Quadrotor UAV state estimation based on High-Degree Cubature Kalman filter

Benzerrouk, Hamza; Nebylov, Alexander; Salhi, Hassen

doi:10.1016/j.ifacol.2016.09.060

Cited by 18 publications

(13 citation statements)

References 15 publications

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“…As the manner to fulfill the objective to estimate the robot path and static map, the estimation based on the probability principle has been frequently proposed, such as Extended Kalman Filter (EKF) [5]- [9]. The use of EKF has been limited because of the consistency issue [10].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Development of SVSF for a Feature-Based Slam Algorithm Using Maximum Likelihood Estimation and Expectation Maximization

Suwoyo

Tian

Wang

et al. 2021

IIUMEJ

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The smooth variable structure filter (SVSF) has been considered as the robust estimator. Like other filters, the SVSF needs an accurate system model and known noise statistics to approximate the posterior state. Unfortunately, the system cannot be accurately modeled, and the noise statistic is unknown in the real application. For these reasons, the performance of SVSF might be decreased or even led to divergence. Therefore, the enhancement of SVSF is required. This paper presents an Adaptive SVSF. Initially, SVSF is smoothed. To provide the ability to estimate the noise statistic, ASVSF is then derived based on maximum likelihood estimation (MLE) and expectation-maximization (EM). Additionally, the unbiased noise statistic is also approached. However, its covariance is complicatedly formulated. It might cause a negative definite symmetric matrix. Therefore, it is tuned based on the innovation covariance estimator (ICE). The ASVSF is designed to solve the online problem of Simultaneous Localization and Mapping (SLAM). Henceforth, it is termed as the ASVSF-SLAM algorithm. The proposed algorithm showed better accuracy and stability compared to the conventional algorithm in terms of root mean square error (RMSE) for both Estimated Path Coordinate (EPC) and Estimated Map Coordinate (EMC). ABSTRAK: Penapis struktur bolehubah lembut (SVSF) telah dianggap sebagai penganggar teguh. Seperti penapis lain, SVSF memerlukan model sistem yang tepat dan statistik hingar yang diketahui bagi menganggar keadaan posterior. Malangnya, sistem tidak dapat dimodelkan dengan tepat dan statistik hingar tidak diketahui dalam aplikasi sebenar. Atas sebab-sebab ini, prestasi SVSF mungkin berkurangan, bahkan berbeza. Oleh itu, memperbaharui SVSF adalah perlu. Kajian ini adalah mengenai SVSF Mudah Suai. Pada awalnya, SVSF dilembutkan. Bagi menyediakan keupayaan anggaran statistik hinggar, ASVSF dihasilkan terlebih dahulu berdasarkan anggaran kemungkinan maksimum (MLE) dan maksimum-harapan (EM). Tambahan, statistik hinggar yang tidak berat sebelah juga dibuat. Walau bagaimanapun, rumusan formula kovarians ini adalah kompleks. Ini mungkin menyebabkan matriks simetri menjadi negatif. Oleh itu, ia diselaraskan berdasarkan penganggar kovarians inovasi (ICE). ASVSF dibina bagi menyelesaikan masalah dalam talian Penempatan dan Pemetaan Serentak (SLAM) dalam talian. Oleh itu, ia disebut sebagai algoritma ASVSF-SLAM. Algoritma yang dicadangkan ini menunjukkan ketepatan dan kestabilan yang lebih baik berbanding algoritma konvensional dari segi ralat punca min kuasa dua (RMSE) bagi kedua-dua Koordinat Anggaran Laluan (EPC) dan Anggaran Koordinat Peta (EMC).

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Section: Introductionmentioning

confidence: 99%

“…For this reason, the Smooth Variable Structure Filter (SVSF) [10]- [13] is often chosen as the alternative filtering method of EKF. The SVSF is relatively considered a new predictorestimator that adopts the sliding mode [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Development of SVSF for a Feature-Based Slam Algorithm Using Maximum Likelihood Estimation and Expectation Maximization

Suwoyo

Tian

Wang

et al. 2021

IIUMEJ

View full text Add to dashboard Cite

show abstract

“…For example, the fading cubature Kalman filter (CKF) was designed to the initial alignment of strapdown inertial navigation system (SINS) (Guo et al, 2020). The quadrotor state estimation based on CKF was proposed (Benzerrouk, Nebylov & Salhi, 2016). An improving CKF method was investigated for the the attitude determination system of missile .…”

Section: Introductionmentioning

confidence: 99%

Neural network assisted Kalman filter for INS/UWB integrated seamless quadrotor localization

Shen

et al. 2021

PeerJ Computer Science

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Due to some harsh indoor environments, the signal of the ultra wide band (UWB) may be lost, which makes the data fusion filter can not work. For overcoming this problem, the neural network (NN) assisted Kalman filter (KF) for fusing the UWB and the inertial navigation system (INS) data seamlessly is present in this work. In this approach, when the UWB data is available, both the UWB and the INS are able to provide the position information of the quadrotor, and thus, the KF is used to provide the localization information by the fusion of position difference between the INS and the UWB, meanwhile, the KF can provide the estimation of the INS position error, which is able to assist the NN to build the mapping between the state vector and the measurement vector off-line. The NN can estimate the KF’s measurement when the UWB data is unavailable. For confirming the effectiveness of the proposed method, one real test has been done. The test’s results demonstrate that the proposed NN assisted KF is effective to the fusion of INS and UWB data seamlessly, which shows obvious improvement of localization accuracy. Compared with the LS-SVM assisted KF, the proposed NN assisted KF is able to reduce the localization error by about 54.34%.

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“…Because the CKF does not require the calculation of the Jacobian and Hessian matrices, the filter is easy to create, it quickly computes estimations with low complexity, and it particularly satisfies the system requirements for fast state estimation in real time. In [18], Benzerrouk et al introduced a highdegree CKF based on spherical-radial cubature rules and applied the filter in a navigation problem for an unmanned aerial vehicle (UAV). The filter was executed and verified on an AR.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Enhanced Robust Cubature Kalman Filter for the State Estimation of an Unmanned Autonomous Helicopter

2019

IEEE Access

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This paper addresses a design and application for the problem of state estimation for an unmanned autonomous helicopter (UAH) equipped with instruments including an inertial measurement unit (IMU), a magnetometer and a global positioning system (GPS). A dynamic enhanced robust cubature Kalman filter (DERCKF) is proposed in this article. First, a robust filtering strategy is formulated to provide a strong constraint for abnormal values. Second, a new robust CKF is formulated based on the spherical cubature and Gaussian quadrature rules to estimate the probability state, without requiring calculation of the Jacobian and Hessian matrices. Then, an enhanced rule is proposed to help eliminate the accuracy degradation caused by model uncertainty disturbance when the experimental platform is operating and to improve the estimation performance of the filter. Meanwhile, by detecting the system uncertainty state, a dynamic enhanced strategy is formulated to achieve automatic adjustments for the dynamic enhanced robust rule and guarantee that the DERCKF will realize valid system state estimation at all times. Finally, numerical experimental results are presented to demonstrate the effectiveness and robustness of the DERCKF.

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Quadrotor UAV state estimation based on High-Degree Cubature Kalman filter

Cited by 18 publications

References 15 publications

Adaptive Development of SVSF for a Feature-Based Slam Algorithm Using Maximum Likelihood Estimation and Expectation Maximization

Adaptive Development of SVSF for a Feature-Based Slam Algorithm Using Maximum Likelihood Estimation and Expectation Maximization

Neural network assisted Kalman filter for INS/UWB integrated seamless quadrotor localization

A Dynamic Enhanced Robust Cubature Kalman Filter for the State Estimation of an Unmanned Autonomous Helicopter

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