Robust fading cubature Kalman filter and its application in initial alignment of SINS

Guo, Shiluo; Chang, Limin; Li, Yang; Sun, Yongrong

doi:10.1016/j.ijleo.2019.163593

Cited by 19 publications

(13 citation statements)

References 15 publications

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“…Moreover, the Kalman filter (KF) with its improving filters have been proposed for the data fusion (Liu et al, 2020). 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).…”

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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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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“…D IFF variants of nonlinear state estimators have been developed in the literature; Extended Kalman Filter (EKF) [1], Unscented Kalman filter (UKF) [2] and cubature Kalman filter (CKF) [3] are the most popular methods. In the EKF, the non-linear function is approximated through Taylor or Jacobian calculations [4], whereas in the UKF [5] has been developed based on the sigma points approach. However, UKF has afford considerably accurate estimation than the EKF estimator.…”

Section: Introductionmentioning

confidence: 99%

“…By introducing the time-dependent variable, called fading factor, named it as Adaptive Fading Kalman Filter (AFKF) [18]. Nonlinear fading EKF [1], [19], and adaptive fading AFUKF [20], adaptive fading cubature KF [4], [15]. The ACKF with a single fading factor was developed for estimating the noise covariance matrices.…”

Section: Introductionmentioning

confidence: 99%

A new state estimation approach-Adaptive Fading Cubature Kalman filter

Narasimhappa

2021

Preprint

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This paper presents a novel adaptive fading cubature Kalman filter (AFCKF) based on double transitive factors. The developed adaptive algorithm is explained in two stages; stage (i) a single transitive factor is used to update the predicted state error covariance, Pk − based on innovation or residual vector, whereas, in stage (ii), the measurement noise covariance matrix, R * k is scaled by another transitive factor. Furthermore, showing the proof concept for estimation of the process noise, Q * k and measurement noise covariance matrices by combining the innovation and residual vector in the AFCKF algorithm. It can provide reliable state estimation in the presence of unknown noise statistics. Bench-marking target tracking example is consider to show the performance improvement of the developed algorithms. As compared with existing adaptive approaches, the proposed fading algorithm can provide better estimation results.

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“…In [19], an adaptive UKF with process noise covariance estimation is proposed to enhance the UKF robustness against process noise uncertainty for vehicular INS/GPS integration. In [20], a robust fading CKF was proposed in initial alignment of the strapdown inertial navigation system (SINS). In [21], a square root cubature information filter was proposed for DVL-aided SINS inmotion initial alignment.…”

Section: Introductionmentioning

confidence: 99%

Huber’s M-Estimation-Based Cubature Kalman Filter for an INS/DVL Integrated System

Hou

Yang

et al. 2020

Mathematical Problems in Engineering

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To deal with the problems of outliers and nonlinearity in the complex underwater environment, a Huber’s M-estimation-based cubature Kalman filter (CKF) is proposed for an inertial navigation system (INS)/Doppler velocity log (DVL) integrated system. First, a loosely coupled INS/DVL integrated system is designed, and the nonlinear system model is established in the case of big misalignment angle. Then, Huber’s M-estimation is introduced for robust estimation to resist outliers. Meanwhile, the CKF is focused to handle the nonlinearity of the state equation. Finally, simulation and the vehicle test are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the conventional Kalman filter (KF) and outlier detection-based robust cubature Kalman filter (RCKF) in terms of navigation accuracy in the complex underwater environment.

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Robust fading cubature Kalman filter and its application in initial alignment of SINS

Cited by 19 publications

References 15 publications

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

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

A new state estimation approach-Adaptive Fading Cubature Kalman filter

Huber’s M-Estimation-Based Cubature Kalman Filter for an INS/DVL Integrated System

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