A multiobjective approach towards weapon assignment in a ground-based air defence environment

Lotter, D. P. ; Nieuwoudt, I. ; Van Vuuren, J. H. (2013)

CITATION: Lotter, D. P., Niewoudt, I. & Van Vuuren, J. H. 2013. A multiobjective approach towards weapon assignment in a ground-based air defence environment. Orion, 29(1):31-54, doi:10.5784/29-1-138.

The original publication is available at http://orion.journals.ac.za

Article

A typical ground-based air defence (GBAD) environment comprises defended assets on the ground which require protection from enemy aircraft entering the defended airspace. Protection against these aircraft is afforded by means of pre-deployed ground-based weapon systems that are assigned to engage these enemy aircraft according to some pre-specified criterion or set of criteria. The conditions under which human operators have to propose assignments of weapon systems to engage these aircraft are severely stressful since time is a critical factor and there is no room for error. Some progress has already been made with respect to the design of computerised threat evaluation and weapon assignment (TEWA) decision support systems (DSSs) within the context of a GBAD system. However, the weapon assignment (WA) component within such a TEWA DSS is typically based on a single criterion (objective). The aim in this paper is to model the WA problem as a multiobjective decision problem. A list of relevant factors (related to objectives) is identified by means of feedback received from a WA questionnaire which was completed by a number of military experts. For illustrative purposes, two objectives, namely the cost of assigning weapon systems for engagement and the accumulated survival probabilities of observed threats as a result of these engagements, were isolated from these factors in order to derive a bi-objective WA model. This model is solved in the context of a simulated, but realistic, GBAD environment by means of an existing multiobjective solution technique called the Nondominated Sorting Genetic Algorithm II.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/94099
This item appears in the following collections: