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

Zhao, Yiwei

doi:10.1016/j.sigpro.2015.07.014

Cited by 90 publications

(50 citation statements)

References 39 publications

(47 reference statements)

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“…The cubature points are generated by:

ξ = \sqrt{\frac{m}{2}} {[1]}_{i}, i = 1, \dots, m,

where

m

denotes the number of the cubature points and

m = 2 n

n

denotes the dimension of the state vector, and

[1]

denotes the point

(\begin{array}{l} 1 \\ 0 \end{array})

in this paper. Then, the equations of the cubature Kalman filter are given by reference [14]:…”

Section: The Gps/ins Integrated Navigationmentioning

confidence: 99%

“…On the other hand, the cubature Kalman filter is a recently developed nonlinear filtering algorithm [13]. Compared with the unscented Kalman filter, the cubature Kalman filter shows better performance in stability; therefore, it has been adopted in the GPS/inertial measurement unit (IMU) integrated navigation system [14]. Many forms of robust filtering algorithms were proposed to control the influence of outliers, but the influence of the model errors was neglected.…”

Section: Introductionmentioning

confidence: 99%

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A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

Jiang

Zhang

2016

Sensors

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The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms.

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“…The cubature points are generated by:

ξ = \sqrt{\frac{m}{2}} {[1]}_{i}, i = 1, \dots, m,

where

m

denotes the number of the cubature points and

m = 2 n

n

denotes the dimension of the state vector, and

[1]

denotes the point

(\begin{array}{l} 1 \\ 0 \end{array})

in this paper. Then, the equations of the cubature Kalman filter are given by reference [14]:…”

Section: The Gps/ins Integrated Navigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

Jiang

Zhang

2016

Sensors

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show abstract

“…Compared with UKF, the CKF has been improved in numerical accuracy, unavailability of a square‐root solution, and filter instability . The CKF has been widely applied to navigation, guidance, signal processing, biology and so on. In recent years, many improved CKF algorithms have been proposed for precision and applicability .…”

Section: Introductionmentioning

confidence: 99%

Distributed fusion cubature Kalman filters for nonlinear systems

Hao

Sun

2019

Intl J Robust & Nonlinear

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Summary This paper is concerned with the distributed fusion estimation problem for multisensor nonlinear systems. Based on the Kalman filtering framework and the spherical cubature rule, a general method for calculating the cross‐covariance matrices between any two local estimators is presented for multisensor nonlinear systems. In the linear unbiased minimum variance sense, based on the cross‐covariance matrices, a distributed fusion cubature Kalman filter weighted by matrices (MW‐CKF) is presented. The proposed MW‐CKF has better accuracy and robustness. An example verifies the effectiveness of the proposed algorithms.

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“…According to the cubature Kalman filtering algorithm, 15 we choose 2n points of a volume set (j i , v i ) to deliver the nonlinear function, and we calculate the new volume point. Assume the state at time instance k. The statistical property is N (x k ;x k , P k ).…”

Section: Introductionmentioning

confidence: 99%

An adaptive threshold algorithm for sensor fault based on the grey theory

Yao

Peng

et al. 2017

Advances in Mechanical Engineering

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An appropriate threshold is the key factor in a diagnosis of fault. However, the traditional method of setting a fixed threshold does not take into consideration the influence of system status and noise interference, and it often leads to false alarms and missed detections of system fault. To improve the accuracy of fault diagnosis, we first obtained the residual signal based on the strong tracking filter method -cubature Kalman filtering. We then proposed an adaptive dynamic threshold adjustment algorithm based on the grey theory. In this method, the threshold value can be dynamically adjusted according to the real-time mean and variance of the residual. Finally, we performed a sensor fault experiment involving three sensors in different locations of a robot. The results demonstrate the feasibility of our proposed method.

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

Cited by 90 publications

References 39 publications

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

Distributed fusion cubature Kalman filters for nonlinear systems

An adaptive threshold algorithm for sensor fault based on the grey theory

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