Advanced Input Generating Algorithm for Effect-Based Weapon-Target Pairing Optimization

• Published in: IEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 42; no. 1; pp. 276 - 280
• Main Author: Bogdanowicz, Z. R.
• Format: Journal Article
• Language: English
• Published: IEEE 01.01.2012
• Subjects: Accuracy; Additives; Collaborative engagement; Cybernetics; effect-based weapon assignment; Genetic algorithms; Human; Humans; JMEM; Manuals; Optimization; scalable lethality; Scanning; Tables; Tables (data); weapon-target pairing; Weapons
• ISSN: 1083-4427; 1558-2426
• DOI: 10.1109/TSMCA.2011.2159591

Effect-based weapon-target pairing assigns weapons to targets for the given desired effects on such targets. The most obvious and natural effects on targets are represented by the percentages of damage of these targets. In this paper, we focus on the generation of input for effect-based weapon-target pairing optimization. One way to generate such input is based on the Joint Munition Effectiveness Manual (JMEM). JMEM allows the evaluation of the weapons. It is a database that contains many tables, and each table contains many different data fields. Because of the sheer size of JMEM, the optimization of weapon-target pairing based on JMEM is currently focused mainly on one target at a time. In other words, the optimization of weapon-target pairing for many targets and weapons is not directly supported by JMEM, although all the necessary data is there. In this paper, we derive an input based on the given JMEM and desired effect(s), which should be useful in the follow-on effect-based weapon-target pairing optimization that is not limited to a single weapon or target. In particular, effect-based weapon-target pairing will rely on the scanning of the attack guidance table that we derive from JMEM to determine a preferred set of weapon combinations for engaging a given set of targets.