Integration of inertial and satellite navigation systems using corrective circuits for UAV

Vasyliev, Volodymyr; Rogozhyn, V.O.; Dolintse, B.I

doi:10.1109/apuavd.2015.7346597

Cited by 6 publications

(5 citation statements)

References 1 publication

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“…In this paper, it is only verified how to use Algorithm 1 to solve the problem for multi-UAV with fixed formation avoiding the obstacles is effective, where the communication topology is reliable and time-invariant. The communication topology of multi-UAV formation is designed to It is obvious that only leader can receive information from ground base station [33], the member of multi-UAV could exchange information from others. The weighted adjacency matrixAof Figure 5 is…”

Section: Design Experiments For Validationmentioning

confidence: 99%

“…The parameters of ①, ② and ③ are brought to Equation (33), and it gets According to Section 4.1 and parts of Algorithm 3, it achieves mutual collision avoidance between two leaders with the same priority using Equation (38). In other words, each leader has to actively avoid the other leader.…”

Section: Design Experiments For Validationmentioning

confidence: 99%

“…It is obvious that only leader can receive information from ground base station [33], the member of multi‐UAV could exchange information from others. The weighted adjacency matrix A of Figure 5 is

A badbreak= ([], \begin{matrix} a_{11} & a_{12} & a_{13} & a_{1 L} & a_{1 G} \\ a_{21} & a_{22} & a_{23} & a_{2 L} & a_{2 G} \\ a_{31} & a_{32} & a_{33} & a_{3 L} & a_{3 G} \\ a_{L 1} & a_{L 2} & a_{L_{3}} & a_{L L} & a_{L G} \\ a_{G 1} & a_{G 2} & a_{G 3} & a_{G L} & a_{G G} \end{matrix}) goodbreak= ([], \begin{matrix} 0 & 1 & 0 & 1 & 0 \\ 1 & 0 & 1 & 0 & 0 \\ 0 & 1 & 0 & 0 & 0 \\ 1 & 0 & 0 & 0 & 1 \\ 0 & 0 & 0 & 0 & 0 \end{matrix}) . ...

…”

Section: Multi‐uav With Fixed 3‐d Formation Avoiding a Spherical Obst...mentioning

confidence: 99%

“…Notes that

{\alpha _i} = \prod\nolimits_{i \ne j} {\frac{{{K_j}}}{{{K_i} + {K_j}}}}

is the weighted factor [33].

{K_i}

denotes the distance between the UAV position

(x,y)

and the circular obstacle position

{({O_x},{O_y})_{i - th}}

.…”

Section: Multi‐uav With Fixed 3‐d Formation Avoiding a Spherical Obst...mentioning

confidence: 99%

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Research on the strategies for collision avoidance of multi‐UAV with three dimensional formation in combination of consensus algorithm and uniform flow

Cao

2023

IET Control Theory & Appl

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The paper proposes the method of uniform flow loading the improved consensus algorithm to achieve obstacle avoidance for multi‐UAV with 3‐D formation. In view of that the flow function is generally only applicable to two dimensional flow field, dimension reduction is to treat multi‐UAV formation as each UAV flows around along the circular surface of the projected cylinder of spherical obstacle at its different heights. To formation guaranteeing the smooth realization of obstacle avoidance of multi‐UAV formation centered on the leader, it is used the tactics of virtually expanding the radius of these static or moving equivalent cylindrical obstacles to reserve a certain space. The innovation of this strategy is to further reduce the possibility of collision between UAV formation and the edge of obstacles, meanwhile the problem of multi‐UAV formation avoiding obstacles is transformed into the problem of collision avoidance navigation for the leader treated as a particle. It is considered collision avoidance for multi‐UAV formation under three scenarios assumed are the most common situations, and the simulation experimental results of these scenarios are demonstrated on the validity of the designed experiments based on these algorithms.

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Section: Design Experiments For Validationmentioning

confidence: 99%

Section: Design Experiments For Validationmentioning

confidence: 99%

A badbreak= ([], \begin{matrix} a_{11} & a_{12} & a_{13} & a_{1 L} & a_{1 G} \\ a_{21} & a_{22} & a_{23} & a_{2 L} & a_{2 G} \\ a_{31} & a_{32} & a_{33} & a_{3 L} & a_{3 G} \\ a_{L 1} & a_{L 2} & a_{L_{3}} & a_{L L} & a_{L G} \\ a_{G 1} & a_{G 2} & a_{G 3} & a_{G L} & a_{G G} \end{matrix}) goodbreak= ([], \begin{matrix} 0 & 1 & 0 & 1 & 0 \\ 1 & 0 & 1 & 0 & 0 \\ 0 & 1 & 0 & 0 & 0 \\ 1 & 0 & 0 & 0 & 1 \\ 0 & 0 & 0 & 0 & 0 \end{matrix}) . ...

…”

Section: Multi‐uav With Fixed 3‐d Formation Avoiding a Spherical Obst...mentioning

confidence: 99%

“…Notes that

{\alpha _i} = \prod\nolimits_{i \ne j} {\frac{{{K_j}}}{{{K_i} + {K_j}}}}

is the weighted factor [33].

{K_i}

denotes the distance between the UAV position

(x,y)

and the circular obstacle position

{({O_x},{O_y})_{i - th}}

.…”

Section: Multi‐uav With Fixed 3‐d Formation Avoiding a Spherical Obst...mentioning

confidence: 99%

See 2 more Smart Citations

Research on the strategies for collision avoidance of multi‐UAV with three dimensional formation in combination of consensus algorithm and uniform flow

Cao

2023

IET Control Theory & Appl

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“…In addition to classic global navigation systems, existing studies suggest using geostationary satellites as sources of navigation information to increase the accuracy of information about object positioning [5]. Early developments of positioning subsystems for UAVs used a simple integration of inertial sensors and GPS [6]. Still, the approaches became more complex over time, allowing for the simultaneous processing of more diverse sources of positioning information.…”

Section: Introductionmentioning

confidence: 99%

Enhancing accuracy of information processing in onboard subsystems of UAVs

Zhukov,

Dolintse

2023

TAPR

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The object of research is the onboard subsystems of Unmanned Aerial Vehicles (UAVs). The research is aimed at analyzing UAVs, specifically the integration and enhancement of satellite-based positioning systems, including Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS). The problem concerns traditional satellite-based positioning services, especially those relying solely on medium earth orbit (MEO) satellites, which are insufficient for specific requirements. The study aims to address the limitations of these systems on onboard subsystems of UAVs, especially in challenging environments laden with jammers and interference, and to provide a more accurate, robust, and continuous positioning solution. The research proposes a «multilayer system of systems» approach that integrates signals from various sources, including low Earth orbit (LEO) satellites, ground-based positioning, navigation, and timing (PNT) systems, and user-centric sensors. The combined approach, termed LeGNSS/INS, leverages the strengths of each component, providing redundancy and enhanced accuracy. The system's performance was evaluated using pseudo-real output data, demonstrating its ability to generate quasi-real dynamic trajectories for UAV flight. The error analysis showed that the proposed method consistently outperforms traditional GNSS systems, especially in challenging environments. The enhanced performance of the LeGNSS/INS system can be attributed to integrating multiple satellite systems with INS and applying optimal filtering techniques. The research also employed mathematical modeling to represent the dependencies and interactions when combining data from different sources, such as GPS, LEO, and INS. The Kalman filter is a mechanism to fuse data from multiple sources optimally. The insights from this study apply to various sectors, including aviation, maritime navigation, autonomous drones, and defense. The enhanced positioning accuracy can significantly improve safety, navigation precision, and operational efficiency. However, the study assumes idealized conditions for satellite signal reception, which might not always be accurate in real-world scenarios. Challenges, such as the martial law conditions in Ukraine affecting data collection and potential satellite signal restrictions, were also highlighted. Further research can delve into the impact of more complex environmental factors and the integration of additional satellite systems or sensors to enhance accuracy further.

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A New Approach to Navigation of Unmanned Aerial Vehicle using Deep Transfer Learning

Silva

Filho

Marinho

et al. 2019

2019 8th Brazilian Conference on Intelligent Systems (BRACIS)

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Integration of inertial and satellite navigation systems using corrective circuits for UAV

Cited by 6 publications

References 1 publication

Research on the strategies for collision avoidance of multi‐UAV with three dimensional formation in combination of consensus algorithm and uniform flow

Research on the strategies for collision avoidance of multi‐UAV with three dimensional formation in combination of consensus algorithm and uniform flow

Enhancing accuracy of information processing in onboard subsystems of UAVs

A New Approach to Navigation of Unmanned Aerial Vehicle using Deep Transfer Learning

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