Fault Detection, Isolation, and Data Fusion for Unmanned Aerial Vehicle Air Data Systems

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

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Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

Sensors ◽

10.3390/s20030919 ◽

2020 ◽

Vol 20 (3) ◽

pp. 919 ◽

Cited By ~ 3

Author(s):

Hao Du ◽

Wei Wang ◽

Chaowen Xu ◽

Ran Xiao ◽

Changyin Sun

Keyword(s):

Data Fusion ◽

State Estimation ◽

Real Time ◽

Unmanned Aerial Vehicle ◽

Position Estimation ◽

Sensor Data ◽

Measurement Unit ◽

Sensor Data Fusion ◽

Multi Sensor Data Fusion ◽

Aerial Vehicle

The question of how to estimate the state of an unmanned aerial vehicle (UAV) in real time in multi-environments remains a challenge. Although the global navigation satellite system (GNSS) has been widely applied, drones cannot perform position estimation when a GNSS signal is not available or the GNSS is disturbed. In this paper, the problem of state estimation in multi-environments is solved by employing an Extended Kalman Filter (EKF) algorithm to fuse the data from multiple heterogeneous sensors (MHS), including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), Light Detection and Ranging (LiDAR), and an RGB-D camera. Finally, the robustness and effectiveness of the multi-sensor data fusion system based on the EKF algorithm are verified by field flights in unstructured, indoor, outdoor, and indoor and outdoor transition scenarios.

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Research on Fault Detection and Diagnosis for Small Unmanned Aerial Vehicle

ESSE 2017 ◽

10.1515/9783110540048-054 ◽

2017 ◽

pp. 527-538

Keyword(s):

Fault Detection ◽

Unmanned Aerial Vehicle ◽

Fault Detection And Diagnosis ◽

Aerial Vehicle ◽

Detection And Diagnosis

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Development on Fault Detection and Diagnosis of Unmanned Aerial Vehicles

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.494-495.861 ◽

2014 ◽

Vol 494-495 ◽

pp. 861-864

Author(s):

Yi Peng Zhang ◽

Ke Cai Cao

Keyword(s):

Fault Detection ◽

Unmanned Aerial Vehicles ◽

Unmanned Aerial Vehicle ◽

State Of The Art ◽

Fault Detection And Diagnosis ◽

The Past ◽

Aerial Vehicles ◽

Aerial Vehicle ◽

Detection And Diagnosis

The reliability of unmanned aerial vehicles (UAVs) has caught the attention of many researchers in the past decades. This paper presents a review on the development and important issues of state-of-the-art researches in the field of fault detection and diagnosis (FDD) techniques. Faults on an individual unmanned aerial vehicle or a group of unmanned aerial vehicles are considered for providing an overall picture of fault detection and diagnosis approaches.

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Thrust balance estimation of an unmanned aerial vehicle: Application to fault detection

2016 3rd Conference on Control and Fault-Tolerant Systems (SysTol) ◽

10.1109/systol.2016.7739722 ◽

2016 ◽

Cited By ~ 1

Author(s):

Piotr Witczak ◽

Marcin Pazera ◽

Marcin Witczak ◽

Jozef Korbicz ◽

Didier Theilliol

Keyword(s):

Fault Detection ◽

Unmanned Aerial Vehicle ◽

Aerial Vehicle

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Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning

Remote Sensing ◽

10.3390/rs12091357 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1357 ◽

Cited By ~ 3

Author(s):

Maitiniyazi Maimaitijiang ◽

Vasit Sagan ◽

Paheding Sidike ◽

Ahmad M. Daloye ◽

Hasanjan Erkbol ◽

...

Keyword(s):

Machine Learning ◽

High Resolution ◽

Data Fusion ◽

Unmanned Aerial Vehicle ◽

Canopy Structure ◽

Canopy Height ◽

Spectral Information ◽

Structure Information ◽

Crop Monitoring ◽

Aerial Vehicle

Non-destructive crop monitoring over large areas with high efficiency is of great significance in precision agriculture and plant phenotyping, as well as decision making with regards to grain policy and food security. The goal of this research was to assess the potential of combining canopy spectral information with canopy structure features for crop monitoring using satellite/unmanned aerial vehicle (UAV) data fusion and machine learning. Worldview-2/3 satellite data were tasked synchronized with high-resolution RGB image collection using an inexpensive unmanned aerial vehicle (UAV) at a heterogeneous soybean (Glycine max (L.) Merr.) field. Canopy spectral information (i.e., vegetation indices) was extracted from Worldview-2/3 data, and canopy structure information (i.e., canopy height and canopy cover) was derived from UAV RGB imagery. Canopy spectral and structure information and their combination were used to predict soybean leaf area index (LAI), aboveground biomass (AGB), and leaf nitrogen concentration (N) using partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) with a newly proposed activation function. The results revealed that: (1) UAV imagery-derived high-resolution and detailed canopy structure features, canopy height, and canopy coverage were significant indicators for crop growth monitoring, (2) integration of satellite imagery-based rich canopy spectral information with UAV-derived canopy structural features using machine learning improved soybean AGB, LAI, and leaf N estimation on using satellite or UAV data alone, (3) adding canopy structure information to spectral features reduced background soil effect and asymptotic saturation issue to some extent and led to better model performance, (4) the ELR model with the newly proposed activated function slightly outperformed PLSR, RFR, and SVR in the prediction of AGB and LAI, while RFR provided the best result for N estimation. This study introduced opportunities and limitations of satellite/UAV data fusion using machine learning in the context of crop monitoring.

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