01888nas a2200253   4500000000100000000000100001008004100002260001200043653002100055653001200076653001100088653002600099653002700125100001700152700002800169700002000197700001600217245009500233856008100328300001200409490000600421520119300427022001401620       2022                            d  c06/202210aMachine Learning10aMissile10aDecoys10aMultilayer Perceptron10aSupport Vector Machine1 aRamón Touza1 aJavier Martínez Torres1 aMaría Álvarez1 aJavier Roca00aObtaining Anti-Missile Decoy Launch Solution from a Ship Using Machine Learning Techniques  uhttps://www.ijimai.org/journal/sites/default/files/2022-05/ijimai_7_4_14.pdf  a163-1700 v73 aOne of the most dangerous situations a warship may face is a missile attack launched from other ships, aircrafts, submarines or land. In addition, given the current scenario, it is not ruled out that a terrorist group may acquire missiles and use them against ships operating close to the coast, which increases their vulnerabilitydue to the limited reaction time. One of the means the ship has for its defense are decoys, designed to deceive the enemy missile. However, for their use to be effective it is necessary to obtain, in a quick way, a valid launching solution. The purpose of this article is to design a methodology to solve the problem of decoy launching and to provide the ship immediately with the necessary data to make the firing decision. To solve the problem machine learning models (neural networks and support vector machines) and a set of training data obtained in simulations will be used. The performance measures obtained with the implementation of multilayer perceptron models allow the replacement of the current procedures based on tables and launching rules with machine learning algorithms that are more flexible and adaptable to a larger number of scenarios.  a1989-1660