Real-time Computing of Air-to-air Missile Multiple Capture Zones Based on Deep Learning

doi:10.3969/j.issn.1000-1093.2020.12.012

Abstract

Abstract: Modern air combat is becoming more and more complex，and the traditional attack zones can only provide the limits of missile launch，which cannot meet the needs of modern air combat decision-making. For this reason，five types of attack zones, maximun attack zone, 50° attack zone, 90° attack zone, horizontal unescapable attack zone, and minimun attack zone, based on escape angles of enemy aircraft are proposed. The existing solving method of attack zone cannot be used to simultaneously solve the problem of multiple attack zones.A multi-function deep fitting network (MFDFN) is proposed to realize the simultaneous solution of multiple attack zones. Firstly，an improved advance-retreat method is designed，and the sample library of capture zone is obtained through ballistic simulation. According to the characteristics of multi-function fitting network，a training strategy，called “overall pre-training and local fine-tuning”，is presented，by which network is supervised trained.The simulated results show that the MFDFN using the “overall pre-training and local fine-tuning” training strategy reduces the computing time while greatly improving the computing accuracy. The average relative error is about 0.27%，and the average absolute error is about 58.81 meters，which proves that the model is reliable and practical.

Key words: air-to-airmissile, attackzone, deeplearning, computing, advance-retreatmethod, multi-functionfitting

CLC Number:

TJ762.2⁺3

YAN Mengda， YANG Rennong， ZUO Jialiang， HU Dongyuan， YUE Longfei， ZHAO Yu. Real-time Computing of Air-to-air Missile Multiple Capture Zones Based on Deep Learning[J]. Acta Armamentarii, 2020, 41(12): 2466-2477.

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