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

International audienceno abstrac

A Survey on Weapon Target Allocation Models and Applications

In Command and Control (C2), Threat Evaluation (TE) and Weapon Target Allocation (WTA) are two key components. To build an automated system in this area after modeling Threat Evaluation and Weapon Target Allocation processes, solving these models and finding the optimal solution are further important issues. This setting demands instantaneous operational planning and decision making under inherent severe stress conditions. The associated responsibilities are usually divided among a number of operators and also computerized decision support systems that aid these operators during the decision making process. In this Chapter, the literature in the area of WTA system with the emphasis on the modeling and solving methods are surveyed

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

An Approximate Dynamic Programming Approach for Comparing Firing Solutions in a Networked Air Defense Environment

The United States Army currently employs a shoot-shoot-look firing policy for air defense. As the Army moves to a networked defense-in-depth strategy, this policy will not provide optimal results for managing interceptor inventories in a conflict to minimize the damage to defended assets. The objective for air and missile defense is to identify the firing policy for interceptor allocation that minimizes expected total cost of damage to defended assets. This dynamic weapon target assignment problem is formulated first as a Markov decision process (MDP) and then approximate dynamic programming (ADP) is used to solve problem instances based on a representative scenario. Least squares policy evaluation (LSPE) and least squares temporal difference (LSTD) algorithms are employed to determine the best approximate policies possible. An experimental design is conducted to investigate problem features such as conflict duration, attacker and defender weapon sophistication, and defended asset values. The LSPE and LSTD algorithm results are compared to two benchmark policies (e.g., firing one or two interceptors at each incoming tactical ballistic missile (TBM)). Results indicate that ADP policies outperform baseline polices when conflict duration is short and attacker weapons are sophisticated. Results also indicate that firing one interceptor at each TBM (regardless of inventory status) outperforms the tested ADP policies when conflict duration is long and attacker weapons are less sophisticated

Determination of Fire Control Policies via Approximate Dynamic Programming

Given the ubiquitous nature of both offensive and defensive missile systems, the catastrophe-causing potential they represent, and the limited resources available to countries for missile defense, optimizing the defensive response to a missile attack is a necessary endeavor. For a single salvo of offensive missiles launched at a set of targets, a missile defense system protecting those targets must decide how many interceptors to fire at each incoming missile. Since such missile engagements often involve the firing of more than one attack salvo, we develop a Markov decision process (MDP) model to examine the optimal fire control policy for the defender. Due to the computational intractability of using exact methods for all but the smallest problem instances, we utilize an approximate dynamic programming (ADP) approach to explore the efficacy of applying approximate methods to the problem. We obtain policy insights by analyzing subsets of the state space that reflect a range of possible defender interceptor inventories. Testing of four scenarios demonstrates that the ADP policy provides high-quality decisions for a majority of the state space, achieving a 7.74% mean optimality gap in the baseline scenario. Moreover, computational effort for the ADP algorithm requires only a few minutes versus 12 hours for the exact dynamic programming algorithm, providing a method to address more complex and realistically-sized instances

Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms

Due to their advantages in flexibility, scalability, survivability, and cost-effectiveness, drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields. This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms. Given a set of available air defense systems, the problem determines the location of each air defense system in a predetermined region, such that the cost for enemy drones to pass through the region would be maximized. The cost is calculated based on a counterpart drone path planning problem. To solve this adversarial problem, we first propose an exact iterative search algorithm for small-size problem instances, and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances. We implement the evolutionary framework with six popular evolutionary algorithms. Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms

Agent-based Modeling Methodology for Analyzing Weapons Systems

Getting as much information as possible to make decisions about acquisition of new weapons systems, through analysis of the weapons systems\u27 benefits and costs, yields better decisions. This study has twin goals. The first is to demonstrate a sound methodology to yield the most information about benefits of a particular weapon system. Second, to provide some baseline analysis of the benefits of a new type of missile, the Small Advanced Capability Missile (SACM) concept, in an unclassified general sense that will help improve further, more detailed, classified investigations into the benefits of this missile. In a simplified, unclassified scenario, we show that the SACM provides several advantages and we demonstrate a basis for further investigation into which tactics should be used in conjunction with the SACM. Furthermore, we discuss how each of the chosen factors influence the air combat scenario. Ultimately, we establish the usefulness of a designed experimental approach to analysis of agent-based simulation models and how agent-based models yield a great amount of information about the complex interactions of different actors on the battlefield