Improved IMM algorithm based on support vector regression for UAV tracking

被引：0

作者：

ZENG Yuan ^{[1
,2
]}

LU Wenbin ^{[1
]}

YU Bo ^{[3
]}

TAO Shifei ^{[3
]}

ZHOU Haosu ^{[1
]}

CHEN Yu ^{[2
]}

机构：

[1] Shanghai Spaceflight Electronic and Communication Equipment Research Institute

[2] Science and Technology on Near-Surface Detection Laboratory

[3] School of Electronic and Optical Engineering, Nanjing University of Science and Technology

来源：

JournalofSystemsEngineeringandElectronics | 2022年 / 33卷 / 04期

关键词：

D O I：

暂无

中图分类号：

V279 [无人驾驶飞机]; TP18 [人工智能理论];

学科分类号：

1111 ; 081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the development of technology, the relevant performance of unmanned aerial vehicles(UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirements on target tracking technology. Strong maneuvering refers to relatively instantaneous and dramatic changes in target acceleration or movement patterns, as well as continuous changes in speed,angle, and acceleration. However, the traditional UAV tracking algorithm model has poor adaptability and large amount of calculation. This paper applies support vector regression(SVR)to the interacting multiple model(IMM) algorithm. The simulation results show that the improved algorithm has higher tracking accuracy for highly maneuverable targets than the original algorithm, and can adjust parameters adaptively, making it more adaptable.

引用

页码：867 / 876

页数：10

共 50 条

[21] An Improved Radar and Infrared Sensor Tracking Fusion Algorithm Based on IMM-UKF [J].

Bao, Tiantian ;

Zhang, Zhenkai ;

Sabahi, Mohamad Farzan .

PROCEEDINGS OF THE 2019 IEEE 16TH INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC 2019), 2019, :420-423

[22] Agricultural Economic Evaluation based on Improved Support Vector Regression [J].

Huang, Min .

PROCEEDINGS OF 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION (ICICTA 2015), 2015, :118-121

[23] Improved Perturb and Observation Method Based on Support Vector Regression [J].

Tan, Bicheng ;

Ke, Xin ;

Tang, Dachuan ;

Yin, Sheng .

ENERGIES, 2019, 12 (06)

[24] Adaptive support vector regression for UAV flight control [J].

Shin, Jongho ;

Kim, H. Jin ;

Kim, Youdan .

NEURAL NETWORKS, 2011, 24 (01) :109-120

[25] Shape from shading based on support vector regression algorithm [J].

Hu, Zhiyong ;

Zhang, Xiufen ;

Xu, Jinghua .

Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery, 2012, 43 (02) :216-220

[26] Blind equalization algorithm based on complex support vector regression [J].

Yang L. ;

Chen L. ;

Zhao B. ;

Zhang G. ;

Li Y. .

Tongxin Xuebao/Journal on Communications, 2019, 40 (10) :180-188

[27] Parameters Optimization of Support Vector Regression based on Cultural Algorithm [J].

Yang, Kaifan .

2011 AASRI CONFERENCE ON APPLIED INFORMATION TECHNOLOGY (AASRI-AIT 2011), VOL 1, 2011, :218-221

[28] Novel blind recovery algorithm based on support vector regression [J].

Ruan, Xiu-Kai ;

Zhang, Zhi-Yong .

Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and Technology, 2009, 33 (SUPPL. 1) :204-208

[29] Weighted Support Vector Regression Algorithm Based on Data Description [J].

Huang, Weimin ;

Shen, Leping .

2008 ISECS INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT, VOL 1, PROCEEDINGS, 2008, :250-254

[30] Support Vector Regression Hybrid Algorithm Based on Rough Set [J].

Deng, Jiuying ;

Chen, Qiang ;

Mao, Zongyuan ;

Gao, Xiangjun .

2008 IEEE CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS, VOLS 1 AND 2, 2008, :188-+

← 1 2 3 4 5 →