Modelling and Optimisation of UHF band EW Based WTA Problem within the Scope of Threat Assessment

The classical weapon target allocation (WTA) problem has been evaluated within the scope of electronic warfare (EW) threat assessment with an electromagnetic effect-based jammer- tactical radio engagement approach. As different from the literature, optimum allocation of non-directional jammers operating at different operating UHF frequencies under constraints to RF emitters is aimed in this study. The values of the targets are modelled using an original threat assessment algorithm developed that takes into account operating frequencies, jamming distance, and weather conditions. The computed jammer-target effect matrix has been solved under different scenarios according to the efficiency and cost constraints. It is seen at the end of the simulations that the allocation results for EW applications largely depend on the effect ratio used. The better results are taken in the case of under 0.5 effect ratio. Finally, jammer-radio allocation problem specified at the suggested model is solved successfully and effectively

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

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

A Concurrent Fuzzy-Neural Network Approach for Decision Support Systems

Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. The past few years have witnessed a growing recognition of Soft Computing technologies that underlie the conception, design and utilization of intelligent systems. Several works have been done where engineers and scientists have applied intelligent techniques and heuristics to obtain optimal decisions from imprecise information. In this paper, we present a concurrent fuzzy-neural network approach combining unsupervised and supervised learning techniques to develop the Tactical Air Combat Decision Support System (TACDSS). Experiment results clearly demonstrate the efficiency of the proposed technique

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