基于多模型网络的激光末制导炮弹诸元解算方法

doi:10.12382/bgxb.2022.1133

摘要/Abstract

摘要：

针对射表解算激光末制导炮弹射击诸元存在较大的截断误差及无法快速精确解算的缺陷,提出一种基于多模型网络的射击诸元快速精确求解方法。建立制导炮弹6自由度弹道模型,分析初始条件中表尺、程装等对射程的影响。考虑气温、气压和风干扰等因素对求解结果的影响,应用最小二乘法对气温、气压进行拟合,采取层权的方式求取精确弹道风,作为网络模型的部分输入。利用数学仿真手段批量化生成样本,对深度神经网络中的每个模型进行离线训练。对新方法进行数学仿真和飞行试验验证。仿真和试验结果表明,相对于传统的射表,新方法求解精度高,误差率小于0.2%。

关键词: 激光末制导炮弹, 诸元解算, 多模型网络, 气象条件, 深度神经网络

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

中图分类号:

TJ765.3

刘畅, 雷红波, 林时尧, 范世鹏, 王江. 基于多模型网络的激光末制导炮弹诸元解算方法[J]. 兵工学报, 2023, 44(9): 2745-2755.

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.

图/表 22

图1 飞行轨迹图

Fig.1 Flight trajectory

图2 弹道轨迹

Fig.2 Ballistic trajectory

图3 气温变化趋势

Fig.3 Trends of temperature change

图4 气压变化趋势

Fig.4 Trends of pressure change

图5 模型整体框架

Fig.5 Overall framework of the model

图6 气压拟合

Fig.6 Pressure fitting

表1 参数的采样范围

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

图7 DNN网络结构

Fig.7 Network structure of DNN

图8 损失函数变化

Fig.8 Change of loss function

图9 测试集精度

Fig.9 Test set precision

表2 仿真条件

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

图10 诸元解算模型输出结果

Fig.10 Output of firing data solution model

表3 误差与精度

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

表4 期望射程的误差与精度

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

表5 横偏的误差与精度

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

图11 多模型网络输出结果

Fig.11 Output of multi-model network

图12 蒙特卡洛仿真实验结果

Fig.12 Monte Carlo simulation result

图13 仿真结果对比

Fig.13 Comparison of simulation results

表6 误差对比

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

表7 实际射击诸元

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

表8 误差对比

Table 8 Error comparison

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

图14 飞行试验效果

Fig.14 Flight test results

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