A Two-Step Optimisation Method for Dynamic Weapon Target Assignment Problem

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Approximate Dynamic Programming for Military Resource Allocation

This research considers the optimal allocation of weapons to a collection of targets with the objective of maximizing the value of destroyed targets. The weapon-target assignment (WTA) problem is a classic non-linear combinatorial optimization problem with an extensive history in operations research literature. The dynamic weapon target assignment (DWTA) problem aims to assign weapons optimally over time using the information gained to improve the outcome of their engagements. This research investigates various formulations of the DWTA problem and develops algorithms for their solution. Finally, an embedded optimization problem is introduced in which optimization of the multi-stage DWTA is used to determine optimal weaponeering of aircraft. Approximate dynamic programming is applied to the various formulations of the WTA problem. Like many in the field of combinatorial optimization, the DWTA problem suffers from the curses of dimensionality and exact solutions are often computationally intractability. As such, approximations are developed which exploit the special structure of the problem and allow for efficient convergence to high-quality local optima. Finally, a genetic algorithm solution framework is developed to test the embedded optimization problem for aircraft weaponeering

Real-Time Heuristics and Metaheuristics for Static and Dynamic Weapon Target Assignments

The problem of targeting and engaging individual missiles (targets) with an arsenal of interceptors (weapons) is known as the weapon target assignment problem. This problem has been well-researched since the seminal work in 1958. There are two distinct categories of the weapon target assignment problem: static and dynamic. The static weapon target assignment problem considers a single instance in which a known number of incoming missiles is to be engaged with a finite number of interceptors. By contrast, the dynamic weapon target assignment problem considers either follow on engagement(s) should the first engagement(s) fail, a subsequent salvo of incoming missiles, or both. This research seeks to define and solve a realistic dynamic model. First, assignment heuristics and metaheuristics are developed to provide rapid near-optimal solutions to the static weapon target assignment. Next, a technique capable of determining how many of each interceptor type to reserve for a second salvo by means of approximate dynamic programming is developed. Lastly, a model that realistically considers erratic flight paths of incoming missiles and determines assignments and firing sequences of interceptors within a simulation to minimize the number of hits to a protected asset is developed. Additionally, the first contemporary survey of the weapon target assignment problem since 1985 is presented. Collectively, this work extends the research of missile defense into practical application more so than currently is found within the literature

Investigation of Cooperative Behavior in Autonomous Wide Search Munitions

The purpose of this research is to investigate the effectiveness of wide-area search munitions in various scenarios using different cooperative behavior algorithms. The general scenario involves multiple autonomous munitions searching for an unknown number of targets of different priority in unknown locations. Three cooperative behavior algorithms are used in each scenario: no cooperation, cooperative attack only, and cooperative classification and attack. In the cooperative cases, the munitions allocate tasks on-line as a group, using linear programming techniques to determine the optimum allocation. Each munition provides inputs to the task allocation routine in the form of probabilities of successfully being able to complete the various tasks. These probabilities of success are based on statistical Poisson field theory. Weighting parameters are applied to the probabilities of success so that optimum settings can be determined via Response Surface Methodology. Results are compared within and across the various scenarios. Initial results did not reflect expected behavior (due to poor choice of responses to optimize). Experiments were modified and more desirable results obtained. In general, cooperative engagement alone attacks and kills fewer targets than no cooperation. Cooperative classification however, kills fewer targets at low false target attack rates (\u3c 0.005/sq km), but outperforms the other algorithms as the false target attack rate increases. This is due primarily to the fact that cooperative classification significantly reduces and stabilizes the effective false target attack rate

Multi-platform coordination and resource management in command and control

Depuis plusieurs années, nous constatons l'augmentation de l'utilisation des techniques d'agents et multiagent pour assister l'humain dans ses tâches. Ce travail de maîtrise se situe dans la même voie. Précisément, nous proposons d'utiliser les techniques multiagent de planification et de coordination pour la gestion de ressources dans les systèmes de commande et contrôle (C2) temps réel. Le problème particulier que nous avons étudié est la conception d'un système d'aide à la décision pour les opérations anti-aérienne sur les frégates canadiennes. Dans le cas où plusieurs frégates doivent se défendre contre des menaces, la coordination est un problème d'importance capitale. L'utilisation de mécanismes de coordination efficaces permet d'éviter les actions conflictuelles et la redondance dans les engagements. Dans ce mémoire, nous présentons quatre mécanismes de coordination basés sur le partage de tâche. Trois sont basés sur les communications : la coordination centrale, le Contract Net, la coordination similaire à celle proposée par Brown; tandis que la défense de zone est basée sur les lois sociales. Nous exposons enfin les résultats auxquels nous sommes arrivés en simulant ces différents mécanismes.The use of agent and multiagent techniques to assist humans in their daily routines has been increasing for many years, notably in Command and Control (C2) systems. This thesis is is situated in this domain. Precisely, we propose to use multiagent planning and coordination techniques for resource management in real-time \acs{C2} systems. The particular problem we studied is the design of a decision-support for anti-air warfare on Canadian frigates. In the case of several frigates defending against incoming threats, multiagent coordination is a complex problem of capital importance. Better coordination mechanisms are important to avoid redundancy in engagements and inefficient defence caused by conflicting actions. In this thesis, we present four different coordination mechanisms based on task sharing. Three of these mechanisms are based on communications: central coordination, Contract Net coordination and Brown coordination, while the zone defence coordination is based on social laws. Finally, we expose the results obtained while simulating these various mechanisms

Optimal Sensor Threshold Control and the Weapon Operating Characteristic for Autonomous Search and Attack Munitions

This Thesis considers the optimal employment of a wide area search munition in a battlespace where a target is known to be uniformly distributed among false targets which are Poisson distributed. The Poisson distribution\u27s parameter is obtained from readily available battlespace intelligence. This work formulates and solves the optimal control problem for deriving the optimal sensor threshold schedule in order to maximize the probability of attacking the target during the battlespace sweep while constraining the probability of attacking a false target. The efficiency gained by optimally varying the sensor threshold is compared against the performance achieved with a static, optimum sensor threshold setting. The Weapon Operating Characteristic, the relationship between maximum achievable probability of target attack and maximum allowable probability of false target attack, is developed

U.S. Unmanned Aerial Vehicles (UAVS) and Network Centric Warfare (NCW) impacts on combat aviation tactics from Gulf War I through 2007 Iraq

Unmanned, aerial vehicles (UAVs) are an increasingly important element of many modern militaries. Their success on battlefields in Afghanistan, Iraq, and around the globe has driven demand for a variety of types of unmanned vehicles. Their proven value consists in low risk and low cost, and their capabilities include persistent surveillance, tactical and combat reconnaissance, resilience, and dynamic re-tasking. This research evaluates past, current, and possible future operating environments for several UAV platforms to survey the changing dynamics of combat-aviation tactics and make recommendations regarding UAV employment scenarios to the Turkish military. While UAVs have already established their importance in military operations, ongoing evaluations of UAV operating environments, capabilities, technologies, concepts, and organizational issues inform the development of future systems. To what extent will UAV capabilities increasingly define tomorrow's missions, requirements, and results in surveillance and combat tactics? Integrating UAVs and concepts of operations (CONOPS) on future battlefields is an emergent science. Managing a transition from manned- to unmanned and remotely piloted aviation platforms involves new technological complexity and new aviation personnel roles, especially for combat pilots. Managing a UAV military transformation involves cultural change, which can be measured in decades.http://archive.org/details/usunmannedaerial109454211Turkish Air Force authors.Approved for public release; distribution is unlimited

Assessing the impact of low workload in supervisory control of networked unmanned vehicles

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. )122-126.This research investigated the effects of prolonged low workload on operator performance in the context of controlling a network of unmanned vehicles (UxVs) in a search, track, and destroy mission with the assistance of an automated planner. In addition, this research focused on assessing the physical, social, and cognitive coping mechanisms that operators rely upon during prolonged low workload missions. An experiment was conducted to collect data for researching the impact of low workload in human supervisory control of networked, heterogeneous UxVs. This research showed that performance was not necessarily affected at the low end of the workload spectrum, especially in the context of human supervisory control of networked UxVs. Given varying levels of low taskload, operators tended to gravitate toward a common total utilization (percent busy time) that was well above the required utilization. The boredom due to the low taskload environment caused operators to spend the majority of their time distracted; to a lesser degree, operators were more directed than divided in terms of attention. More directed attention predicted higher operator performance, especially in the tracking portion of the mission. Higher utilization predicted improved operator performance in search and destroy tasks, but hindered the automation's ability to track targets. Video gaming experience was a detriment to destroying hostile targets in this long duration, low workload mission involving human supervisory control of networked UxVs. Vigilance, shown by a decrement in amount of directed attention per hour, decreased over the course of the mission duration. Top performers had higher directed attention and coped with the boredom through extreme focus or use of switching times to stay engaged in the mission. In comparison to a moderate workload study, participants in this low workload experiment performed both better and worse. Low workload did not necessarily cause a drop in operator performance.by Christin S. Hart.S.M

Naval Research Program 2021 Annual Report

NPS NRP Annual ReportThe Naval Postgraduate School (NPS) Naval Research Program (NRP) is funded by the Chief of Naval Operations and supports research projects for the Navy and Marine Corps. The NPS NRP serves as a launch-point for new initiatives which posture naval forces to meet current and future operational warfighter challenges. NRP research projects are led by individual research teams that conduct research and through which NPS expertise is developed and maintained. The primary mechanism for obtaining NPS NRP support is through participation at NPS Naval Research Working Group (NRWG) meetings that bring together fleet topic sponsors, NPS faculty members, and students to discuss potential research topics and initiatives.Chief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.