A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis

D’Amato, Egidio; Nardi, V.; Notaro, Immacolata; Scordamaglia, Valerio

doi:10.3390/s21093066

Cited by 31 publications

(8 citation statements)

References 78 publications

(106 reference statements)

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“…al. In the article [89] authors focus on the detection of malfunctions of low-cost inertial sensors used in micro-and mini-UAVs. They use two parallel particle filters.…”

Section: Model-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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“…al. In the article [89] authors focus on the detection of malfunctions of low-cost inertial sensors used in micro-and mini-UAVs. They use two parallel particle filters.…”

Section: Model-based Methodsmentioning

confidence: 99%

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

Puchalski

Giernacki

2022

Drones

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“…Being an essential task, the problem of attitude estimation on the basis of sensor measurements can be non-trivial [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…At the cost of a further increase in computational burden, in literature several examples based on Particle Filtering (PF) [9,[32][33][34] exist.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Attitude and Altitude Estimation for Indoor Autonomous UAVs

Bassolillo

D’Amato

Notaro

et al. 2022

Drones

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In recent years the use of Unmanned Aerial Vehicles (UAVs) has considerably grown in the civil sectors, due to their high flexibility of use. Currently, two important key points are making them more and more successful in the civil field, namely the decrease of production costs and the increase in navigation accuracy. In this paper, we propose a Kalman filtering-based sensor fusion algorithm, using a low cost navigation platform that contains an inertial measurement unit (IMU), five ultrasonic ranging sensors and an optical flow camera. The aim is to improve navigation in indoor or GPS-denied environments. A multi-rate version of the Extended Kalman Filter is considered to deal with the use of heterogeneous sensors with different sampling rates, and the presence of non-linearities in the model. The effectiveness of the proposed sensor platform is evaluated by means of numerical tests on the dynamic flight simulator of a quadrotor. Results show high precision and robustness of the attitude estimation algorithm, with a reduced computational cost, being ready to be implemented on low-cost platforms.

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“…In recent years, quadrotor Unmanned Aerial Vehicle (UAV) has gradually become an important part of people’s lives [ 1 ]. The quadrotor UAV has the advantages of simple operation, vertical take-off and landing, and good flexibility [ 2 ]. It is widely used in video shooting, agricultural cultivation, logistics transportation, and other fields [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Model and Data-Driven Combination: A Fault Diagnosis and Localization Method for Unknown Fault Size of Quadrotor UAV Actuator Based on Extended State Observer and Deep Forest

Shang

Zhao

2022

Sensors

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The rotor is an essential actuator of quadrotor UAV, and is prone to failure due to high speed rotation and environmental disturbances. It is difficult to diagnose rotor faults and identify the fault localization simultaneously. In this paper, we propose a fault diagnosis and localization scheme based on the Extended State Observer (ESO) and Deep Forest (DF). This scheme can accurately complete the fault diagnosis and localization for the quadrotor UAV actuator without knowing the fault size by combining the model-based and the data-driven methods. First, we obtain the angular acceleration residual signal of the quadrotor UAV by using ESO. The residual signal is the difference between the observed state of ESO and the true fault state. Then, we design the residual feature analysis method by considering the position distribution of the quadrotor UAV actuator. This method can embed the actuator fault localization information into the fault data by simultaneously considering pitch and roll of the quadrotor UAV. Finally, we complete the fault diagnosis and localization of the quadrotor UAV actuator by processing the fault data by using DF. This scheme has the advantages of straightforward observer modeling, strong generalization ability, adaptability to small sample data, and few hyperparameters. Our simulation results indicate that the accuracy of the proposed scheme reaches more than 99% for the unknown size of the quadrotor UAV actuator fault.

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A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis

Cited by 31 publications

References 78 publications

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

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

Enhanced Attitude and Altitude Estimation for Indoor Autonomous UAVs

Model and Data-Driven Combination: A Fault Diagnosis and Localization Method for Unknown Fault Size of Quadrotor UAV Actuator Based on Extended State Observer and Deep Forest

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