基于改進(jìn)艦尾流模型和多層耦合分析的機載雷達測量建模

葛泉波; 王遠亮; 李宏

doi:10.16383/j.aas.c220815

基于改進(jìn)艦尾流模型和多層耦合分析的機載雷達測量建模

doi: 10.16383/j.aas.c220815

葛泉波^{1, 2, 3,},
王遠亮^4,,
李宏^5,

1.
南京信息工程大學(xué)自動(dòng)化學(xué)院南京 210044
2.
江蘇省大氣環(huán)境與裝備技術(shù)協(xié)同創(chuàng )新中心南京 210044
3.
江蘇省大數據分析技術(shù)重點(diǎn)實(shí)驗室南京 210044
4.
上海海事大學(xué)物流工程學(xué)院上海 200135
5.
中國飛行試驗研究院西安 710089

基金項目: 國家自然科學(xué)基金(62033010, U23B2061), 江蘇省“青藍工程” (R2023Q07)資助

詳細信息

作者簡(jiǎn)介:
葛泉波：南京信息工程大學(xué)教授. 主要研究方向為狀態(tài)估計與信息融合, 自主智能無(wú)人系統, 飛行器測試數據分析和電力物聯(lián)網(wǎng)技術(shù). 本文通信作者. E-mail: 003535@nuist.edu.cn

王遠亮：上海海事大學(xué)物流工程學(xué)院博士研究生. 2020年獲天津理工大學(xué)電氣電子工程學(xué)院碩士學(xué)位. 主要研究方向為無(wú)人艦載機位姿估計, 卡爾曼濾波算法的應用. E-mail: 202040210002@stu.shmtu.edu.cn

李宏：中國飛行試驗研究院研究員. 主要研究方向為航空飛行器測試, 光電測量, 試驗數據處理, 系統工程設計. E-mail: lihongcfte@163.com

中圖分類(lèi)號: Y
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- 文章訪(fǎng)問(wèn)數: 492
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- 被引次數: 0
出版歷程
- 收稿日期: 2022-10-16
- 錄用日期: 2023-02-23
- 網(wǎng)絡(luò )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: 為提高復雜海洋環(huán)境中無(wú)人艦載機(Unmanned carrier-based aircraft, UCA)自動(dòng)著(zhù)艦時(shí)導航定位的準確性, 研究艦尾流對機載雷達測量過(guò)程的動(dòng)態(tài)影響問(wèn)題, 建立一種基于多層級耦合性分析的測量影響動(dòng)態(tài)建模分析方法. 首先, 利用直接分解法和前向差分法建立一種基于離散化狀態(tài)空間的時(shí)變艦尾流模型, 以克服傳統傳遞函數方法存在的局限性; 其次, 基于艦尾流各分量均與飛機飛行速度相關(guān)的客觀(guān)事實(shí), 通過(guò)在時(shí)變系統中考慮艦尾流分量間的相互作用關(guān)系來(lái)構建一種更符合實(shí)際系統特征的分量自耦合艦尾流模型; 緊接著(zhù), 采用UCA姿態(tài)角變化能夠改變坐標轉換矩陣的思想, 研究艦尾流與UCA位姿變化間的耦合聯(lián)系, 提出一種準確性更高的艦尾流對UCA位姿的深度影響模型; 然后, 以航母姿態(tài)變化對艦載雷達測量結果的影響模型為基礎, 通過(guò)考慮本研究場(chǎng)景的內在特性, 建立UCA姿態(tài)變化對雷達測量結果的影響模型分析方法; 緊接著(zhù), 采用示意圖方式獲得位移變化對機載雷達測量結果的影響模型; 最后, 針對艦船受海洋大氣(風(fēng)、浪、流)干擾而出現失速這一現象, 建立實(shí)際海洋環(huán)境中艦尾流對機載雷達測量結果的非線(xiàn)性非高斯影響分析模型. 仿真實(shí)驗研究驗證了上述模型分析方法的有效性和優(yōu)越性.
- 艦尾流 /
- 機載雷達 /
- 狀態(tài)空間 /
- 耦合性 /
- 非線(xiàn)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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圖 1 研究思路框圖

Fig. 1 Block diagram of research ideas

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圖 2 自耦合存在性示意圖

Fig. 2 Self coupling existence diagram

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圖 3 航母姿態(tài)角對應無(wú)人機姿態(tài)角示意圖

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

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圖 4 不同平臺中雷達所處位置坐標示意圖

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

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圖 5 由橫滾角和俯仰角導致的方位角影響

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

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圖 6 姿態(tài)變化對傳感器測量的距離影響示意圖

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

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圖 7 UCA位置確定示意圖和位置變化示意圖

Fig. 7 UCA location determination diagram and location change diagram

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圖 8 沿Z軸、Y軸和X軸運動(dòng)的測量影響示意圖

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

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圖 9 大氣紊流狀態(tài)空間模型三個(gè)方向風(fēng)速對比圖

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

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圖 10 隨機分量狀態(tài)空間模型三個(gè)方向風(fēng)速對比圖

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

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圖 11 不同艦尾流模型三個(gè)方向風(fēng)速對比圖

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

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圖 12 不同艦尾流模型三個(gè)方向風(fēng)速誤差對比圖

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

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圖 13 慣性坐標系三軸方向的位移變化模型

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

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圖 14 慣性坐標系三軸方向的位移變化誤差對比圖

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

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圖 15 UCA姿態(tài)角變化模型

Fig. 15 UCA attitude angle change model

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圖 16 UCA姿態(tài)角變化誤差對比

Fig. 16 Comparison of UCA attitude angle change error

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圖 17 位移變化對機載雷達測量結果的影響仿真對比圖

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

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圖 18 位移變化對雷達測量結果的影響誤差仿真對比圖

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

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圖 19 姿態(tài)變化對雷達測量結果的影響仿真對比圖

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

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圖 20 姿態(tài)變化對雷達測量結果的影響誤差仿真對比圖

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

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圖 21 位姿變化對機載雷達測量結果的影響仿真對比圖

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

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圖 22 位姿變化對雷達測量結果的影響誤差仿真對比圖

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

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圖 23 艦尾流對雷達測量結果影響的非高斯性驗證

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

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圖 24 風(fēng)速誤差對比圖

Fig. 24 Comparison diagram of wind speed error

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圖 25 兩種模型對雷達測量結果影響的誤差仿真對比圖

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

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表 1 三種模型均方根誤差結果

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 兩種位移變化模型均方根誤差結果

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 兩種姿態(tài)變化模型均方根誤差結果

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

ATCAW ACCAW

${\rmrf50c1hsl6}\theta$-RMSE 0.0144 0.0079
${\rmrf50c1hsl6}\psi$-RMSE 0.0050 0.0040
${\rmrf50c1hsl6}\phi$-RMSE 0.0720 0.0397

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表 4 兩種位移變化干擾下測量影響模型均方根誤差結果

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 兩種姿態(tài)變化干擾下測量影響模型均方根誤差結果

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

RATCAW RACCAW

${\rmrf50c1hsl6}R$-RMSE 0.0213 0.0130
${\rmrf50c1hsl6}E$-RMSE 0.0436 0.0276
${\rmrf50c1hsl6}A$-RMSE 0.4117 0.2321

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表 6 兩種位姿變化干擾下測量影響模型均方根誤差結果

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

RPTCAW RPCCAW

${\rmrf50c1hsl6}R$-RMSE 0.0745 0.0347
${\rmrf50c1hsl6}E$-RMSE 0.0436 0.0277
${\rmrf50c1hsl6}A$-RMSE 0.4117 0.2321

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表 7 兩種風(fēng)速模型均方根誤差結果

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 兩種測量影響模型均方根誤差結果

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

RPCCAW WRPCCAW

${\rmrf50c1hsl6}R$-RMSE 0.0452 0.0379
${\rmrf50c1hsl6}E$-RMSE 0.0547 0.0504
${\rmrf50c1hsl6}A$-RMSE 0.6778 0.6772

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參考文獻(43)

[1]	Ahmed R F, Golam Kibria B M. Robust ground moving target detection for airborne radar using a novel feature-based machine learning approach. Journal of the Franklin Institute, 2022, 359(9): 4449-4467 doi: 10.1016/j.jfranklin.2022.04.031
[2]	Chen H M, Lu Y B, Liu J, Yi X L, Sun H W, Mu H Q, et al. Efficient knowledge-aided target relocation algorithm for airborne radar. The Journal of Engineering, 2019, 2019(21): 7589-7592 doi: 10.1049/joe.2019.0642
[3]	Kang X J, Li J, Fan X T, Wan W H. Real-time RGB-D simultaneous localization and mapping guided by terrestrial LiDAR point cloud for indoor 3-D reconstruction and camera pose estimation. Applied Sciences, 2019, 9(16): Article No. 3264 doi: 10.3390/app9163264
[4]	Chen L L, Lv Z Y, Shen X Y, Wu Y H, Sun X M. Adaptive attitude control for a coaxial tilt-rotor UAV via immersion and invariance methodology. IEEE/CAA Journal of Automatica Sinica, 2022, 9(9): 1710-1713 doi: 10.1109/JAS.2022.105827
[5]	王峰, 黃子路, 韓孟臣, 邢立寧, 王凌. 基于KnCMPSO算法的異構無(wú)人機協(xié)同多任務(wù)分配. 自動(dòng)化學(xué)報, 2023, 49(2): 399-414 Wang Feng, Huang Zi-Lu, Han Meng-Chen, Xing Li-Ning, Wang Ling. A knee point based coevolution multi-objective particle swarm optimization algorithm for heterogeneous UAV cooperative multi-task allocation. Acta Automatica Sinica, 2023, 449(2): 399-414
[6]	楊智博. 艦載機自動(dòng)著(zhù)艦系統縱向控制策略研究 [博士學(xué)位論文], 哈爾濱工程大學(xué), 中國, 2020. Yang Zhi-Bo. Research on Longitudinal Control Strategy of the Automatic Carrier Landing System [Ph.D. dissertation], Harbin Engineering University, China, 2020.
[7]	Li X, Duan H B, Tian Y L, Wang F Y. Exploring image generation for UAV change detection. IEEE/CAA Journal of Automatica Sinica, 2022, 9(6): 1061-1072 doi: 10.1109/JAS.2022.105629
[8]	范云生, 陳欣宇, 趙永生, 宋保健. 基于擴張狀態(tài)觀(guān)測器的四旋翼吊掛飛行系統非線(xiàn)性控制. 自動(dòng)化學(xué)報, 2023, 49(8): 1758-1770 Fan Yun-Sheng, Chen Xin-Yu, Zhao Yong-Sheng, Song Bao-Jian. Nonlinear control of quadrotor suspension system based on extended state observer. Acta Automatica Sinica, 2023, 49(8): 1758-1770
[9]	王鑫. 飛翼布局無(wú)人機自主著(zhù)艦控制關(guān)鍵技術(shù)研究 [博士學(xué)位論文], 南京航空航天大學(xué), 中國, 2017. Wang Xin. Technology of Automatic Carrier Landing for Flying-Wing Unmanned Aerial Vehichle [Ph.D. dissertation], Nanjing University of Aeronautics and Astronautics, China, 2017.
[10]	Hao R Z, Huang J. On construction method of shipborne and airborne radar intelligence and related equipment knowledge graph. Journal of Physics: Conference Series, 2017, 887: Article No. 012042
[11]	譚文淵, 曹義華. 基于嵌套網(wǎng)格的艦載直升機流場(chǎng)仿真及風(fēng)限圖計算. 航空動(dòng)力學(xué)報, 2020, 35(10): 2166-2175 Tan Wen-Yuan, Cao Yi-Hua. Simulation of flow field and calculation of safe operating envelope of shipborne helicopter based on chimera grid. Journal of Aerospace Power, 2020, 35(10): 2166-2175
[12]	Department of Defense Handbook. Flying Qualities of Piloted Aircraft, MIL-HDBK-1797, 1997.
[13]	趙所, 李震, 侯中喜, 張大為. 艦尾流場(chǎng)擾動(dòng)影響分析及抑制技術(shù)研究. 華中科技大學(xué)學(xué)報(自然科學(xué)版), 2021, 49(6): 86-91 Zhao Suo, Li Zhen, Hou Zhong-Xi, Zhang Da-Wei. Study on influence analysis and rejection technology of carrier air wake. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2021, 49(6): 86-91
[14]	陶楊, 韓維. 基于改進(jìn)多目標遺傳算法的艦尾紊流模擬方法. 北京航空航天大學(xué)學(xué)報, 2015, 41(3): 443-448 Tao Yang, Han Wei. Carrier airwake simulation methods based on improved multi-objective genetic algorithm. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(3): 443-448
[15]	羅飛, 張軍紅, 王博, 唐瑞琳, 唐煒. 基于直接升力與動(dòng)態(tài)逆的艦尾流抑制方法. 航空學(xué)報, 2021, 42(12): 187-202 Luo Fei, Zhang Jun-Hong, Wang Bo, Tang Rui-Lin, Tang Wei. Air wake suppression method based on direct lift and nonlinear dynamic inversion control. Acta Aeronautica et Astronautica Sinica, 2021, 42(12): 187-202
[16]	周俊. 艦載機著(zhù)艦飛/推綜合控制技術(shù)研究 [碩士學(xué)位論文], 南京航空航天大學(xué), 中國, 2018. Zhou Jun. Research on Carrier Aircraft Landing Based on Integrated Filght/Propulsion Control [Master thesis], Nanjing University of Aeronautics and Astronautics, China, 2018.
[17]	賁亮亮. 艦載機著(zhù)艦環(huán)境擾動(dòng)影響及其響應分析 [碩士學(xué)位論文], 南京航空航天大學(xué), 中國, 2013. Ben Liang-Liang. The Environmental Disturbance Influence and Response Analysis on Approaching of Shipboard Aircraft [Master thesis], Nanjing University of Aeronautics and Astronautics, China, 2013.
[18]	周志恒, 趙建軍, 桑德一, 楊利斌. 艦艇姿態(tài)對作戰系統動(dòng)態(tài)對準精度的影響分析. 兵工自動(dòng)化, 2016, 35(5): 51-55, 71 Zhou Zhi-Heng, Zhao Jian-Jun, Sang De-Yi, Yang Li-Bin. Analysis of effects on alignment in dynamic state precision of warship combat system caused by its attitude. Ordnance Industry Automation, 2016, 35(5): 51-55, 71
[19]	桑德一, 趙建軍, 楊利斌. 航母運動(dòng)對著(zhù)艦引導雷達精度的影響. 中國艦船研究, 2014, 9(6): 8-13 doi: 10.3969/j.issn.1673-3185.2014.06.002 Sang De-Yi, Zhao Jian-Jun, Yang Li-Bin. The impact on landing guidance radar precision caused by the movement of aircraft carriers. Chinese Journal of Ship Research, 2014, 9(6): 8-13 doi: 10.3969/j.issn.1673-3185.2014.06.002
[20]	王可, 徐明亮, 李亞飛, 姜曉恒, 魯愛(ài)國, 李鑒. 一種面向航空母艦甲板運動(dòng)狀態(tài)預估的魯棒學(xué)習模型. 自動(dòng)化學(xué)報, DOI: 10.16383/j.aas.c210664 Wang Ke, Xu Ming-Liang, Li Ya-Fei, Jiang Xiao-Heng, Lu Ai-Guo, Li Jian. A robust learning model for deck motion prediction of aircraft carrier. Acta Automatica Sinica, DOI: 10.16383/j.aas.c210664
[21]	Wang Y L, Li H, Ge Q B. A novel modeling analysis of carrier air wake based on component coupling correlation. In: Proceedings of the IEEE International Conference on Unmanned Systems (ICUS). Beijing, China: IEEE, 2021. 970?975
[22]	肖業(yè)倫, 金長(cháng)江. 大氣擾動(dòng)中的飛行原理. 北京: 國防工業(yè)出版社, 1993. Xiao Ye-Lun, Jin Chang-Jiang. Flight Principle in Atmospheric Disturbance. Beijing: National Defense Industry Press, 1993.
[23]	Jiao X, Jiang J, Wang X H, Zhen Z Y. Research on effects of sea states on air wake. In: Proceedings of the 6th IEEE Conference on Industrial Electronics and Applications. Beijing, China: IEEE, 2011. 758?762
[24]	李新飛. 艦載機起降關(guān)鍵技術(shù)仿真研究 [博士學(xué)位論文], 哈爾濱工程大學(xué), 中國, 2012. Li Xin-Fei. Simulation of Key Technology of Lanuch and Land for Carrier-Based Aircraft [Ph.D. dissertation], Harbin Engineering University, China, 2012.
[25]	Cui K K, Han W, Liu Y J, Wang X W, Su X C, Liu J. Model predictive control for automatic carrier landing with time delay. International Journal of Aerospace Engineering, 2021, 2021: Article No. 8613498
[26]	Zhu Q D, Yang Z B. Design of air-wake rejection control for longitudinal automatic carrier landing cyber-physical system. Computers & Electrical Engineering, 2020, 84: Article No. 106637
[27]	牟杭. 多輸入多輸出振動(dòng)系統狀態(tài)空間建模方法研究 [碩士學(xué)位論文], 南京航空航天大學(xué), 中國, 2018. Mou Hang. Research on State Space Modeling Method for Multi-Input and Multi-Output Vibration System [Master thesis], Nanjing University of Aeronautics and Astronautics, China, 2018.
[28]	Nagrath J, Goral M. Control System Engineering. Hoboken: Wiley Eastern Limited, 2016.
[29]	謝莉, 楊慧中, 丁鋒. 非均勻采樣數據系統的新型模型描述方法. 自動(dòng)化學(xué)報, 2017, 43(5): 806-813 Xie Li, Yang Hui-Zhong, Ding Feng. Novel input-output representation of non-uniformly sampled-data systems. Acta Automatica Sinica, 2017, 43(5): 806-813
[30]	劉文定. 自動(dòng)控制原理. 第4版. 北京: 電子工業(yè)出版社, 2018. Liu Wen-Ding. Automatic Control Principle (4th edition). Beijing: Electronic Industry Press, 2018.
[31]	胡壽松. 自動(dòng)控制原理. 第7版. 北京: 科學(xué)出版社, 2019. Hu Shou-Song. Automatic Control Principle (Seventh edition). Beijing: Science Press, 2019.
[32]	劉豹, 唐萬(wàn)生. 現代控制理論. 北京: 機械工業(yè)出版社, 2017. Liu Bao, Tang Wan-Sheng. Modern Control Theory. Beijing: China Machine Press, 2017.
[33]	段廣仁. 高階系統方法-II. 能控性與全驅性. 自動(dòng)化學(xué)報, 2020, 46(8): 1571-1581 Duan Guang-Ren. High-order system approaches: Ⅱ. Controllability and full-actuation. Acta automatica Sinica, 2020, 46(8): 1571-1581
[34]	Li W, Zhao S X, Liu K, Lu H C, Fan T H. Dynamic response calculation algorithm for floating offshore wind turbines based on a state-space method of transfer function. Frontiers in Energy Research, 2022, 10: 909416 doi: 10.3389/fenrg.2022.909416
[35]	田玉平. 自動(dòng)控制原理. 北京: 科學(xué)出版社, 2018. Tian Yu-Ping. Automatic Control Principle. Beijing: Science Press, 2018.
[36]	Bartecki K. Approximate state-space and transfer function models for 2@2 linear hyperbolic systems with collocated boundary inputs. International Journal of Applied Mathematics and Computer Science, 2020, 30(3): 475-491
[37]	李繁飆, 楊皓月, 王鴻鑫, 陽(yáng)春華, 廖力清. 基于干擾估計的非對稱(chēng)運動(dòng)下飛機剎車(chē)系統模型預測控制. 自動(dòng)化學(xué)報, 2022, 48(7): 1690-1703 Li Fan-Biao, Yang Hao-Yue, Wang Hong-Xin, Yang Chun-Hua, Liao Li-Qing. Model predictive control of aircraft braking system under asymmetric motion based on disturbance estimation. Acta Automatica Sinica, 2022, 48(7): 1690-1703
[38]	李繁飆, 黃培銘, 陽(yáng)春華, 廖力清, 桂衛華. 基于非線(xiàn)性干擾觀(guān)測器的飛機全電剎車(chē)系統滑?？刂圃O計. 自動(dòng)化學(xué)報, 2021, 47(11): 2557-2569 Li Fan-Biao, Huang Pei-Ming, Yang Chun-Hua, Liao Li-Qing, Gui Wei-Hua. Sliding mode control design of aircraft electric brake system based on nonlinear disturbance observer. Acta Automatica Sinica, 2021, 47(11): 2557-2569
[39]	孫榮恒. 應用數理統計. 第3版. 北京: 科學(xué)出版社, 2014. Sun Rong-Heng. Applied Mathematical Statistics (3rd edition). Beijing: Science Press, 2014.
[40]	李永泉, 郭雨, 張陽(yáng), 張立杰. 基于牛頓歐拉法的一種空間被動(dòng)過(guò)約束并聯(lián)機構動(dòng)力學(xué)建模方法. 機械工程學(xué)報, 2020, 56(11): 48-57 doi: 10.3901/JME.2020.11.048 Li Yong-Quan, Guo Yu, Zhang Yang, Zhang Li-Jie. Dynamic modeling method of spatial passive over-constrained parallel mechanism based on newton Euler method. Journal of Mechanical Engineering, 2020, 56(11): 48-57 doi: 10.3901/JME.2020.11.048
[41]	謝新連, 王余寬, 何傲, 潘偉, 許小衛. 考慮風(fēng)浪流影響的船舶路徑規劃及算法. 重慶交通大學(xué)學(xué)報(自然科學(xué)版), 2022, 41(7): 1-8 Xie Xin-Lian, Wang Yu-Kuan, He Ao, Pan Wei, Xu Xiao-Wei. Ship path planning and algorithm considering the effect of wind, wave and current. Journal of Chongqing Jiaotong University (Natural Science), 2022, 41(7): 1-8
[42]	魏照坤. 風(fēng)浪影響下的集裝箱船舶航速優(yōu)化 [博士學(xué)位論文], 大連海事大學(xué), 中國, 2018. Wei Zhao-Kun. The Containerships Sailing Speed Optimization Considering Wind and Waves [Ph.D. dissertation], Dalian Maritime University, China, 2018.
[43]	國強, 李文韜. 基于正則化約束總體最小二乘的TDOA/FDOA無(wú)源定位方法. 哈爾濱工業(yè)大學(xué)學(xué)報, 2022, 54(5): 81-87 Guo Qiang, Li Wen-Tao. Passive TDOA/FDOA location based on regularized constrained total least squares. Journal of Harbin Institute of Technology, 2022, 54(5): 81-87

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基于改進(jìn)艦尾流模型和多層耦合分析的機載雷達測量建模

doi: 10.16383/j.aas.c220815

計量

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

計量

目錄

	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
${\rmrf50c1hsl6}\theta$-RMSE	0.0144	0.0079
${\rmrf50c1hsl6}\psi$-RMSE	0.0050	0.0040
${\rmrf50c1hsl6}\phi$-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
${\rmrf50c1hsl6}R$-RMSE	0.0213	0.0130
${\rmrf50c1hsl6}E$-RMSE	0.0436	0.0276
${\rmrf50c1hsl6}A$-RMSE	0.4117	0.2321

	RPTCAW	RPCCAW
${\rmrf50c1hsl6}R$-RMSE	0.0745	0.0347
${\rmrf50c1hsl6}E$-RMSE	0.0436	0.0277
${\rmrf50c1hsl6}A$-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
${\rmrf50c1hsl6}R$-RMSE	0.0452	0.0379
${\rmrf50c1hsl6}E$-RMSE	0.0547	0.0504
${\rmrf50c1hsl6}A$-RMSE	0.6778	0.6772

留言板

基于改進(jìn)艦尾流模型和多層耦合分析的機載雷達測量建模

doi: 10.16383/j.aas.c220815

計量

出版歷程

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

計量

出版歷程

目錄