An Informational Approach for Fault Tolerant Data Fusion Applied to a UAV’s Attitude, Altitude, and Position Estimation

Saied, Majd; Tabikh, Ali Rida; Francis, Clovis; Hamadi, Hussein; Lussier, Benjamin

doi:10.1109/jsen.2021.3124731

Cited by 18 publications

(3 citation statements)

References 40 publications

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“…In another work [131], Saied et al similarly to [78] consider the problem of fault detection of navigation sensors. Together, for error detection and error identification, they use two extended informational Kalman filters, the outputs of which are compared using the Bhattacharyya distance metric.…”

Section: Mixed Model-and Data-based Methodsmentioning

confidence: 99%

UAV Fault Detection Methods, State-of-the-Art

Puchalski

Giernacki

2022

Drones

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The continual expansion of the range of applications for unmanned aerial vehicles (UAVs) is resulting in the development of more and more sophisticated systems. The greater the complexity of the UAV, the greater the likelihood that a component will fail. Due to the fact that drones often operate in close proximity to humans, the reliability of flying robots, which directly affects the level of safety, is becoming more important. This review article presents recent research works on fault detection on unmanned flying systems. They include papers published between January 2016 and August 2022. Web of Science and Google Scholar databases were used to search for articles. Terminology related to fault detection of unmanned aerial vehicles was used as keywords. The articles were analyzed, each paper was briefly summarized and the most important details concerning each of the described articles were summarized in the table.

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Section: Mixed Model-and Data-based Methodsmentioning

confidence: 99%

UAV Fault Detection Methods, State-of-the-Art

Puchalski

Giernacki

2022

Drones

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“…In recent years, unmanned aerial vehicles (UAVs) have experienced rapid development in both civilian and military sectors [ 1 , 2 , 3 ]. UAVs implement control through sensitive flight parameters such as launch force, flight attitude, altitude, speed, position, etc., ensuring the safety and efficient completion of tasks [ 4 , 5 , 6 , 7 ]. The flight phases of a UAV can be divided into the initial stage, mid-stage, and final stage.…”

Section: Introductionmentioning

confidence: 99%

Research on UAV Flight Parameter Identification Method Based on Launch Force and Airspeed

Chen,

Li,

et al. 2024

Sensors

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Flight parameters are crucial criteria for UAV control, playing a significant role in ensuring the safe and efficient completion of missions. Launch force and airspeed information are key parameters in the early and middle stages of flight, serving as important data for monitoring the UAV’s flight status. In response to challenges such as weak launch force, low identification rates, small airspeed, and low recognition accuracy in UAVs, a method for identifying UAV flight parameters based on launch force and airspeed is proposed. From the aspect of launch force identification, a recognition method based on a low-g value accelerometer information source is proposed, utilizing a ‘multi-level time window + threshold’ approach. For airspeed identification, an optimization method for airspeed measurement under the Kalman filter architecture is introduced. A device for airspeed measurement based on pressure sensors is designed, and the recommended installation position is determined through simulation. Furthermore, the feasibility and robustness of the proposed launch force identification and airspeed measurement optimization methods are validated through simulation. Finally, the effectiveness of the design is verified through centrifuge and wind tunnel experiments. This research provides technical support for the identification of the launch force and airspeed measurement in UAVs.

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“…In the development and application of rotorcraft, to overcome the errors caused by a single sensor, most of the UAV navigation and positioning are carried out by multi-sensor fusion. The integrated navigation system applies multi-sensor information fusion technology that unifies each navigation system's sensor data into the same coordinate system and then fuses the data with appropriate mathematical estimation methods to obtain the optimal estimation of the body motion state [2]. In the current civil rotorcraft integrated navigation system, the most widely used method is the combination of inertial navigation and satellite navigation [3] [4].…”

Section: Introductionmentioning

confidence: 99%

Design of Multi-sensor Fusion Positioning and Navigation Method Based on Federated Kalman Filter for Low-Speed Rotorcraft of Atmospheric Detection

Chen

Rahman

2023

J. Phys.: Conf. Ser.

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A rotorcraft needs to accurately sample the predetermined path at a low flight rate when performing atmospheric sounding tasks; thus, it is particularly critical to obtain accurate positioning and navigation information in real-time. Hence, this paper designs a multi-sensor fusion positioning and navigation algorithm for atmospheric detection of low-speed rotorcraft. The multi-sensor fusion positioning and navigation algorithm for velocity and spatial position are presented based on the Federated Kalman Filter combined with the accelerometer, Global Positioning System (GPS), and barometer. The experimental results show that the fusion algorithm result of the velocity and spatial position is closer to the actual position than a single sensor. In conclusion, this paper’s multi-sensor fusion positioning and navigation algorithm overcomes the problems of low positioning accuracy of a single sensor and cumulative errors and effectively improves positioning accuracy, real-time, and stability.

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An Informational Approach for Fault Tolerant Data Fusion Applied to a UAV’s Attitude, Altitude, and Position Estimation

Cited by 18 publications

References 40 publications

UAV Fault Detection Methods, State-of-the-Art

UAV Fault Detection Methods, State-of-the-Art

Research on UAV Flight Parameter Identification Method Based on Launch Force and Airspeed

Design of Multi-sensor Fusion Positioning and Navigation Method Based on Federated Kalman Filter for Low-Speed Rotorcraft of Atmospheric Detection

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