Aircraft attitude estimation based on central difference Kalman filter

Han, Ping; Gan, Haoliang; He, Weikun; Alazard, Daniel; Defaÿ, François; Brière, Yves

doi:10.1109/icosp.2012.6491658

Cited by 11 publications

(5 citation statements)

References 8 publications

(6 reference statements)

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“…Missile: DFMF is used as low-pass filter and derivative network [10][11] , and its memory length parameter is 0.8 in simulations. The curves of simulation results are showed as Fig.1-4.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…A simple method is that we defined the Ω as Im I c Ω plus Gaussian white noise. The foundation of CDKF is central difference approximation, which is a method for calculating the statistics of random variable via a nonlinear function [7][8] . We suppose that states are Gaussian random variables, therefore, only mean and covariance are essential to be approximated.…”

Section: Los Rate Estimation Methods Based On Central Difference and Cdkfmentioning

confidence: 99%

“…But truncations decrease estimation precision; Calculation of Jacobian matrix's are nontrivial in most applications and usually leads to significant implemental difficulty. A new method, CDKF [7][8][9] without and approximation for the nonlinear system model was proposed. The CDKF can predict the mean and covariance up to, even higher than the second order precision for Gauss distributed random variable.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Research on Line of Sight Angle Rate Reconstruction based on Strap down Seeker

Jiang¹,

Nie²,

Zhang³

2015

Advances in Intelligent Systems Research

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Abstract. The detecting information of strap down seeker can't be applied to proportional navigation directly because it is coupled with missile attitude. So the method of line of sight (LOS) rate reconstruction for strap down seeker is proposed. The reconstruction result would be reflected by background noise and any inaccurate metrical information. Thus a LOS rate reconstruction method based on central difference approximation and Central Difference Kalman Filter (CDKF) was proposed to obtain more precise guidance information. Monte Carlo simulations confirmed the validity of the designed method.

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“…Missile: DFMF is used as low-pass filter and derivative network [10][11] , and its memory length parameter is 0.8 in simulations. The curves of simulation results are showed as Fig.1-4.…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Los Rate Estimation Methods Based On Central Difference and Cdkfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on Line of Sight Angle Rate Reconstruction based on Strap down Seeker

Jiang¹,

Nie²,

Zhang³

2015

Advances in Intelligent Systems Research

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“…UKF can be directly applied to strongly nonlinear systems with at least second order approximation precision and Jacobian matrices are needless, which improves the estimation accuracy and makes it easier to implement (Chang et al, 2018; Hajiyev and Guler, 2017; Lee et al, 2017). CDKF performs a little better than UKF in terms of estimation accuracy (AbdelAziz and Hamza, 2011; Han et al, 2012; Wu and Zhao, 2012). The more obvious advantage of CDKF is that only one tuning parameter is required in contrast to three ones adopted in UKF, it means that CDKF is more convenient and reliable in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Strong tracking central difference Kalman filter for CubeSat attitude estimation with status mutation

Zhang

et al. 2021

Transactions of the Institute of Measurement and Control

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This paper addressed a strongly nonlinear problem caused by status mutation in CubeSat attitude estimation system. The multiple fading factors are employed to make different state channels have separate adjustment ability, which enhances the tracking performance for status mutation. The second-order difference transformation is adopted to improve the approximation accuracy of state posterior mean and covariance. Therefore, a multiple fading second-order central difference Kalman filter (MFSCDKF) is formed for CubeSat attitude estimation system in the presence of status mutation. Compared with the single fading factor first-order central difference Kalman filter, the proposed one can improve tracking performance and estimation accuracy simultaneously. Simulation results based on real telemetry data from the on-orbit CubeSat NJUST-1 verify the effectiveness of the proposed MFSCDKF.

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“…To use all available information, Rhudy [2] combined the control inputs with the sensor measurements through Kalman filtering technology. Han et al [3] used central difference Kalman filter (CDKF) based on Stirling interpolation formulation to prevent the defects of the computational complexity and large linearization error caused by extended Kalman filter (EKF). Wind gusts had a great impact on the attitude estimation accuracy of the Small Unmanned Aircraft Systems (SUASs); Weibel et al [4] utilized Global Positioning System (GPS) velocities to estimate attitude and heading reference systems that corrected accelerometer specific-force measurements.…”

Section: Introductionmentioning

confidence: 99%

The Aircraft Pose Estimation Based on a Convolutional Neural Network

Wei

Han

et al. 2019

Mathematical Problems in Engineering

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The pose estimation of the aircraft in the airport plays an important role in preventing collisions and constructing the real-time scene of the airport. However, current airport target surveillance methods regard the aircraft as a point, neglecting the importance of pose estimation. Inspired by human pose estimation, this paper presents an aircraft pose estimation method based on a convolutional neural network through reconstructing the two-dimensional skeleton of an aircraft. Firstly, the key points of an aircraft and the matching relationship are defined to design a 2D skeleton of an aircraft. Secondly, a convolutional neural network is designed to predict all key points and components of the aircraft kept in the confidence maps and the Correlation Fields, respectively. Thirdly, all key points are coarsely matched based on the matching relationship and then refined through the Correlation Fields. Finally, the 2D skeleton of an aircraft is reconstructed. To overcome the lack of benchmark dataset, the airport surveillance video and Autodesk 3ds Max are utilized to build two datasets. Experiment results show that the proposed method get better performance in terms of accuracy and efficiency compared with other related methods.

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Aircraft attitude estimation based on central difference Kalman filter

Cited by 11 publications

References 8 publications

Research on Line of Sight Angle Rate Reconstruction based on Strap down Seeker

Research on Line of Sight Angle Rate Reconstruction based on Strap down Seeker

Strong tracking central difference Kalman filter for CubeSat attitude estimation with status mutation

The Aircraft Pose Estimation Based on a Convolutional Neural Network

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