Multiple Model Adaptive Complementary Filter for Attitude Estimation

Kottath, Rahul; Narkhede, Parag; Kumar, V. K.; Karar, Vinod; Poddar, Shashi

doi:10.1016/j.ast.2017.07.011

Cited by 51 publications

(24 citation statements)

References 29 publications

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“…The results also show that the accuracy of EKF is better than that of Madgwick’s CF. In addition, the similar conclusions are drawn by Kottath et al [ 12 ] too. Therefore, regarding estimation accuracy, KFs are considered as the better choices other than conventional CFs.…”

Section: Introductionsupporting

confidence: 89%

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Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering

Xia

Geng

2018

Sensors

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Pedestrian dead reckoning (PDR) using smart phone-embedded micro-electro-mechanical system (MEMS) sensors plays a key role in ubiquitous localization indoors and outdoors. However, as a relative localization method, it suffers from the problem of error accumulation which prevents it from long term independent running. Heading estimation error is one of the main location error sources, and therefore, in order to improve the location tracking performance of the PDR method in complex environments, an approach based on robust adaptive Kalman filtering (RAKF) for estimating accurate headings is proposed. In our approach, outputs from gyroscope, accelerometer, and magnetometer sensors are fused using the solution of Kalman filtering (KF) that the heading measurements derived from accelerations and magnetic field data are used to correct the states integrated from angular rates. In order to identify and control measurement outliers, a maximum likelihood-type estimator (M-estimator)-based model is used. Moreover, an adaptive factor is applied to resist the negative effects of state model disturbances. Extensive experiments under static and dynamic conditions were conducted in indoor environments. The experimental results demonstrate the proposed approach provides more accurate heading estimates and supports more robust and dynamic adaptive location tracking, compared with methods based on conventional KF.

show abstract

Section: Introductionsupporting

confidence: 89%

“…Regarding the steady holding modes, the device attitude heading should be estimated first by fusing the aforementioned sensor data. The fusion is always by means of complementary filters (CFs) [ 10 , 11 , 12 ] and Kalman filters (KFs) [ 13 , 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering

Xia

Geng

2018

Sensors

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show abstract

“…By contrast, the quaternion method boasts a small amount of calculation and no singularity, and thus satisfies the requirements of real-time full attitude solution. Therefore, this paper adopts the quaternion method to solve the attitude angles of the human body [8]. Quaternion, consisting of four parts, is a mathematical tool to describe the rotation of a rigid body:…”

Section: Procedures and Principle Of Quaternion-based Solution To Humamentioning

confidence: 99%

A Human Attitude Solving Algorithm Based on Fuzzy Proportional-Integral-Derivative Controller and Complementary Filter

Wang¹,

Yang²,

Zhang³

et al. 2019

I2M

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The traditional solving algorithms for human attitude face problems like poor stability and low accuracy. To overcome these problems, this paper puts forward a novel human attitude solving algorithm based on fuzzy proportional-integral-derivative (PID) controller and complementary filter, which integrates the data collected by accelerometer, magnetometer and gyro. Through complementary filtering, the error of the gyro was corrected with the aid of accelerometer and magnetometer. Based on the traditional proportional-integral (PI) controller, the fuzzy control was introduced to adjust the parameters in real time, and the differential control (D) was added to improve the dynamic performance of the system, creating a fuzzy PID controller. Then, the fuzzy PID controller was adopted to control the complementary filtering, and the quaternion updating method was employed to compute the human attitude. To verify its effectiveness, our algorithm was compared with the traditional PI filtering algorithm through static and dynamic experiments, using an MPU9150 nine-axis motion tracking device and the MATLAB on the upper computer. The experimental results show that our algorithm achieved stable and accurate output of attitude angles. The research findings are of great application potential in rehabilitation therapy, virtual reality (VR) and human-computer interaction (HCI).

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“…Complementary filter (CF) approaches are popular alternatives for low-cost platforms with need of attitude estimation [29], [30], [31], [32]. Associated thoughts have been proposed for many years [33].…”

Section: Introductionmentioning

confidence: 99%

Generalized Linear Quaternion Complementary Filter for Attitude Estimation From Multisensor Observations: An Optimization Approach

Zhou

Fourati

et al. 2019

IEEE Trans. Automat. Sci. Eng.

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Multiple Model Adaptive Complementary Filter for Attitude Estimation

Cited by 51 publications

References 29 publications

Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering

Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering

A Human Attitude Solving Algorithm Based on Fuzzy Proportional-Integral-Derivative Controller and Complementary Filter

Generalized Linear Quaternion Complementary Filter for Attitude Estimation From Multisensor Observations: An Optimization Approach

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