Firing Data Calculation Method for Laser Terminal Guidance Projectile Based on Multi-model Network

doi:10.12382/bgxb.2022.1133

Abstract

Abstract:

To deal with the large truncation errors and defects of being unable to give quick and accurate solutions in calculating the firing data of laser terminal guidance projectiles by the firing table, a method based on multi-model network is proposed. Firstly, a six-degree-of-freedom trajectory model of the guided projectile is established, and the influence of the rear sight on the range in the initial conditions is analyzed. Secondly, considering the influence of air temperature, air pressure, wind interference and other factors on the solution, the least square method is used to fit the air temperature and pressure, and the layer weight method is adopted to obtain the accurate ballistic wind as part of the input of the network model. Thirdly, samples are generated in batches through simulations, which are applied to train each model in the deep neural network. Finally, the proposed method is verified by simulations and flight tests. The simulation results show that the proposed method has a higher precision and an error rate of less than 0.2% compared with the traditional firing table.

Key words: laser terminal guidance projectile, firing data solution, multi-model network, meteorological condition, deep neural networks

CLC Number:

TJ765.3

LIU Chang, LEI Hongbo, LIN Shiyao, FAN Shipeng, WANG Jiang. Firing Data Calculation Method for Laser Terminal Guidance Projectile Based on Multi-model Network[J]. Acta Armamentarii, 2023, 44(9): 2745-2755.

Figures/Tables 22

Fig.1 Flight trajectory

Fig.2 Ballistic trajectory

Fig.3 Trends of temperature change

Fig.4 Trends of pressure change

Fig.5 Overall framework of the model

Fig.6 Pressure fitting

Table 1 Sampling range of parameters

参数类型	采样范围	采样间隔
表尺/mil	[450,660]	2
程装/分划	[95,115]	2

Fig.7 Network structure of DNN

Fig.8 Change of loss function

Fig.9 Test set precision

Table 2 Simulation conditions

组数	期望射程/m	气象条件获取日期和时间
1	15300	2020年6月20日9时
2	15800	2021年7月3日14时
3	16300	2022年7月16日17时

Fig.10 Output of firing data solution model

Table 3 Error and precision

期望射程/m	输出射程/m	误差/m	误差率/%
15300	15317	17	0.110
15800	15815	15	0.095
16300	16321	21	0.130

Table 4 Error and precision of desired range

期望射程/m	输出射程/m	误差/m	误差率/%
15300	15315	15	0.098
15800	15822	22	0.140
16300	16324	24	0.150

Table 5 Error and precision of lateral deviation

期望射程/m	实际横偏/m	预测横偏/m	误差/m
15300	26.8	27.5	0.7
15800	26.5	27	0.5
16300	26.4	26.8	0.4

Fig.11 Output of multi-model network

Fig.12 Monte Carlo simulation result

Fig.13 Comparison of simulation results

Table 6 Error comparison

期望射程/m	多模型网络/m	射表解算/m
15300	15315	15368
16300	16324	16435

Table 7 Firing data

表尺/mil	程装/分划	方向修偏/mil	延时/s
555	105	右偏6	46

Table 8 Error comparison

期望射程/m	多模型网络/m	射表解算/m
16000	16019	16096

Fig.14 Flight test results

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