Weapon Allocation Research Articles

The impact of political, military, and social factors on the repositioning of Belarus within the regional security architecture

The next phase of Russian aggression against Ukraine demonstrated that despite lacking a large military, territorial, population, or raw material potential, Belarus could play a significant role in the project to construct a new regional, continental, and even global security architecture. The purpose of this article is to discuss the problem of repositioning Belarus, which was affected not only by the multidimensional change in Belarusian-Ukrainian relations as a result of the war but also by long-term efforts to elevate Belarusian-EU relations. In this area, the fundamental research question is whether the war in Ukraine, the breach of the so-called Budapest Memorandum and the deepening of the multifaceted Russian-Belarusian integration within the Union State (including the militarisation of Belarus and the allocation of tactical nuclear weapons) will cause the projects for the redefinition of Belarus-EU relations (which are instrumental in reducing the intensity of political turbulence in the region) to disappear from the EU diplomatic agenda for a long time, with a direct negative impact on the future of European security. The article verifies the hypothesis that due to the specificity of the Belarusian authoritarian model, despite the systematically deepening Russian-Belarusian integration and the ever-expanding sanctions policy towards Belarus, (2) due to the shape of Belarusian-Ukrainian relations after 2014 and the periodically satisfactory Belarusian-EU relations, (1) there are still prospects for Belarus to pursue foreign policy diversification and repositioning of the European vector in it.

Mathematical Model for Solving Problem of Target Allocation of Antiaircraft Fire Weapons in AD Contour of Surface Ship (in AD System of Group of Ships) in Case of Attack Threat and Repelling Attacks of Anti-Ship Cruise Missiles

The improvement of the algorithms of processing information and controlling of weapons systems in combination with their automation, which can significantly reduce time from the moment of detection of air targets to their destruction, is a promising direction in organizing of building and effective combat use of such complex systems as the contour of antiaircraft defense (AD) of a single surface ship and the AD system of a group of the surface ships. The transition to the creation and use of the precision-guided long-range munition, e.g. anti-ship missiles (AShM), allows to destroy ships from remote lines without entering the zone of the fighter aircraft action and reach of the antiaircraft missile system. When repelling attacks of anti-ship cruise missiles, a poor-quality solution of the problem of the target allocation of the antiaircraft fire weapons of a ship (group of ships) does not allow to realize the combat capabilities of the AD contour (system) as a whole, which leads to a loss of combat capability of the ship (ships). The main difference between the proposed mathematical model for solving the target allocation problem and those implemented in the combat information and controlling systems of the surface ships is the fact that it provides the possibility of evaluating own antiaircraft fire weapons and enemy’s AShM when developing an optimal target allocation plan based on a detailed account of the AShM detection range, coordination of the destruction zones, sectors of fire, lines of the implementation of the AShM tasks, mutual security measures, electromagnetic compatibility of the electronic means, i.e. taking into account the existing zones of the destruction of the antiaircraft fire weapons, as well as solving the problem of the target allocation in time. The solution of the task is carried out both directly during the antiaircraft combat, and in the case of the attack threat of the AShM. In its turn, the last one makes it possible to optimize building of the antiaircraft fire covering system and the AD system of both a single surface ship and the group of the surface ships as a whole, and to optimize the solution of the target allocation problem, in particular. The presented mathematical model and the algorithm and program developed on its basis can be recommended after appropriate modification for inclusion in both automated combat control systems and respectively combat information and control systems of connections of surface ships and single ships, that allows to solve the problem of the target allocation on a time scale close to real.

CONSIDERATIONS ON THE DESIGN OF THE AIR DEFENCE RESPONSE IN THE CURRENT AIRSPACE

The unprecedented technological development in the field of weapons and ammunition used in the airspace, at low and very low altitudes, is one of the major challenges that the traditional air defence systems have to face. To this end, the military specialists of the modern armies pay special attention to the new types of air threats and seek solutions to maintain a balanced situation in the field of airspace security. In this regard, a number of transformations of the VSHORAD, SHORAD air defence missile systems are identified. The technical and tactical solutions mainly refer to increasing the capabilities of the weapons and ammunition systems, which can be used in achieving the specific algorithm of countering an air threat by applying certain specific methods, TEWA (Threat Evaluation and Weapons Allocation). This scientific approach highlights the possibilities of taking short and very short range air action as a starting point in the current development of the air defence systems, which by applying the operational art can temporarily compensate for the technological gap between the air threats and the air defence response.

A Novel Heterogeneous Sensor-Weapon-Target Cooperative Assignment for Ground-to-Air Defense by Efficient Evolutionary Approaches

Based on the tactics of “intercepting at the furthest and resisting in steps” in the ground-to-air defense, an integrated sensor/heterogeneous weapon allocation problem is proposed for the first time. It is formulated as a dynamic sensor/heterogeneous weapon target assignment (DS/HWTA) model in continuous time domain, including the models of the threat assessment, sensor detection probability, weapon damage probability, decision timing, unified optimization objective and constraints. An evolutionary algorithm for dynamic sensor/heterogeneous weapon target assignment (EA-DS/HWTA) is proposed, in which three coding methods are designed to build the solution individuals. The de-constrained initial population is realized based on type I coding. A modified position based crossover (MPBX) based on type II coding is presented to maintain the sensor-weapon synergy of the solutions. Based on a greedy fitness strategy, the type II coding individual is complemented and transformed to type III coding to calculate the solution fitness. The extensive experiments show that the DS/HWTA model reflects the cooperation requirements of sensor-weapon in anti-penetration operation, and the proposed solving algorithm has good convergence and real-time performance.

정적 무기할당 알고리즘 평가를 위한 시뮬레이터 설계

This paper deals with a simulator that evaluates weapon allocation algorithms for missile defense systems. A combat system that allows for an immediate response to a rapidly developing offshore air threat is essential, and weapon allocation is vital. To achieve this, it is necessary to evaluate and improve the existing threat evaluation and weapon allocation algorithms. However, the existing weapon allocation algorithm has been assumed to be static, because it is not easy to fill the gap of real time due to elements such as time and distance. Thus, we introduce a concept of unit time in the simulation and design a simulator that can evaluate the weapon allocation algorithm more realistically by covering the gap between the static weapon allocation algorithm and the real-time dynamic battlefield environment. Based on this, we evaluate the existing algorithms.

Optimal multi-stage allocation of weapons to targets using adaptive dynamic programming

We consider the optimal allocation of resources (weapons) to a collection of tasks (targets) with the objective of maximizing the reward for completing tasks (destroying targets). Tasks arrive in two stages, where the first stage tasks are known and the second stage task arrivals follow a random distribution. Given the distribution of these second stage task arrivals, simulation and mathematical programming are used within a dynamic programming framework to determine optimal allocation strategies. The special structure of the assignment problem is exploited to recursively update functional approximations representing future rewards using subgradient information. Through several theorems, optimality of the algorithm is proven for a two-stage Dynamic Weapon-Target Assignment Problem.

Modeling Intelligent Decision-Making Command and Control Agents: An Application to Air Defense

The paper is a half-way between the agent technology and the mathematical reasoning to model tactical decision making tasks. These models are applied to air defense (AD) domain for command and control (C2). It also addresses the issues related to evaluation of agents. The agents are designed and implemented using the agent-programming paradigm. The agents are deployed in an air combat simulated environment for performing the tasks of C2 like electronic counter counter measures, threat assessment, and weapon allocation. The simulated AD system runs without any human intervention, and represents state-of-the-art model for C2 autonomy. The use of agents as autonomous decision making entities is particularly useful in view of futuristic network centric warfare.

An optimal dynamic threat evaluation and weapon scheduling technique

Real time scheduling problems demand high level of flexibility and robustness under complex dynamic scenarios. Threat Evaluation (TE) and Weapon Assignment (WA), together TEWA is one such complex dynamic system that has optimal or near optimal utilization of scarce defensive resources, a supreme priority. Several static solutions of TEWA have been proposed. This paper discusses an optimal dynamic multi-air threat evaluation and weapon allocation algorithm using a variant of Stable Marriage Algorithm (SMA). WA uses a new dynamic weapon scheduling algorithm, allowing multiple engagements using shoot-look-shoot strategy, to compute near-optimal solution. For optimality different types of constraints are identified and defined. Testing part of this paper shows feasibility of this approach for a range of scenarios.

Novel Goal-Based Weapon Target Assignment Doctrine

The weapon target assignment problem is aboutfinding the optimal allocation of weapons to threats in a way that minimizes the expected damage inflicted on the defender assets. In 1986LloydandWitsenhousendemonstratedthatweapontargetassignmentwasNP-complete with no exact algorithm. Researchers in this area are trying to provide an exact solution to specialcasesoftheproblemorheuristicsthatattempttosupplyanapproximatesolutionusing a variety of tools and techniques from nonlinear network flows to artificial neural networks and genetic algorithms.A new approach for tackling the weapon target assignment problem is proposed in this paper. Such an approach is a novel goal-based system.The proposed novel approach combines state of the art goal-based optimization approach and the Hungarian method to preserve good performance under different air defense mission configurations. An air defense mission design and analysis package is developed to provide realistic air defense missions data to the algorithm. The proposed algorithm has the best performance when compared with other weapon target assignment doctrines.

Solving Battle Management/Command Control and Communication Problem using Modified BIONET

This paper proposes and implements a neural architecture to solve the weapon allocation problem in the multi-layer defense scenario using modified BIONET neural network architecture. The presynaptic layer of the modified BIONET reduces the dimensionality of the principal state equation by partitioning the state space. The post-synaptic layer of the modified BIONET includes the perceptron Q-learning rule. The cortical layer incorporates L-learning scheme to provide better exploration over action space. Thus, action selection is effectively made with quicker convergence of training. The reward scheme in the reinforcement learning is obtained by calculating the measure of probability of survival. The decision module has been enhanced by incorporating the features corresponding to the battle weapons for effective representation of the environment. Thus, the modified BIONET neural architecture is used to increase the efficiency of assets saved in the simulation and the time complexity is reduced due to the state-space partitioning scheme involved in the neural network. The proposed modified BIONET is implemented in MATLAB and the percentage of assets saved is increased. Also, the training time is drastically reduced. Thus, the modified BIONET resulted in saving more assets with faster convergence of learning.

Hybrid Genetic-simulated Annealing Algorithm for Optimal Weapon Allocation in Multilayer Defence Scenario

Simulated annealing is one of the several heuristic optimisation techniques, that has been studied in the past to determine the most effective mix of weapons and their allocation to enemytargets in a multilayer defence scenario. Simulated annealing is a general stochastic search algorithm. It is usually employed as an optimisation method to find a near-optimal solution forhard combinatorial optimisation problems, but it is very difficult to give the accuracy of the solution found. To find a better solution, aji often used strategy is to run the algorithm byapplying the existing best solution from the population space as the initial starting point. Giving many passes of genetic algorithm can generate the best start-point solution. This paper describes a new hybrid optimisation method, named genetic-simulated annealing, that combines the global crossover operators from genetic algorithm and the local stochastic hill-climbing features from simulated annealing, to arrive at an improved solution with reduced computational time. The basic idea is to use the genetic operators of genetic algorithm to quickly converge the search to a near-global minima/maxima, that will further be refined to a near-optimum solution by simulated anneling using annealing process. The new hybrid algorithm has been applied to optimal weapon allocation in multilayer defence scenario problem to arrive at a better solution than produced by genetic algorithm or simulated annealing alone.

Genetic algorithm for optimal weapon allocation in multilayer defence scenario

Several heuristic optimisation techniques have been studied in the past to determine the most effective mix of weapons and their allocation to enemy targets in a multilayer defence scenario. This paper discusses a genetic algorithm approach to arrive at improved solutions with reduced computational time. The most important aspect of the new approach is the mapping of the nonlinear optimisation problem into a discrete problem. The results demonstrate that if the problem mapping is correct, even a primitive algorithm can yield high quality results to a complex optimisation problem.

Optimal weapons allocation against layered defenses

We solve the problem of optimal allocation of weapons to targets in the presence of layered regional defenses. The general solution technique is an integer program transformable to a minimum-cost network flow. This model assumes the defense has perfect weapons. Results of a small sample scenario are included. Additionally, a representative attrition algorithm is described and the two models combined to form a hybrid algorithm. The hybrid algorithm allows for less-than-perfect weapons while maintaining optimality.

A new solution to optimization-satisfaction problems by a penalty method

This paper is concerned with an optimization-satisfaction problem to determine an optimal solution such that a certain objective function is minimized, subject to satisfaction conditions against uncertainties of any disturbances or opponents' decisions. Such satisfaction conditions require that plural performance criteria are always less than specified values against any disturbances or opponents' decisions. Therefore, this problem is formulated as a minimization problem with the constraints which include max operations with respect to the disturbances or the opponents' decision variables. A new computational method is proposed in which a series of approximate problems transformed by applying a penalty function method to the max operations within the satisfaction conditions are solved by usual nonlinear programming. It is proved that a sequence of approximated solutions converges to a true optimal solution. The proposed algorithm may be useful for systems design under unknown parameters, process control under uncertainties, general approximation theory, and strategic weapons allocation problems.

Optimum design in the presence of parametric uncertainty

A new class of optimal design problems that incorporates environmental uncertainty is formulated and related to worst-case design, minimax objective design, and game theory. A numerical solution technique is developed and applied to a weapon allocation problem, a structural design problem with an infinite family of load conditions, and a vibration isolator design problem with a band of excitation frequencies.

Optimal Weapon Allocation with Overlapping Area Defenses

We present a strategy for finding an optimal allocation of weapons to maximize the expected damage of a given collection of targets. In our scenario the area defenses can overlap, and we allow more than one weapon type. We show how the generalized Lagrange-multiplier method can be used effectively by exploiting certain graph structure when maximizing the Lagrangian.

A Review of the Literature on the Missile-Allocation Problem

This paper reviews the missile-allocation-problem literature. The problem considered is: given an existing weapon force and a set of targets, what is the optimal allocation of weapons to targets? References are organized by type, characterized by submodel, discussed, and annotated. It is proposed that this review methodology by applied to other appropriate areas.

An Optimum Allocation of Different Weapons to a Target Complex

How best to allocate offensive weapons of various types to a group of targets in order to maximize the payoff constitutes a very important problem for optimizing strategies of defense. Solutions to the allocation problem, for the case where one type of weapon is involved, exist in the literature. Essentially these solutions fall into two categories digital solutions which yield integral results, and analytical solutions, based on the Lagrange Multipliers, which treat the number of weapons as a continuous variable and thus produce fractional answers. Whenever the number of weapons is large compared with the number of targets, analytical methods are preferred to digital ones because they achieve a saving in computer time at a negligible loss of accuracy. On the other hand, if the number of weapons is of the same order of magnitude as the number of targets, digital methods are preferable because they yield exact answers, as opposed to analytical solutions that produce rounding errors too large to render the results useful. The object of this paper is to generalize an analytical solution to the case where the attacker has more than one type of weapon available for assignment to an undefended or virtually undefended target complex. Defense strategies are not included. The paper presents a computational example where three types of weapons, each with a different number of attacking vehicles, are allocated to twenty targets. This example also permits a comparison between the fractional results of an analytical solution and the integral values obtained by a computer-oriented algorithm.

A Missile Allocation Problem

The problem of finding the allocation of weapons to a target system that results in the largest payoff is present in almost every war gaming exercise. A technique often employed to find a solution to such a problem is to consider the problem as a continuous allocation process. However, the continuous solution may not furnish the solution to the actual problem, which is a discrete allocation process. This paper presents a solution to a missile allocation problem when the problem is considered in its continuous form. This same problem is investigated in its discrete form and it is shown that the solution in the continuous case is not, in general, a solution for the discrete case. Further, a necessary condition is derived for the existence of a solution for the discrete problem that differs from the solution for the corresponding continuous problem.