Suppression Of Enemy Air Defense Research Articles

Potemkin on the Dnieper: the Failure of Russian Airpower in the Ukraine war

ABSTRACT Russia’s airpower failure in the Ukraine war was due to incompetent air campaigning and execution, coupled with the success of a highly effective Ukrainian ground-based air defense. The Russian Air Forces (VKS) attempted to execute what they term a ‘Strategic Air Operation’ based on a ‘non-contact’ doctrine, articulated widely in recent years. But they could not achieve this in practice. As a result, like the famous Potemkin Village of Catherine the Great’s time, Russia’s Air Force today is only a façade of a modern twenty-first-century Air Force. Throughout the first eight months of the invasion, Russia failed to achieve air superiority, failed at suppression of enemy air defense, and failed to deny the use of airpower to its adversary. The absence of Russian airpower was prominently on display during the September 2022 counterattack in the Kharkiv area, where Ukraine took back 3,000 plus square miles of its territory and again with the counteroffensives in the south, where Ukraine retook Kherson. This paper explores both the ‘how’ of Russia’s airpower failure along with ‘why’ it could not execute its own stated doctrine.

Deep Reinforcement Learning for UAV Intelligent Mission Planning

Rapid and precise air operation mission planning is a key technology in unmanned aerial vehicles (UAVs) autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning (DRL) is proposed to solve the shortcomings of the traditional intelligent optimization algorithm, such as relying on simple, static, low-dimensional scenarios, and poor scalability. Specifically, the suppression of enemy air defense (SEAD) mission planning is described as a sequential decision-making problem and formalized as a Markov decision process (MDP). Then, the SEAD intelligent planning model based on the proximal policy optimization (PPO) algorithm is established and a general intelligent planning architecture is proposed. Furthermore, three policy training tricks, i.e., domain randomization, maximizing policy entropy, and underlying network parameter sharing, are introduced to improve the learning performance and generalizability of PPO. Experiments results show that the model in this work is efficient and stable, and can be adapted to the unknown continuous high-dimensional environment. It can be concluded that the UAV intelligent mission planning model based on DRL has powerful intelligent planning performance, and provides a new idea for researching UAV autonomy.

Unmanned Aerial Vehicle Swarm Cooperative Decision-Making for SEAD Mission: A Hierarchical Multiagent Reinforcement Learning Approach

Unmanned aerial vehicle (UAV) swarm cooperative decision-making has attracted increasing attentions because of its low-cost, reusable, and distributed characteristics. However, existing non-learning-based methods rely on small-scale, known scenarios, and cannot solve complex multi-agent cooperation problem in large-scale, uncertain scenarios. This paper proposes a hierarchical multi-agent reinforcement learning (HMARL) method to solve the heterogeneous UAV swarm cooperative decision-making problem for the typical suppression of enemy air defense (SEAD) mission, which is decoupled into two sub-problems, i.e., the higher-level target allocation (TA) sub-problem and the lower-level cooperative attacking (CA) sub-problem. We establish a HMARL agent model, consisting of a multi-agent deep Q network (MADQN) based TA agent and multiple independent asynchronous proximal policy optimization (IAPPO) based CA agents. MADQN-TA agent can dynamically adjust the TA schemes according to the relative position. To encourage exploration and promote learning efficiency, the Metropolis criterion and inter-agent information exchange techniques are introduced. IAPPO-CA agent adopts independent learning paradigm, which can easily scale with the number of agents. Comparative simulation results validate the effectiveness, robustness, and scalability of the proposed method.

Cooperative Task Assignment of a Heterogeneous Multi-UAV System Using an Adaptive Genetic Algorithm

The cooperative multiple task assignment problem (CMTAP) is an NP-hard combinatorial optimization problem. In this paper, CMTAP is to allocate multiple heterogeneous fixed-wing UAVs to perform a suppression of enemy air defense (SEAD) mission on multiple stationary ground targets. To solve this problem, we study the adaptive genetic algorithm (AGA) under the assumptions of the heterogeneity of UAVs and task coupling constraints. Firstly, the multi-type gene chromosome encoding scheme is designed to generate feasible chromosomes that satisfy the heterogeneity of UAVs and task coupling constraints. Then, AGA introduces the Dubins car model to simulate the UAV path formation and derives the fitness value of each chromosome. In order to comply with the chromosome coding strategy of multi-type genes, we designed the corresponding crossover and mutation operators to generate feasible offspring populations. Especially, the proposed mutation operators with the state-transition scheme enhance the stochastic searching ability of the proposed algorithm. Last but not least, the proposed AGA dynamically adjusts the number of crossover and mutation populations to avoid the subjective selection of simulation parameters. The numerical simulations verify that the proposed AGA has a better optimization ability and convergence effect compared with the random search method, genetic algorithm, ant colony optimization method, and particle search optimization method. Therefore, the effectiveness of the proposed algorithm is proven.

Cooperative Multiple Task Assignment of Heterogeneous UAVs Using a Modified Genetic Algorithm with Multi-type-gene Chromosome Encoding Strategy

The cooperative multiple task assignment problem (CMTAP) of heterogeneous fixed-wing unmanned aerial vehicles (UAVs) performing the Suppression of Enemy Air Defense (SEAD) mission against multiple ground stationary targets is studied in this paper. The CMTAP is a NP-hard combinatorial optimization problem, which faces many challenges like problem scale, heterogeneity of UAVs (different capability and maneuverability), task coupling and task precedence constraints. To address this issue, we proposed a modified genetic algorithm (GA) with multi-type-gene chromosome encoding strategy. Firstly, the multi-type-gene encoding scheme is raised to generate feasible chromosomes that satisfy the UAV capability, task coupling and task precedence constraints. Then, Dubins car model is adopted to calculate the mission execution time (objective function of CMTAP model) of each chromosome, and make each chromosome conform to the UAV maneuverability constraint. To balance the searching ability of algorithm and the diversity of population, we raise the modified crossover operator and multiple mutation operators according to the multi-type-gene chromosome encoding. The simulation results demonstrate that the modified GA has better optimization performance compared with random search method, ant colony optimization method and particle search optimization method.

Market-Based Task Assignment for Cooperative Timing Missions in Dynamic Environments

New market-based decentralized algorithms are proposed for the task assignment of multiple unmanned aerial vehicles in dynamic environments with a limited communication range. In particular, a cooperative timing mission that cannot be performed by a single vehicle is considered. The baseline algorithms for a connected network are extended to deal with time-varying network topology including isolated subnetworks due to a limited communication range. The mathematical convergence and scalability analyses show that the proposed algorithms have a polynomial time complexity, and numerical simulation results support the scalability of the proposed algorithm in terms of the runtime and communication burden. The performance of the proposed algorithms is demonstrated via Monte Carlo simulations for the scenario of the suppression of enemy air defenses.

벡터필드 유도기법이 적용된 다수 무인기를 이용한 적 방공망 제압 임무의 실험적 검증

현대전에서 적 방공망 제압 임무는 과거에 비해 그 중요성이 점차 부각되고 있다. 그러나 본 임무는 적의 방공망에 아군의 항공기와 조종사가 노출되는 위험을 안고 있어, 아군의 피해를 최소화하면서 효과적으로 임무를 수행할 수 있도록 하는 연구가 활발히 진행되고 있다. 본 논문에서는 이러한 연구의 일환으로써, 벡터필드 유도기법을 이용하여, 다수 무인기의 적 방공망 제압 임무를 설계하였다. 벡터필드 유도기법은 다른 유도기법들에 비하여 계산 량이 적고 쉽게 응용이 가능한 이점이 있어, 다수 무인기 운용에 해당 기법을 적용하는 경우 무인기간의 상호 협동을 통한 다양한 형태의 임무를 계획할 수 있다. 따라서 벡터필드 유도기법을 이용한 다수 무인기의 적 방공망 제압 임무의 설계방법을 제시함과 동시에, 비행실험을 통하여 그 운용 가능성을 확인해 보았다. In modern warfare, the importance of SEAD(Suppression of Enemy Air Defense) mission is being emphasized. However, this mission runs the risk of hull damage or casualties of our friendly air forces. Because of these risks, research on the way of minimizing damages and enhancing mission capability is under active discussion. As a part of this research, SEAD mission planning with multiple UAVs has been covered using vector field guidance. This guidance method not only applies to various forms of flight path but also requires less computational power than other guidance methods. Therefore, in this paper, planning methods of SEAD mission for multiple UAVs using vector field guidance and experimental data from flight experiments regarding designed mission has been covered.

Optimal resource management algorithm for unmanned aerial vehicle missions in hostile territories

In this paper, an optimal distribution algorithm for a large group of heterogeneous unmanned aerial vehicles is developed. A typical unmanned aerial vehicle cooperative control in a battlefield can be categorized as a hierarchical system that is usually composed of several levels, and the decision making step, or the resource management step, is the main focus of this paper. In the resource management step, the factors to be decided are the proper number and types of unmanned aerial vehicles that will be committed to each operational area to increase the overall performance of the entire group and achieve a successful mission accomplishment. A task assignment algorithm, which is the next level in the cooperative control hierarchy, may begin with a higher chance of success when the number and types of resources are given correctly by the resource management step. This research suggests an optimal resource management algorithm for operations in various combat or civilian missions by solving an integer linear programming problem. A Suppression of Enemy Air Defense (SEAD) mission is considered as the main example in this paper. Finally, the algorithm is supported with number of verifications and numerical simulations in various SEAD mission cases.

Multilevel Coalition Formation Strategy for Suppression of Enemy Air Defenses Missions

This paper investigates the problem of how to form coalitions in teams of heterogeneous vehicles. In particular, a coordination strategy is designed for unmanned vehicles to autonomously carry out the suppression of enemy air defenses (SEAD) mission, which would benefit from a heterogeneous network of unmanned vehicles to search and destroy threats in an unexplored area. Inspiration for this work is drawn from natural systems, and we consider the alliance-forming behavior of bottlenose dolphins as a guiding example. A two-phased approach is taken to develop the bioinspired strategy. First, in the context of multi-agent systems, a mathematical model is produced that expressively captures the alliance-forming behavior. Next, this model is tailored to the suppression of enemy air defenses mission: the target application. Advantages of using this bioinspired approach are discussed and simulations are provided to demonstrate its operation.

Intermediate carriers for UAV swarms: Problem of fleet composition

This article introduces a fleet composition algorithm for a fleet of intermediate carriers, which should deliver a swarm of miniature unmanned aerial vehicles (mini-UAVs) to a mission area. The algorithm is based on the sequential solution of several knapsack problems with various constraints. The algorithm allows both to form an initial set of required types of intermediate carriers, and to generate a fleet of intermediate carriers. The formation of a fleet of intermediate carriers to solve a suppression of enemy air defense (SEAD) problem is presented to illustrate the proposed algorithm.

CBBA 기반 SEAD 임무를 위한 이종무인기의 분산형 임무할당 알고리듬 연구

This paper presents a distributed task assignment algorithm for the suppression of enemy air defense (SEAD) mission of heterogeneous UAVs, based on the consensus-based bundle algorithm (CBBA). SEAD mission can be modeled as a task assignment problem of multiple UAVs performing multiple air defense targets, and UAVs performing SEAD mission consist of the weasel for destruction of enemy`s air defense system and the striker for the battle damage assessment (BDA) or other tasks. In this paper, a distributed task assignment algorithm considering path-planning in presence of terrain obstacle is developed for heterogeneous UAVs, and then it is applied to SEAD mission. Through numerical simulations the performance and the applicability of the proposed method are tested.

Looking Beyond the J-UCAS's Demise

The objective pursued by J-UCAS advocates was to promote the definition of uninhabited systems dedicated to a wide array of dull, duty and dangerous missions, including suppression of enemy air defenses (SEAD), surveillance, precision strike and, last but not least, combat operations. What was remarkable about the J-UCAS is that it involved not only a different way of thinking about air systems, but also a radical breakthrough in the way the whole process of engineering and development has to be thought of. Now terminated as a program on its own, the J-UCAS 'experiment' appears to be an ideal test-bed in order to demonstrate how military technological programs, especially those initiated in order to explore innovative and experimental capabilities, are socially constructed. Since the inception of the J-UCAS, great confusion seems to have existed as regards both the origins and the objectives of the project.

A General Method of Measuring Interoperability and Describing Its Impact on Operational Effectiveness

A general method of measuring the interoperability of a heterogeneous set of systems, experiencing any type and number of interoperations, in the context of an operational process is given. Furthermore, for confrontational operations (friendly versus adversary), the method gives sufficient conditions for relating the interoperability measurement to operational effectiveness. Owing to the difficulty in creating a general method of interoperability measurement, developers of extant interoperability assessment methods have relied upon problem decomposition to produce methods of assessing the interoperability of specific types of systems experiencing distinct modes of interoperation. Unfortunately, this approach fractured the problem, effectively driving them further from the solution.Therefore, in this research, a holistic, fundamental, and flexible means of describing a general method of interoperability measurement was undertaken which models systems according to their interoperability-related features in the context of an operational process.An application of the method highlights the new concept of confrontational interoperability, demonstrates the relationship between interoperability and operational effectiveness in the context of a suppression of enemy air defenses (SEAD) scenario, and illustrates how an interoperability measurement can motivate system upgrades.

Suppression of enemy air defenses (SEAD) as an information duel

AbstractBlue strike aircraft enter region ℛ to attack Red targets. In Case 1, Blue conducts (preplanned) SEAD to establish air superiority. In the (reactive) SEAD scenario, which is Case 2, such superiority is already in place, but is jeopardized by prohibitive interference from Red, which threatens Blue's ability to conduct missions. We utilize both deterministic and stochastic models to explore optimal tactics for Red in such engagements. Policies are developed which will guide both Red's determination of the modes of operation of his engagement radar, and his choice of Blue opponent to target next. An index in the form of a simple transaction kill ratio plays a major role throughout. Published 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 723–742, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10046

Modeling and analysis of air campaign resource allocation: a spatio-temporal decomposition approach

In this paper, we address the modeling and analysis issues associated with a generic theater level campaign where two adversaries pit their military resources against each other over a sequence of multiple engagements. In particular, we consider the scenario of an air raid campaign where one adversary uses suppression of enemy air defense (SEAD) aircraft and bombers (BMBs) against the other adversary's invading ground troops (GTs) that are defended by their mobile air defense (AD) units. The original problem is decomposed into a temporal and a spatial resource allocation problem. The temporal resource allocation problem is formulated and solved in a game-theoretical framework as a multiple resource interaction problem with linear attrition functions. The spatial resource allocation problem is posed as a risk minimization problem in which the optimal corridor of ingress and optimal movement of the GTs and AD units are decided by the adversaries. These two solutions are integrated using an aggregation/deaggregation approach to evaluate resource strengths and distribute losses. Several simulation experiments were carried out to demonstrate the main ideas.

Ergonomics of UAV/UCAV Mission Success: Considerations for Data Link, Control, and Display Issues

The United States Armed Forces are turning to Unmanned Aerial Vehicles (UAVs), and their combat-modified counterparts (UCAVs), to support human pilots and improve electronic surveillance and weapons deployment capabilities, particularly for risky suppression of enemy air-defense (SEAD) operations. Successful implementation and integration of UAVs/UCAVs into today's military requires focused attention toward human factors issues to ensure mission success and efficiency. In addition to considerations for satellite links, positive target verification, and assurance of collateral safety prior to weapon(s) launch, a primary concern is how operators interact with the UAV/UCAV system. This paper describes a portion of these concerns by presenting human factors considerations for UAV/UCAV-operator data links, vehicle control, and operator display issues. Relevant empirical findings are reviewed and implications for training and systems design are outlined.

The Potential Impact of Utilizing Advanced Engine Technology for a Combat Capable Unmanned Air Vehicle (UAV)

This paper evaluates the potential impact of utilizing advanced engine technology for a limited life, combat capable, unmanned air vehicle (UAV) application. A study was conducted to define payoffs in terms of mission capability and system level life cycle costs (LCC) associated with implementing three different engine development approaches into a combat capable UAV design. The three different approaches considered were: a new, advanced technology engine; an existing (off-the-shelf) engine; and a derivative of an existing engine with limited technology insertion. A detailed vehicle configuration design was developed to conduct this assessment, including a low observable (LO), highly integrated engine/airframe layout for survivability and mission adaptable considerations. The vehicle is designed with multirole mission capability such as suppression of enemy air defense (SEAD), close air support (CAS), and battlefield air interdiction (BAI). A system level performance comparison is assessed with the three different engine approaches, specifically for the SEAD-type mission. For the cost analysis, the multirole mission capability is reflected in the overall assumptions such as in the number of aircraft needed to meet the mission requirements. A system level assessment such as in this study is essential in determining whether the additional costs associated with the development of a new, advanced engine is worth the investment. The results of this study suggest that advanced engine technology insertion can provide significant benefits in terms of mission range capability, vehicle weight/size, and overall life cycle costs versus an existing engine.