Design and Evaluation of UAV Flow Angle Estimation Filters

Tian, Pengzhi; Huo, Chao; Flanagan, Harold P.; Hagerott, Steven G.; Gu, Yu

doi:10.1109/taes.2018.2852359

Cited by 23 publications

(9 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A CF typically consists of combining high-frequency information from one source with low-frequency information from another source. For state x , the CF algorithm can be summarized in the Laplace domain by [ 45 ]

where ζ CF and ω CF represent the damping ratio and natural frequency for the CF. Alternatively, specifically for the knee flexion angle application, an example CF for discrete time is offered in Seel et al as follows [ 17 ]:

where λ is the fixed gain for the CF, which ranges from 0 to 1.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running

Rhudy,

Mahoney,

Altman-Singles

2024

Sensors

View full text Add to dashboard Cite

The knee flexion angle is an important measurement for studies of the human gait. Running is a common activity with a high risk of knee injury. Studying the running gait in realistic situations is challenging because accurate joint angle measurements typically come from optical motion-capture systems constrained to laboratory settings. This study considers the use of shank and thigh inertial sensors within three different filtering algorithms to estimate the knee flexion angle for running without requiring sensor-to-segment mounting assumptions, body measurements, specific calibration poses, or magnetometers. The objective of this study is to determine the knee flexion angle within running applications using accelerometer and gyroscope information only. Data were collected for a single test participant (21-year-old female) at four different treadmill speeds and used to validate the estimation results for three filter variations with respect to a Vicon optical motion-capture system. The knee flexion angle filtering algorithms resulted in root-mean-square errors of approximately three degrees. The results of this study indicate estimation results that are within acceptable limits of five degrees for clinical gait analysis. Specifically, a complementary filter approach is effective for knee flexion angle estimation in running applications.

show abstract

where λ is the fixed gain for the CF, which ranges from 0 to 1.…”

Section: Methodsmentioning

confidence: 99%

“…A CF typically consists of combining high-frequency information from one source with low-frequency information from another source. For state x, the CF algorithm can be summarized in the Laplace domain by [45]…”

Section: Complementary Filter (Cf)mentioning

confidence: 99%

Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running

Rhudy,

Mahoney,

Altman-Singles

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The complementary filter (CF), which consists of secondorder high-pass and low-pass filters, is another attractive method to reconstruct airspeed with low computational complexity. In summary, given [a x a y a z ] , [φ θ] , [α β] , and δ e , the proposed second-order CF, the airspeed V a,cf , can be reconstructed by integrating the high-frequency part of Va and the low-frequency synthetic measurements Ṽa,syn as follows [1], [26]:…”

Section: B Second Order Cf For Airspeed Estimationmentioning

confidence: 99%

“…Basically, ADS is a vital component of the existing sensor suite for both manned and unmanned aerial vehicles (UAVs) and directly provides some measurements, including the angle of attack (AOA), side slip angle (SSA), and airspeed. These parameters contain essential information about an aircraft's performance and safety in both normal and abnormal conditions [1], [2]. Unfortunately, ADSs are usually deployed outside an aircraft fuselage; thus, ADSs are likely to be exposed to conditions which can cause malfunctions.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy With a Temporal Convolutional Network

Lim

Ryu

Rhudy

et al. 2022

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

A synthetic air data system (SADS) is an analytical redundancy technique that is crucial for unmanned aerial vehicles (UAVs) and is used as a backup system during air data sensor failures. Unfortunately, the existing state-of-theart approaches for SADS require GPS signals or high-fidelity dynamic UAV models. To address this problem, a novel synthetic airspeed estimation method that leverages deep learning and an unscented Kalman filter (UKF) for analytical redundancy is proposed. Our novel fusion-based method only requires an inertial measurement unit (IMU), elevator control input, and airflow angles while GPS, lift/drag coefficients, and complex aircraft dynamic models are not required. Additionally, we demonstrate that our proposed temporal convolutional network (TCN) is a more efficient model for airspeed estimation than the renowned models, such as ResNet or bidirectional long short-term memory (LSTM). Our deep learning-aided UKF was experimentally verified on long-duration real flight data and has promising performance compared with the state-of-theart methods. In particular, it is confirmed that our proposed method robustly estimates the airspeed even under dynamic flight conditions where the performance of conventional methods is degraded.

show abstract

“…It can be seen from Figure 8 that the attitude angle would accumulate drift after the solution, because there still have been some errors after the calibration of the UAV gyroscope. The corrected sampling value can be combined with the data fusion algorithm, 25,26 for example, Kalman filter and complementary filter, to suppress the accumulated errors and ensure the attitude stability for long-time flight.…”

Section: Experiments On Uavmentioning

confidence: 99%

A cross product calibration method for micro-electro mechanical system gyroscope in unmanned aerial vehicle attitude determination system

Wang

2020

Measurement and Control

View full text Add to dashboard Cite

This paper presents a novel calibration method for micro-electro mechanical system gyroscope in attitude measurement system of small rotor unmanned aerial vehicles. This method is based on an observation vector and its cross product, which is especially valuable for the in-field calibration without the aid of external equipment. By analysing the error model of the tri-axial gyroscope, the principle of calibration is proposed. Compared with other algorithms, numerical simulations are performed to evaluate the effectiveness of integral form of the cross product calibration method. Experiment on the hex-rotor unmanned aerial vehicle platform shows that the proposed method has great advantages in low-cost integrated navigation system.

show abstract

Design and Evaluation of UAV Flow Angle Estimation Filters

Cited by 23 publications

References 26 publications

Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running

Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running

Deep Learning-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy With a Temporal Convolutional Network

A cross product calibration method for micro-electro mechanical system gyroscope in unmanned aerial vehicle attitude determination system

Contact Info

Product

Resources

About