Evaluating the performance of TEWA systems

Detta är en avhandling från Örebro : Örebro universitet

Sammanfattning: It is in military engagements the task of the air defense to protect valuable assets such as air bases from being destroyed by hostile aircrafts and missiles. In order to fulfill this mission, the defenders are equipped with sensors and firing units. To infer whether a target is hostile and threatening or not is far from a trivial task. This is dealt with in a threat evaluation process, in which the targets are ranked based upon their estimated level of threat posed to the defended assets. Once the degree of threat has been estimated, the problem of weapon allocation comes into the picture. Given that a number of threatening targets have been identified, the defenders need to decide on whether any firing units shall be allocated to the targets, and if so, which firing unit to engage which target. To complicate matters, the outcomes of such engagements are usually stochastic. Moreover, there are often tight time constraints on how fast the threat evaluation and weapon allocation processes need to be executed. There are already today a large number of threat evaluation and weapon allocation (TEWA) systems in use, i.e. decision support systems aiding military decision makers with the threat evaluation and weapon allocation processes. However, despite the critical role of such systems, it is not clear how to evaluate the performance of the systems and their algorithms. Hence, the work in thesis is focused on the development and evaluation of TEWA systems, and the algorithms for threat evaluation and weapon allocation being part of such systems. A number of algorithms for threat evaluation and static weapon allocation are suggested and implemented, and testbeds for facilitating the evaluation of these are developed. Experimental results show that the use of particle swarm optimization is suitable for real-time target-based weapon allocation in situations involving up to approximately ten targets and ten firing units, while it for larger problem sizes gives better results to make use of an enhanced greedy maximum marginal return algorithm, or a genetic algorithm seeded with the solution returned by the greedy algorithm.