Cubature Kalman Filter based Localization and Mapping

Chandra, Kumar Pakki Bharani; Gu, Da‐Wei; Postlethwaite, Ian

doi:10.3182/20110828-6-it-1002.03104

Cited by 33 publications

(20 citation statements)

References 6 publications

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“…The CKF is a second order approximation to a nonlinear system and has been widely applied in many fields [30,31]. According to the CKF working principle, the larger the dimensionality of the state vector is, the more Cubature points are required to propagate states and covariance matrix [8][9][10].…”

Section: Ckf+ekf Hybrid Filtering Methodsmentioning

confidence: 99%

Cubature + Extended Hybrid Kalman Filtering Method and Its Application in PPP/IMU Tightly Coupled Navigation Systems

Zhao

2015

IEEE Sensors J.

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Implementing the GPS (Global Positioning System) total carrier phase observations based on the PPP (Precise Point Positioning) technique in a navigation Kalman filter can improve the position accuracy of a GPS/IMU (Inertial Measurement Unit) tightly-coupled navigation system to the sub-meter level. However, the carrier phase implementation introduces extra states like ambiguities, to the Kalman filter state vector, which increases the computational burden especially when nonlinear filtering methods are applied. In this paper, in order to reduce the computational burden of the PPP/IMU tightly-coupled navigation system, a CKF (Cubature Kalman Filter)+EKF (Extended Kalman Filter) hybrid filtering method by applying a linear filtering method to estimate the linear states mainly GPS related states, while a nonlinear filtering method to estimate the nonlinear states like IMU related states, is proposed. The hybrid filtering method can make a balance between keeping the CKF benefits in dealing with nonlinear problems and reducing the computational time. The simulation and experiment results show the effectiveness of the method.Index Terms-Cubature Kalman filter, Extended Kalman filter, PPP, IMU, Hybrid filtering method 1530-437X (c)

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Section: Ckf+ekf Hybrid Filtering Methodsmentioning

confidence: 99%

Cubature + Extended Hybrid Kalman Filtering Method and Its Application in PPP/IMU Tightly Coupled Navigation Systems

Zhao

2015

IEEE Sensors J.

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“…In [22,23], a Cubature H1 filter and its square-root version are proposed and verified in a continuous stirred tank reactor and a permanent magnet synchronous motor as examples. The CKF can also be applied to the SLAM (Simultaneous Localisation And Mapping) problem as reported in [24].…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of Cubature Kalman filter in a GPS/IMU tightly-coupled navigation system

Zhao

2016

Signal Processing

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“…This will make the covariance matrix of a high dimensional system nonpositive, thus leading to deteriorated or diverged filtering solutions. For strongly nonlinear and Gaussian systems, CKF also outperforms UKF in terms of accuracy and stability [26,27].…”

Section: Introductionmentioning

confidence: 99%

Randomly Weighted CKF for Multisensor Integrated Systems

et al. 2019

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The cubature Kalman filter (CKF) is an estimation method for nonlinear Gaussian systems. However, its filtering solution is affected by system error, leading to biased or diverged system state estimation. This paper proposes a randomly weighted CKF (RWCKF) to handle the CKF limitation. This method incorporates random weights in CKF to restrain system error’s influence on system state estimation by dynamic modification of cubature point weights. Randomly weighted theories are established to estimate predicted system state and system measurement as well as their covariances. Simulation and experimental results as well as comparison analyses demonstrate the presented RWCKF conquers the CKF problem, leading to enhanced accuracy for system state estimation.

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Cubature Kalman Filter based Localization and Mapping

Cited by 33 publications

References 6 publications

Cubature + Extended Hybrid Kalman Filtering Method and Its Application in PPP/IMU Tightly Coupled Navigation Systems

Cubature + Extended Hybrid Kalman Filtering Method and Its Application in PPP/IMU Tightly Coupled Navigation Systems

Performance evaluation of Cubature Kalman filter in a GPS/IMU tightly-coupled navigation system

Randomly Weighted CKF for Multisensor Integrated Systems

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