Comparative Performance Analysis of Two Multi-Aerial Threat Evaluation Algorithms
DOI:
https://doi.org/10.55972/spectrum.v26i1.418Keywords:
Threat evaluation, Air defense operations, Real-time, Machine learning, Artificial neural networkAbstract
In modern aerial defense operation, the evaluation of potential threats is of paramount importance for effective response strategies, particularly when such assessment is performed in real-time. This study presents a comparative analysis of an algorithm developed by the authors, and referred to as DM, and a Markov chain-based approach (MC) in terms of prediction accuracy, execution time, and processing capacity. Notably, DM consistently achieved higher accuracy until simulation time 1350, despite both methods utilizing the same Artificial Neural Network architecture. Additionally, DM exhibited superior execution time and processing capacity, handling a maximum of 89 threats within a one-second timeframe, while MC processed 10 threats. Based on this, it can be asserted that DM meets the requirements for real-time threat evaluation. The results can be attributed to DM's simplified methodology, enabling more accurate and distinct predictions.}
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