Unscented Kalman filter and smoothing applied to attitude estimation of artificial satellites

Garcia, Roberta Veloso; Kuga, Helio Koiti; Silva, William Reis; Zanardi, Maria Cecília

doi:10.1007/s40314-018-0576-8

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…Although the EKF model is linearized by first-order Taylor series expansion of the process/measurement models for the current state estimate, it is difficult to obtain the ideal estimation, when the system has strong nonlinearity or the initial estimation error is large. In the Gaussian case, sigma point approaches used in an unscented Kalman filter (UKF) [28] and cubature Kalman filter (CKF) [29] have appeared. Compared with EKF, UKF and CKF provide estimates with higher accuracy and avoid the calculation of the Jacobian matrix.…”

Section: Introductionmentioning

confidence: 99%

Attitude and Heading Estimation for Indoor Positioning Based on the Adaptive Cubature Kalman Filter

Geng

Xia

2021

Micromachines

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The demands for indoor positioning in location-based services (LBS) and applications grow rapidly. It is beneficial for indoor positioning to combine attitude and heading information. Accurate attitude and heading estimation based on magnetic, angular rate, and gravity (MARG) sensors of micro-electro-mechanical systems (MEMS) has received increasing attention due to its high availability and independence. This paper proposes a quaternion-based adaptive cubature Kalman filter (ACKF) algorithm to estimate the attitude and heading based on smart phone-embedded MARG sensors. In this algorithm, the fading memory weighted method and the limited memory weighted method are used to adaptively correct the statistical characteristics of the nonlinear system and reduce the estimation bias of the filter. The latest step data is used as the memory window data of the limited memory weighted method. Moreover, for restraining the divergence, the filter innovation sequence is used to rectify the noise covariance measurements and system. Besides, an adaptive factor based on prediction residual construction is used to overcome the filter model error and the influence of abnormal disturbance. In the static test, compared with the Sage-Husa cubature Kalman filter (SHCKF), cubature Kalman filter (CKF), and extended Kalman filter (EKF), the mean absolute errors (MAE) of the heading pitch and roll calculated by the proposed algorithm decreased by 4–18%, 14–29%, and 61–77% respectively. In the dynamic test, compared with the above three filters, the MAE of the heading reduced by 1–8%, 2–18%, and 2–21%, and the mean of location errors decreased by 9–22%, 19–31%, and 32–54% respectively by using the proposed algorithm for three participants. Generally, the proposed algorithm can effectively improve the accuracy of heading. Moreover, it can also improve the accuracy of attitude under quasistatic conditions.

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Section: Introductionmentioning

confidence: 99%

Attitude and Heading Estimation for Indoor Positioning Based on the Adaptive Cubature Kalman Filter

Geng

Xia

2021

Micromachines

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“…However, when the noise is colored noise or the system is uncertain, the results of Kalman filtering are suboptimal and unstable. To solve the nonlinear problem, extended Kalman filter (EKF) [11], unscented Kalman filter (UKF) [12] and cubature Kalman filter (CKF) [13,14] are proposed. Compared with EKF, CKF can avoid linearization of the nonlinear system by using cubature point sets to approximate the mean and variance [15].…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning

Geng

Xia

et al. 2021

Sensors

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Indoor localization based on pedestrian dead reckoning (PDR) is drawing more and more attention of researchers in location-based services (LBS). The demand for indoor localization has grown rapidly using a smartphone. This paper proposes a 3D indoor positioning method based on the micro-electro-mechanical systems (MEMS) sensors of the smartphone. A quaternion-based robust adaptive cubature Kalman filter (RACKF) algorithm is proposed to estimate the heading of pedestrians based on magnetic, angular rate, and gravity (MARG) sensors. Then, the pedestrian behavior patterns are distinguished by detecting the changes of pitch angle, total accelerometer and barometer values of the smartphone in the duration of effective step frequency. According to the geometric information of the building stairs, the step length of pedestrians and the height difference of each step can be obtained when pedestrians go up and downstairs. Combined with the differential barometric altimetry method, the optimal height can be computed by the robust adaptive Kalman filter (RAKF) algorithm. Moreover, the heading and step length of each step are optimized by the Kalman filter to reduce positioning error. In addition, based on the indoor map vector information, this paper proposes a heading calculation strategy of the 16-wind rose map to improve the pedestrian positioning accuracy and reduce the accumulation error. Pedestrian plane coordinates can be solved based on the Pedestrian Dead-Reckoning (PDR). Finally, combining pedestrian plane coordinates and height, the three-dimensional positioning coordinates of indoor pedestrians are obtained. The proposed algorithm is verified by actual measurement examples. The experimental verification was carried out in a multi-story indoor environment. The results show that the Root Mean Squared Error (RMSE) of location errors is 1.04–1.65 m by using the proposed algorithm for three participants. Furthermore, the RMSE of height estimation errors is 0.17–0.27 m for three participants, which meets the demand of personal intelligent user terminal for location service. Moreover, the height parameter enables users to perceive the floor information.

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“…UKF operates on the premise that with a fixed number of parameters, it is easier to approximate a Gaussian distribution than to approximate an arbitrary nonlinear function [10]. UKF can provide higher estimation accuracy, faster convergence, and higher computation speed than EKF and it is robust to large initial estimation errors [11]- [14].…”

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confidence: 99%

A Factor Graph Approach for Attitude Estimation of Spacecraft Using a Stellar Gyroscope

et al. 2019

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A stellar gyroscope is an attitude sensor that is composed of a micro star sensor and a microelectro-mechanical system gyroscope. The attitude is the output by fusing the information of the two sensors. Due to the processing of the star image from the star sensor, a measurement delay is introduced and can affect the performance of the fusion of the two sensors. In this paper, a factor graph method that is based on a sliding window is investigated for dealing with the measurement delay. Temporary factors are calculated to temporarily occupy the places of the factors that are under processing, and sliding window incremental smoothing is only performed with a set of factors and states after the delayed measurement has arrived. To reduce the frequency of attitude calculation based on a star image, the keyframe strategy is adopted. Only in keyframes, the attitude is solved by using the star image and used as the measurement of the factor. In nonkeyframes, only the star positions in the star images are determined, with a robust star tracking method that is aided by the gyroscope. Moreover, a high-rate low-latency output framework that is by a gyroscope is proposed for accelerating the attitude output rate. According to the results of the experiments on simulated data, the proposed method can overcome the problem of measurement delay and is more effective than the compared methods. According to the results of the experiments on real data, the proposed method can be used in applications. INDEX TERMS Attitude estimation, factor graph, temporary factor, sliding window incremental smoothing. I. INTRODUCTION With an ever-widening range of space missions becoming feasible, attitude determination performance is critical for many spacecraft. Since a small spacecraft may have a limited payload, only compact, low-power and miniature sensors can be used on satellites. Using new sensor technologies, such as micro-electro mechanical system(MEMS) gyroscopes and active pixel sensor (APS) imaging devices, suitable attitude sensors, namely, stellar gyroscope, has been developed by many institutes, such as the Charles Stark Draper Laboratory, University of Kentucky and StarVision Technologies [1]-[3]. A stellar gyroscope is composed of a MEMS gyroscope and a micro star sensor and is small, lightweight and inexpensive [4]. Under typical operating conditions, a star sensor can output attitude information accurately with a low output rate. The output rate of the gyroscope is high; however, the angular velocity suffers due to long-term stability, namely, The associate editor coordinating the review of this manuscript and approving it for publication was Zheng Hong Zhu.

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Unscented Kalman filter and smoothing applied to attitude estimation of artificial satellites

Cited by 11 publications

References 6 publications

Attitude and Heading Estimation for Indoor Positioning Based on the Adaptive Cubature Kalman Filter

Attitude and Heading Estimation for Indoor Positioning Based on the Adaptive Cubature Kalman Filter

Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning

A Factor Graph Approach for Attitude Estimation of Spacecraft Using a Stellar Gyroscope

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