Calibration of static pressure sensors using Extended Kalman Filter at high angles of attack and transonic Mach numbers

Kamali, C; Jain, Shikha; Saraf, Amitabh; Goyal, Anup

doi:10.1109/indiancc.2016.7441170

Cited by 6 publications

(9 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on real flight test data, three recursive parameter estimation algorithms including EKF, simplified Unscented Kalman Filter (UKF) and UKF are compared for APE of two aircrafts (Chowdhary & Jategaonkar, 2009). EKF based technique for the calibration of various parameters measured by air data system of an aircraft is studied in (Kamali et al, 2016). In Bayoğlu and Nalci (2016) an adaptive KF is applied to APE for a supersonic missile having rapid speed variations.…”

Section: Introductionmentioning

confidence: 99%

New Heuristic Algorithms for Rolling Air Frame Aerodynamic Parameters Estimation

Mohamad¹,

Karimi²,

Naderi³

2020

Aviation

View full text Add to dashboard Cite

In this research, based on heuristic optimization algorithms, three new strategies are developed for Aerodynamic Parameters Estimation (APE) of one pair ON-OFF actuator rolling airframe. In the 1st method namely EAM-PSO the aerodynamic parameters are directly estimated. While, the next two algorithms called EBM-PSO and SEBM-PSO are two-step strategies. In the 1st step the aerodynamic forces and moments are estimated, then after passing through a designed smoothing filter, in the 2nd step aerodynamic parameters are estimated. In EBM-PSO all the aerodynamic parameters are estimated at once by solving one optimization problem. In SEBM-PSO the APE is converted to solve four separate optimization problems. A modified particle swarm optimization algorithm is developed and used in estimation process. The performance of proposed algorithms is compared with that of state of the art algorithm EKF. The simulation results show that SEBM-PSO and EBM-PSO are better than EAM-PSO in term of accuracy and run time.

show abstract

Section: Introductionmentioning

confidence: 99%

New Heuristic Algorithms for Rolling Air Frame Aerodynamic Parameters Estimation

Mohamad¹,

Karimi²,

Naderi³

2020

Aviation

View full text Add to dashboard Cite

show abstract

“…But in [1], the wind estimation is not taken into consideration. Experiences on flight testing of a high-performance aircraft show that the FPR using MLE works well for low angles of attack and in the presence of steady winds that are not varying with altitude [6]. But to achieve high angles of attack, wind up turn maneuvers are performed where the wind significantly varies with altitude.…”

Section: Introductionmentioning

confidence: 99%

“…But to achieve high angles of attack, wind up turn maneuvers are performed where the wind significantly varies with altitude. It is observed in [6] that the MLE based method is not able to estimate varying winds with altitude and hence results in poor FPR. Whereas the KF based techniques [6][7][8][9] have been shown to perform better under these conditions.…”

Section: Introductionmentioning

confidence: 99%

“…It is observed in [6] that the MLE based method is not able to estimate varying winds with altitude and hence results in poor FPR. Whereas the KF based techniques [6][7][8][9] have been shown to perform better under these conditions. In references [6][7][8][9], KF based techniques have been developed for air data calibration and accuracy determination of airdata system of a high-performance aircraft.…”

Section: Introductionmentioning

confidence: 99%

“…Whereas the KF based techniques [6][7][8][9] have been shown to perform better under these conditions. In references [6][7][8][9], KF based techniques have been developed for air data calibration and accuracy determination of airdata system of a high-performance aircraft. But in [6][7][8][9], more accurate inertial sensors have been used to calibrate the airdata sensors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Limitations of Flight Path Reconstruction techniques

Kamali

Ozger

2019

Sādhanā

Self Cite

View full text Add to dashboard Cite

The Flight Path Reconstruction (FPR) techniques are performed to verify the data compatibility check and post-flight. This is often achieved by calibrating the onboard sensors such as inertial and airdata sensors. In this paper, the limitations of FPR techniques in terms of Maximum Likelihood Estimation (MLE) and Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) have been reported. To demonstrate the FPR and sensor calibration, kinematic trajectory simulations with wind box type maneuvers have been performed. It is also shown as how a kinematic simulation is valid for the studies carried out in this work.

show abstract

A Comparative Study on Air Data System Calibration Methods based on Machine Learning

Lee

Kim

et al. 2019

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

View full text Add to dashboard Cite

Calibration of static pressure sensors using Extended Kalman Filter at high angles of attack and transonic Mach numbers

Cited by 6 publications

References 2 publications

New Heuristic Algorithms for Rolling Air Frame Aerodynamic Parameters Estimation

New Heuristic Algorithms for Rolling Air Frame Aerodynamic Parameters Estimation

Limitations of Flight Path Reconstruction techniques

A Comparative Study on Air Data System Calibration Methods based on Machine Learning

Contact Info

Product

Resources

About