Research on Cooperative Navigation Algorithm of the UAV Swarm Based on Spherical Interpolation

2021 ◽  
pp. 4459-4470
Author(s):  
Junnan Du ◽  
Rong Wang ◽  
Zhi Xiong ◽  
Jianye Liu
Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 782
Author(s):  
Shuo Cao ◽  
Honglei Qin ◽  
Li Cong ◽  
Yingtao Huang

Position information is very important tactical information in large-scale joint military operations. Positioning with datalink time of arrival (TOA) measurements is a primary choice when a global navigation satellite system (GNSS) is not available, datalink members are randomly distributed, only estimates with measurements between navigation sources and positioning users may lead to a unsatisfactory accuracy, and positioning geometry of altitude is poor. A time division multiple address (TDMA) datalink cooperative navigation algorithm based on INS/JTIDS/BA is presented in this paper. The proposed algorithm is used to revise the errors of the inertial navigation system (INS), clock bias is calibrated via round-trip timing (RTT), and altitude is located with height filter. The TDMA datalink cooperative navigation algorithm estimate errors are stated with general navigation measurements, cooperative navigation measurements, and predicted states. Weighted horizontal geometric dilution of precision (WHDOP) of the proposed algorithm and the effect of the cooperative measurements on positioning accuracy is analyzed in theory. We simulate a joint tactical information distribution system (JTIDS) network with multiple members to evaluate the performance of the proposed algorithm. The simulation results show that compared to an extended Kalman filter (EKF) that processes TOA measurements sequentially and a TDMA datalink navigation algorithm without cooperative measurements, the TDMA datalink cooperative navigation algorithm performs better.


2021 ◽  
Vol 1207 (1) ◽  
pp. 012002
Author(s):  
Yang Shao ◽  
Qinghua Luo ◽  
Chao Liu ◽  
Xiaozhen Yan ◽  
Kexin Yang

Abstract Cooperative navigation is one of the key methods for multiple autonomous underwater vehicles (AUVs) to obtain accurate positions when performing tasks underwater. In the realistic state-space model of the multi-AUV cooperative navigation system, where the system noise does not satisfy the additivity, it is necessary to augment the dimension of the state variables before nonlinear filtering. Aiming at the problem that the error of traditional algorithms increases linearly with the dimension of state-space, a cooperative navigation method based on Augmented Embedded Cubature Kalman filter (AECKF) algorithm is proposed. The experiment results show that the AECKF cooperative navigation algorithm has better positioning accuracy and stability than the traditional algorithm.


2021 ◽  
Author(s):  
Mang Wang ◽  
Xianfei Pan ◽  
Langping An ◽  
Ze Chen ◽  
Zheming Tu ◽  
...  

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1044 ◽  
Author(s):  
Zheping Yan ◽  
Lu Wang ◽  
Tongda Wang ◽  
Zewen Yang ◽  
Tao Chen ◽  
...  

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