Multi-rate cubature Kalman filter based data fusion method with residual compensation to adapt to sampling rate discrepancy in attitude measurement system

Guo, Xiaoting; Sun, Changku; Wang, Peng

doi:10.1063/1.4997072

Cited by 16 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(6) If the initial estimation R 0 is obtained, the optimal R in equation ( 3) can be computed by the following iterative method: first, let R k express the kth estimation of rotation matrix R, then the kth estimation of the translation matrix can be obtained by equation (6), that is t k = t R k , and…”

Section: Algorithm Designmentioning

confidence: 99%

See 1 more Smart Citation

An orthogonal iteration pose estimation algorithm based on an incident ray tracking model

Sun¹,

Dong²,

Zhang³

et al. 2018

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

Although the pinhole imaging model prevails among current pose estimation algorithms, this idealized model gradually shows its limitation and inaccuracy, especially in harsh environments. In recent years, a few generic imaging models have been proposed to replace the dominant pinhole one. Among them, the camera model, named the incident ray tracking model, has proved to be superior. This model consists of mappings between pixels on the image and straight lines in 3D space. Based on the incident ray tracking camera model, a pose estimation algorithm, which can be called the perspective-ray-based orthogonal iteration (PROI), is proposed in this paper. The proposed algorithm introduces the popular orthogonal iteration algorithm into the incident ray tracking model. In this method, the object space collinearity error is expressed by a more accurate perspective ray instead of the traditional line of sight. The orthogonal rotation matrices are computed by an iterative algorithm and the iterative initial value is given in a weak perspective model. Experiment results on the pose estimation with better accuracy and efficiency by PROI have shown the superiority of the proposed method compared with the best currently employed optimization algorithms.

show abstract

Section: Algorithm Designmentioning

confidence: 99%

“…Pose estimation [1][2][3][4][5][6]12] has widespread applications in augmented reality [7], aircraft docking [8], unmanned air vehicle navigation [9] and robotics control [10]. In augmented reality, the head pose is considered as vital information in humancomputer interaction.…”

Section: Introductionmentioning

confidence: 99%

An orthogonal iteration pose estimation algorithm based on an incident ray tracking model

Sun¹,

Dong²,

Zhang³

et al. 2018

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Among them, the multi-frequency is an important problem in theory and practice. As the sampling frequency of the inertial sensor is high and that of visual sensor is low, an inconsistent sampling frequency always exists in a vision and inertial integrated attitude measurement system [22]. Correspondingly, a degradation of measurement performance is unavoidable in the intersampling of slow vision data.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Fusion Strategy for Reliable Attitude Measurement Utilizing Vision and Inertial Sensors

et al. 2019

View full text Add to dashboard Cite

In this paper, we present a radial basis function (RBF) and cubature Kalman filter (CKF) based enhanced fusion strategy for vision and inertial integrated attitude measurement for sampling frequency discrepancy and divergence. First, the multi-frequency problem of the integrated system and the reason for attitude divergence are analyzed. Second, the filter equation and attitude differential equation are constructed to calculate attitudes separately in time series when visual and inertial data are available or when there are only inertial data. Third, attitude errors between inertial and vision are sent to the input layer of RBF for training. After this, through the activation function of the hidden layer, the errors are transferred to the output layer for weighting the sums, and the training model is established. To overcome the problem of divergence inherent in a multi-frequency system, the well-trained RBF, which can output the attitude errors, is utilized to compensate the attitudes calculated by pure inertial data. Finally, semi-physical simulation experiments under different scenarios are performed to validate the effectiveness and superiority of the proposed scheme in accurate attitude measurements and enhanced anti-divergence capability.

show abstract

“…Recently, the micro-electromechanical system (MEMS) gyroscope is widely applied in the areas of navigation, control and tracking due to its advantages of small size and light weight (Sang et al, 2015;Zhang et al, 2015). However, bias and noise contained in the gyroscope signal (Kirkko-Jaakkola et al, 2012;El-Sheimy et al, 2008;Guo et al, 2017Guo et al, , 2018 can result in significant drift. Correcting bias and suppressing noise are essential to enhance precision and performance of MEMS gyroscope.…”

Section: Introductionmentioning

confidence: 99%

A hybrid method for MEMS gyroscope signal error compensation

Guo

Sun

Wang

et al. 2018

Self Cite

View full text Add to dashboard Cite

Purpose This paper aims to propose a hybrid method based on polynomial fitting bias self-compensation, grey forward-backward linear prediction (GFBLP) and moving average filter (MAF) for error compensation in micro-electromechanical system gyroscope signal especially under motion state. Design/methodology/approach The error compensation can be divided into two processes: bias correction and noise reduction. A polynomial drift angle fitting algorithm is used to correct bias before denoising processing. For noise reduction, operation can be taken in two stages: detection and processing. First, sample variances are used to judge motion state. According to the detection results, algorithmic system switches between grey GFBLP and MAF to ensure fast convergence rate and small steady-state error. Findings Experimental results show that the proposed method can correct bias effectively for practical gyroscope signal, and can eliminate noise effectively for both practical gyroscope signal and synthetic signal, which indicates the effectiveness of the proposed method. Originality/value Bias correction and noise reduction are considerations. Noise contained in practical or synthetic signal can be reduced rapidly and effectively, which benefits from the new idea of combination grey GFBLP, MAF and sample variances. And most importantly, it is applicable for signal denoising under arbitrary motion state condition, which is different from other methods where the convergence performance is seldom analyzed.

show abstract

Multi-rate cubature Kalman filter based data fusion method with residual compensation to adapt to sampling rate discrepancy in attitude measurement system

Cited by 16 publications

References 28 publications

An orthogonal iteration pose estimation algorithm based on an incident ray tracking model

An orthogonal iteration pose estimation algorithm based on an incident ray tracking model

An Enhanced Fusion Strategy for Reliable Attitude Measurement Utilizing Vision and Inertial Sensors

A hybrid method for MEMS gyroscope signal error compensation

Contact Info

Product

Resources

About