基于改进舰尾流模型和多层耦合分析的机载雷达测量建模

葛泉波; 王远亮; 李宏

doi:10.16383/j.aas.c220815

基于改进舰尾流模型和多层耦合分析的机载雷达测量建模

doi: 10.16383/j.aas.c220815

葛泉波^{1, 2, 3,},
王远亮^4,,
李宏^5,

1.
南京信息工程大学自动化学院南京 210044
2.
江苏省大气环境与装备技术协同创新中心南京 210044
3.
江苏省大数据分析技术重点实验室南京 210044
4.
上海海事大学物流工程学院上海 200135
5.
中国飞行试验研究院西安 710089

基金项目: 国家自然科学基金(62033010, U23B2061), 江苏省“青蓝工程” (R2023Q07)资助

详细信息

作者简介:
葛泉波：南京信息工程大学教授. 主要研究方向为状态估计与信息融合, 自主智能无人系统, 飞行器测试数据分析和电力物联网技术. 本文通信作者. E-mail: 003535@nuist.edu.cn

王远亮：上海海事大学物流工程学院博士研究生. 2020年获天津理工大学电气电子工程学院硕士学位. 主要研究方向为无人舰载机位姿估计, 卡尔曼滤波算法的应用. E-mail: 202040210002@stu.shmtu.edu.cn

李宏：中国飞行试验研究院研究员. 主要研究方向为航空飞行器测试, 光电测量, 试验数据处理, 系统工程设计. E-mail: lihongcfte@163.com

中图分类号: Y
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出版历程
- 收稿日期: 2022-10-16
- 录用日期: 2023-02-23
- 网络出版日期: 2023-08-21
- 刊出日期: 2024-03-29

Airborne Radar Measurement Modeling Based on Improved Carrier Air Wake Model and Multi-layer Coupling Analysis

GE Quan-Bo^{1, 2, 3
,},
WANG Yuan-Liang^4
,,
LI Hong^5
,

1.
School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044
2.
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing 210044
3.
Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing 210044
4.
Logistics Engineering College, Shanghai Maritime University, Shanghai 200135
5.
Chinese Flight Test Establishment, Xi＇an 710089

Funds: Supported by National Natural Science Foundation of China (62033010, U23B2061) and Qing Lan Project of Jiangsu Province (R2023Q07)

More Information

Author Bio:
GE Quan-Bo　Professor at Nanjing University of Information Science and Technology. His research interest covers state estimation and information fusion, autonomous intelligent unmanned system, aircraft test data analysis, and power internet of things technology. Corresponding author of this paper

WANG Yuan-Liang　Ph.D. candidate at the Logistics Engineering College, Shanghai Maritime University. He received his master degree from the College of Electronic Engineering, Tianjin University of Technology in 2020. His research interest covers UCA pose estimation and application of Kalman filter algorithm

LI Hong　Researcher at Chinese Flight Test Establishment. His research interest covers aircraft test, photoelectric measurement, test data processing, and system engineering design

摘要

摘要: 为提高复杂海洋环境中无人舰载机(Unmanned carrier-based aircraft, UCA)自动着舰时导航定位的准确性, 研究舰尾流对机载雷达测量过程的动态影响问题, 建立一种基于多层级耦合性分析的测量影响动态建模分析方法. 首先, 利用直接分解法和前向差分法建立一种基于离散化状态空间的时变舰尾流模型, 以克服传统传递函数方法存在的局限性; 其次, 基于舰尾流各分量均与飞机飞行速度相关的客观事实, 通过在时变系统中考虑舰尾流分量间的相互作用关系来构建一种更符合实际系统特征的分量自耦合舰尾流模型; 紧接着, 采用UCA姿态角变化能够改变坐标转换矩阵的思想, 研究舰尾流与UCA位姿变化间的耦合联系, 提出一种准确性更高的舰尾流对UCA位姿的深度影响模型; 然后, 以航母姿态变化对舰载雷达测量结果的影响模型为基础, 通过考虑本研究场景的内在特性, 建立UCA姿态变化对雷达测量结果的影响模型分析方法; 紧接着, 采用示意图方式获得位移变化对机载雷达测量结果的影响模型; 最后, 针对舰船受海洋大气(风、浪、流)干扰而出现失速这一现象, 建立实际海洋环境中舰尾流对机载雷达测量结果的非线性非高斯影响分析模型. 仿真实验研究验证了上述模型分析方法的有效性和优越性.
- 舰尾流 /
- 机载雷达 /
- 状态空间 /
- 耦合性 /
- 非线性非高斯
Abstract: To improve the accuracy of navigation and positioning for unmanned carrier-based aircraft (UCA) automatic landing in complex marine environments, this study investigated the dynamic effects of carrier air wake on onboard radar measurements and established a modeling and analysis method based on multi-level coupling analysis. Firstly, a time-varying carrier air wake model based on a state-space discretization approach using direct decomposition and forward differences was developed to overcome the limitations of traditional transfer function methods. Secondly, a component self-coupling carrier air wake model was constructed to be more consistent with actual system characteristics by considering the interaction between components, which are all related to the aircraft＇s flight speed. Thirdly, a more accurate depth effect model of carrier air wake on UCA＇s position was proposed by studying the coupling relationship between carrier air wake and UCA＇s attitude changes through the concept of coordinate transformation matrices. Subsequently, an analysis method of the effect of UCA＇s attitude changes on radar measurements was developed based on the impact of aircraft carrier attitude changes on radar measurements. Then, a displacement change effect model on onboard radar measurements was obtained using a diagrammatic approach. Finally, a nonlinear and non-Gaussian effect analysis model of carrier air wake on onboard radar measurements in actual marine environments was established to address aircraft stalling caused by atmospheric disturbances such as wind, waves, and currents. Simulation experiments showed the effectiveness and superiority of the proposed modeling and analysis methods.
- Carrier air wake /
- airborne radar /
- state space /
- coupling /
- nonlinear non-Gaussian

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图 1 研究思路框图

Fig. 1 Block diagram of research ideas

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图 2 自耦合存在性示意图

Fig. 2 Self coupling existence diagram

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图 3 航母姿态角对应无人机姿态角示意图

Fig. 3 Schematic diagram of UCA attitude angle corresponding to aircraft carrier attitude angle

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图 4 不同平台中雷达所处位置坐标示意图

Fig. 4 Schematic diagram of radar position coordinates in different platforms

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图 5 由横滚角和俯仰角导致的方位角影响

Fig. 5 Azimuth angle error caused by roll and pitch angle

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图 6 姿态变化对传感器测量的距离影响示意图

Fig. 6 Schematic diagram of the influence of attitude change on the distance measured by the sensor

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图 7 UCA位置确定示意图和位置变化示意图

Fig. 7 UCA location determination diagram and location change diagram

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图 8 沿Z轴、Y轴和X轴运动的测量影响示意图

Fig. 8 Schematic diagram of measurement effects along Z-axis, Y-axis and X-axis movements

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图 9 大气紊流状态空间模型三个方向风速对比图

Fig. 9 Comparison of wind speeds in three directions of the spatial model of atmospheric turbulence states

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图 10 随机分量状态空间模型三个方向风速对比图

Fig. 10 Comparison plot of wind speeds in three directions of the stochastic component state space model

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图 11 不同舰尾流模型三个方向风速对比图

Fig. 11 Comparison chart of wind speeds in three directions for different carrier air wake models

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图 12 不同舰尾流模型三个方向风速误差对比图

Fig. 12 Comparison of wind speed errors in three directions of different carrier air wake models

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图 13 惯性坐标系三轴方向的位移变化模型

Fig. 13 Displacement variation model in three-axis direction in inertial coordinate system

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图 14 惯性坐标系三轴方向的位移变化误差对比图

Fig. 14 Comparison plot of displacement change error in the triaxial direction of the inertial coordinate system

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图 15 UCA姿态角变化模型

Fig. 15 UCA attitude angle change model

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图 16 UCA姿态角变化误差对比

Fig. 16 Comparison of UCA attitude angle change error

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图 17 位移变化对机载雷达测量结果的影响仿真对比图

Fig. 17 Simulation comparison diagram of the influence of displacement change on the measurement accuracy of airborne radar

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图 18 位移变化对雷达测量结果的影响误差仿真对比图

Fig. 18 Error simulation comparison diagram of influence of displacement change on radar measurement accuracy

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图 19 姿态变化对雷达测量结果的影响仿真对比图

Fig. 19 Simulation comparison diagram of the influence of attitude change on radar measurement accuracy

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图 20 姿态变化对雷达测量结果的影响误差仿真对比图

Fig. 20 Error simulation comparison diagram of influence of attitude change on airborne radar measurement accuracy

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图 21 位姿变化对机载雷达测量结果的影响仿真对比图

Fig. 21 Simulation comparison diagram of the influence of position and attitude changes on the measurement accuracy of airborne radar

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图 22 位姿变化对雷达测量结果的影响误差仿真对比图

Fig. 22 Error simulation comparison diagram of influence of position and attitude change on airborne radar measurement accuracy

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图 23 舰尾流对雷达测量结果影响的非高斯性验证

Fig. 23 Verification of non-Gaussian effect of ship wake on radar measurement accuracy

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图 24 风速误差对比图

Fig. 24 Comparison diagram of wind speed error

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图 25 两种模型对雷达测量结果影响的误差仿真对比图

Fig. 25 Error simulation comparison diagram of the influence of two models on radar measurement results

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表 1 三种模型均方根误差结果

Table 1 Root mean square error results of three models

TCAW ACAW CCAW

$u_g\text{-}{\rm{RMSE}} $ 0.4791 0.3036 0.2979
$l_g\text{-}{\rm{RMSE}} $ 0.2481 0.2025 0.1951
$w_g\text{-}{\rm{RMSE}} $ 0.7180 0.3960 0.3918

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表 2 两种位移变化模型均方根误差结果

Table 2 Root mean square error results of two displacement variation models

DTCAW DCCAW

dx-RMSE 0.0442 0.0240
dy-RMSE 0.0661 0.0410
dz-RMSE 0.0393 0.0218

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表 3 两种姿态变化模型均方根误差结果

Table 3 Root mean square error results of two attitude change models

ATCAW ACCAW

${\rm{d}}\theta$-RMSE 0.0144 0.0079
${\rm{d}}\psi$-RMSE 0.0050 0.0040
${\rm{d}}\phi$-RMSE 0.0720 0.0397

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表 4 两种位移变化干扰下测量影响模型均方根误差结果

Table 4 Root mean square error results of measurement influence model under two kinds of displacement changes

RDTCAW RDCCAW

dR-RMSE 0.0664 0.0356
dE-RMSE 5.5629 × 10⁻⁴ 2.6464 × 10⁻⁴
dA-RMSE 8.2381 × 10⁻⁴ 2.6558 × 10⁻⁴

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表 5 两种姿态变化干扰下测量影响模型均方根误差结果

Table 5 Root mean square error results of measurement influence model under two kinds of attitude changes

RATCAW RACCAW

${\rm{d}}R$-RMSE 0.0213 0.0130
${\rm{d}}E$-RMSE 0.0436 0.0276
${\rm{d}}A$-RMSE 0.4117 0.2321

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表 6 两种位姿变化干扰下测量影响模型均方根误差结果

Table 6 Root mean square error results of measurement influence model under the interference of two kinds of posture changes

RPTCAW RPCCAW

${\rm{d}}R$-RMSE 0.0745 0.0347
${\rm{d}}E$-RMSE 0.0436 0.0277
${\rm{d}}A$-RMSE 0.4117 0.2321

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表 7 两种风速模型均方根误差结果

Table 7 Root mean square error results of two wind speed models

CCAW SCCAW

$u_g\text{-}{\rm{RMSE}} $ 0.2560 0.2260
$l_g\text{-}{\rm{RMSE}} $ 0.2261 0.1905
$w_g\text{-}{\rm{RMSE}} $ 0.3316 0.3143

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表 8 两种测量影响模型均方根误差结果

Table 8 Root mean square error results of two measurement impact models

RPCCAW WRPCCAW

${\rm{d}}R$-RMSE 0.0452 0.0379
${\rm{d}}E$-RMSE 0.0547 0.0504
${\rm{d}}A$-RMSE 0.6778 0.6772

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基于改进舰尾流模型和多层耦合分析的机载雷达测量建模

doi: 10.16383/j.aas.c220815

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Airborne Radar Measurement Modeling Based on Improved Carrier Air Wake Model and Multi-layer Coupling Analysis

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	TCAW	ACAW	CCAW
$u_g\text{-}{\rm{RMSE}} $	0.4791	0.3036	0.2979
$l_g\text{-}{\rm{RMSE}} $	0.2481	0.2025	0.1951
$w_g\text{-}{\rm{RMSE}} $	0.7180	0.3960	0.3918

	DTCAW	DCCAW
dx-RMSE	0.0442	0.0240
dy-RMSE	0.0661	0.0410
dz-RMSE	0.0393	0.0218

	ATCAW	ACCAW
${\rm{d}}\theta$-RMSE	0.0144	0.0079
${\rm{d}}\psi$-RMSE	0.0050	0.0040
${\rm{d}}\phi$-RMSE	0.0720	0.0397

	RDTCAW	RDCCAW
dR-RMSE	0.0664	0.0356
dE-RMSE	5.5629 × 10⁻⁴	2.6464 × 10⁻⁴
dA-RMSE	8.2381 × 10⁻⁴	2.6558 × 10⁻⁴

	RATCAW	RACCAW
${\rm{d}}R$-RMSE	0.0213	0.0130
${\rm{d}}E$-RMSE	0.0436	0.0276
${\rm{d}}A$-RMSE	0.4117	0.2321

	RPTCAW	RPCCAW
${\rm{d}}R$-RMSE	0.0745	0.0347
${\rm{d}}E$-RMSE	0.0436	0.0277
${\rm{d}}A$-RMSE	0.4117	0.2321

	CCAW	SCCAW
$u_g\text{-}{\rm{RMSE}} $	0.2560	0.2260
$l_g\text{-}{\rm{RMSE}} $	0.2261	0.1905
$w_g\text{-}{\rm{RMSE}} $	0.3316	0.3143

	RPCCAW	WRPCCAW
${\rm{d}}R$-RMSE	0.0452	0.0379
${\rm{d}}E$-RMSE	0.0547	0.0504
${\rm{d}}A$-RMSE	0.6778	0.6772

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基于改进舰尾流模型和多层耦合分析的机载雷达测量建模

doi: 10.16383/j.aas.c220815

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Airborne Radar Measurement Modeling Based on Improved Carrier Air Wake Model and Multi-layer Coupling Analysis

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