Sensitivity Analysis of Model-based Synthetic Air Data Estimators

Lie, Fidelis Adhika Pradipta; Gebre-Egziabher, Demoz

doi:10.2514/6.2015-0081

Cited by 10 publications

(7 citation statements)

References 9 publications

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“…and note that Va can be replaced with Ṽa,syn or Va,TCN (see Section VI.C). The dynamic equation Va for the airspeed state V a can be obtained from (11).…”

Section: A Ukf For Airspeed Estimationmentioning

confidence: 99%

“…Unfortunately, when GPS is not available due to some situations, such as jamming or spoofing [10], the synthetic airspeed estimate will not be available. Second, most of the previous studies utilized highfidelity vehicle dynamic UAV models to estimate the air data [11]- [13]. However, obtaining an accurate high-fidelity UAV model is a very complex, costly, and time-consuming task.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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.

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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.

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“…and note that Va can be replaced with Ṽa,syn or Va,TCN (see Section VI.C). The dynamic equation Va for the airspeed state V a can be obtained from (11).…”

Section: A Ukf For Airspeed Estimationmentioning

confidence: 99%

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.

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“…The air data functions (ADFs) shall calculate all air parameters as required by the standard AS8002 [5] from direct measures of the following: Synthetic sensors are researched extensively in the academic field of air data, but there are no examples of synthetic sensors certified for civil operations. Air data synthetic sensors are mainly used as (1) Pitot-free aircraft speed estimators [6], (2) vane/sensor-free flow angle estimators [7], and (3) Pitot and vane/sensor-free for both airspeed and flow angle estimators [8].…”

Section: Introductionmentioning

confidence: 99%

Safety Analysis of a Certifiable Air Data System Based on Synthetic Sensors for Flow Angle Estimation

Lerro

Battipede

2021

Applied Sciences

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This work deals with the safety analysis of an air data system (ADS) partially based on synthetic sensors. The ADS is designed for the small aircraft transportation (SAT) community and is suitable for future unmanned aerial vehicles and urban air mobility applications. The ADS’s main innovation is based on estimation of the flow angles (angle-of-attack and angle-of-sideslip) using synthetic sensors instead of classical vanes (or sensors), whereas pressure and temperature are directly measured with Pitot and temperature probes. As the air data system is a safety-critical system, safety analyses are performed and the results are compared with the safety objectives required by the aircraft integrator. The present paper introduces the common aeronautical procedures for system safety assessment applied to a safety critical system partially based on synthetic sensors. The mean time between failures of ADS’s sub-parts are estimated on a statistical basis in order to evaluate the failure rate of the ADS’s functions. The proposed safety analysis is also useful in identifying the most critical air data system parts and sub-parts. Possible technological gaps to be filled to achieve the airworthiness safety objectives with nonredundant architectures are also identified.

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“…Moreover, the accuracy of AOA and wind speed significantly influences the accuracy of the synthetic airspeed. The other primary category of airspeed estimation without direct measurement utilizes high fidelity vehicle dynamics models of UAVs to estimate air data, which is often referred to as aerodynamic model-based SADS [15]. These types of estimation filters have been explored in various works such as [16] and [17].…”

Section: Introductionmentioning

confidence: 99%

Model-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy

Youn

Ryu

Jang

et al. 2021

IEEE Robot. Autom. Lett.

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This paper proposes a novel method for modelaided synthetic airspeed estimation of UAVs. The major contribution of the proposed algorithm is that the synthetic airspeed measurement is newly formulated for analytical redundancy. This filter only requires inertial measurement unit (IMU), airflow angles, and elevator control input along with a simple aircraft model containing only three lift coefficient parameters; no GPS or complex aircraft dynamic model are required. Particularly, two novel filters (unscented Kalman filter and complementary filter) are proposed and evaluated without direct airspeed and GPS measurements. Flight test results of a UAV demonstrated that the proposed algorithm yields accurate estimated airspeed, demonstrating its effectiveness for analytical redundancy.

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Sensitivity Analysis of Model-based Synthetic Air Data Estimators

Cited by 10 publications

References 9 publications

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

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

Safety Analysis of a Certifiable Air Data System Based on Synthetic Sensors for Flow Angle Estimation

Model-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy

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