A Collaborative Assessment of Anti-UAV Cluster Operational Effectiveness Based on BP Neural Network and FAHP-TOPSIS

With the rapid development of UAV cluster technology, it is difficult for traditional air defense means to effectively deal with the new threats brought by them, and the evaluation of anti-UAV cluster combat effectiveness has become a key area of current research. In this paper, two types of assessment models are constructed to solve this problem: one is the five-dimensional capability automatic scoring model based on BP neural network, which realizes the automation and high efficiency of the assessment process; and the other is the FAHP-TOPSIS comprehensive assessment model integrating the characteristics of the battlefield environment, which dynamically adjusts the weights of the environmental parameters to improve the objectivity and adaptability of the assessment. Both models take the five-dimensional capabilities of reconnaissance and perception, intelligence processing, combat management, attack and defense confrontation, and sustained combat of the anti-UAV cluster system as the assessment dimensions, and combine expert scoring with the dynamic weight allocation mechanism of battlefield scenarios to form a comprehensive assessment system.