed by a RP to save the asset. Probabilistic methods are proposed to evaluate the mission success probability (MSP) and system survival probability (SSP). An optimization problem is formulated and solved, which determines the joint optimal time-varying performance policy and OP aborting policy, maximizing the MSP while providing a required level of SSP. A case study of UAV executing a reconnaissance mission is carried out to demonstrate the suggested model and examine influence of the SSP level as well as shock parameters on the mission performance and optimal policies. The advantage of time-varying performance in enhancing the MSP is also demonstrated.

Design and Flight Test of a Tube-Launched Unmanned Aerial Vehicle

Unmanned aerial vehicles (UAVs) have already proven valuable for intelligence, search, and reconnaissance missions; however, their integration into manned aircraft to augment existing capabilities is still an emerging field. This paper describes the design of an aircraft that fits inside a G-sized sonobuoy canister, deploys from a manned aircraft in-flight, and flies for up to 111 km and 83 min while providing telemetry to a remote operator. While UAVs with similar performance requirements exist, most were designed to fit in larger canisters. Multiple UAVs can be deployed in the air to expand the search capabilities of manned aircraft, ultimately allowing a larger search area per cost compared to manned aircraft alone. Individual performance characteristics of the aircraft such as aerodynamics, weight, propulsion, and stability were developed in the preliminary design phase based on given performance requirements. The performance of the aircraft was assessed using analytical and empirical methods. Wing folding mechanisms were prototyped for use on the production aircraft for flight testing. Propulsion, aerodynamic, and structural capabilities were validated separately using experimental methods. The folding mechanisms used in this UAV allow it to achieve the benefits of a longer wingspan while remaining compact and easy to deploy.

The EEFIT Remote Sensing Reconnaissance Mission for the February 2023 Turkey Earthquakes

The EEFIT Remote Sensing Reconnaissance Mission for the February 2023 Turkey Earthquakes