Novel Adaptive Fuzzy Extended Kalman Filter for Attitude Estimation in Gps-Denied Environment

Assad, Ammar; Khalaf, Wassim; Chouaib, Ibrahim

doi:10.1134/s2075108719030027

Cited by 9 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, a comparison of the GRNN-based neural filter and KF for target movement vector estimation was presented in [48,49], where the GRNN-based approach was characterized by superior estimation performance only during steady motions. In [50], a fuzzy inference system was proposed to tune the noise covariance matrix of the EKF based on the filter innovation sequence through a covariance-matching technique. The experimental results showed that the fuzzy rule-based adaptive strategy effectively improved the estimation accuracy with respect to the standard EKF algorithm.…”

Section: Survey On Attitude Estimationmentioning

confidence: 99%

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Odry

Kecskés

Šarčević

et al. 2020

Sensors

View full text Add to dashboard Cite

This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.The MEMS inertial measurement unit (IMU), composed of tri-axis MEMS accelerometer, gyroscope, and magnetometer sensors, also known as the measurement system of magnetic, angular rate, and gravity (MARG) sensor arrays, is the most commonly utilized device to track the real-time orientation of mobile platforms at present. The low-cost, low power consumption, and small size characteristics meet technological requirements, and therefore these devices have been widely utilized in embedded systems, where the filtering algorithm is executed by a microprocessor. As a result, an attitude and heading reference system (AHRS) has been formed, which provides the complete orientation measurement relative to the Earth's gravitational and magnetic fields (global reference system), where the attitude denotes the roll and pitch angles, whereas heading refers to the yaw Euler angle [19]. The role of the aforementioned filtering algorithm is to combine the individual features of each sensor and provide both properly smoothed and robust attitude results with regard to the global reference system, in either Euler angles or quaternions. The most common method applied in sensor fusion techniques synthesizes the short-term accuracy of gyroscope-based attitude realizations and the accelerometer and magnetometer provide rough, low-frequency attitude corrections. This technique cancels the accumulated error (drift), smooths the signals, and produces long-term stable outputs if the IMU is in stationary states. Significant decrease in estimation performance arises when...

show abstract

Section: Survey On Attitude Estimationmentioning

confidence: 99%

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Odry

Kecskés

Šarčević

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…In this section, the proposed AHEKF is developed by using an adaptive scale factor ( σ k ) in error covariance matrix ( P k | k ) to make a tradeoff between robustness and accuracy of the filter, which might not applicable when using directly adaptive γ . To evaluate the system errors, a residual vector ( V k ) is defined as (Assad et al, 2019; Yang et al, 2001)…”

Section: The Adaptive H-infinity Extended Kalman Filtermentioning

confidence: 99%

“…Although the multiple-model-based adaptive estimation (MMAE) can somehow overcome the model uncertainty, due to implementing multiple banks of KFs, the computational burden would impact the system performance (Kottath et al, 2017). The adaptive fuzzy algorithm is proposed in Assad et al (2019), Yazdkhasti and Sasiadek (2018), and Yazdkhasti et al (2016), which is based on EKF, weighted EKF, and unscented Kalman filter (UKF), improving the estimation accuracy by adaption of the process ( Q k ) and measurement ( R k ) covariance matrices. Despite the advantages of the H-infinity filters such as robustness and independence of initial noise knowledge, for the optimal results, more parameters need to be tuned, and high accuracy of the filter cannot obtain in some cases (Wang et al, 2017; Yu et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Adaptive H-infinity extended Kalman filtering for a navigation system in presence of high uncertainties

Yazdkhasti

Sabzevari

Sąsiadek

2022

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

The optimal performance of the Kalman filters is highly dependent on the measurement and process noise characteristics, making the whole system unable to achieve the desired estimation in the presence of non-Gaussian mean noise distribution and high initial uncertainties. Recently, the H-infinity filter, as a robust algorithm, has been broadly used, as it is not being dependent on the pre-knowledge of the noise nature; however, making a balance between high robustness and estimation accuracy is a challenging issue. Hence, to overcome this problem, a new adaptive H-infinity extended Kalman filter (AHEKF) was designed in this paper, which benefits from both high robustness and precision. The suggested algorithm contains two adaptive sections to achieve high accuracy as well as controlling the effects of time-varying noise characteristics, high initial uncertainties, and abnormal data that can degrade the accuracy of state estimation in an integrated navigation system. The presented algorithm was used to integrate data from two independent sensors data. The simulation results for an inertial navigation system (INS)/global positioning system (GPS) sensor fusion are presented and compared with the standard H-infinity filter, extended Kalman filter (EKF), and unscented Kalman filter (UKF) to show the effectiveness of the proposed algorithm. Evaluations demonstrate that the AHEKF achieves over 50% higher accuracy and robustness, and over 2.5 times faster convergence of estimation errors than the standard H-infinity filter.

show abstract

“…The parameter α ∈ [0 1] determines the weighing factor for gyroscope and accelerometer/magnetometer estimates. The LCF estimate, in terms of transfer function, can be represented as in Equation (9).…”

Section: -α Hpfmentioning

confidence: 99%

“…A nonlinear version of KF, i.e., EKF, is a widely adopted attitude estimation technique and is still popular amongst researchers [5,6]. Active research is ongoing to incorporate techniques like machine learning [7], statistical methods [8] and fuzzy logic [9,10] to improve the estimation accuracy of KF. However, KF requires the system's mathematical model to be known correctly and is dependent on the system noise parameters.…”

Section: Introductionmentioning

confidence: 99%

Cascaded Complementary Filter Architecture for Sensor Fusion in Attitude Estimation

Narkhede

Poddar

Walambe

et al. 2021

Sensors

View full text Add to dashboard Cite

Attitude estimation is the process of computing the orientation angles of an object with respect to a fixed frame of reference. Gyroscope, accelerometer, and magnetometer are some of the fundamental sensors used in attitude estimation. The orientation angles computed from these sensors are combined using the sensor fusion methodologies to obtain accurate estimates. The complementary filter is one of the widely adopted techniques whose performance is highly dependent on the appropriate selection of its gain parameters. This paper presents a novel cascaded architecture of the complementary filter that employs a nonlinear and linear version of the complementary filter within one framework. The nonlinear version is used to correct the gyroscope bias, while the linear version estimates the attitude angle. The significant advantage of the proposed architecture is its independence of the filter parameters, thereby avoiding tuning the filter’s gain parameters. The proposed architecture does not require any mathematical modeling of the system and is computationally inexpensive. The proposed methodology is applied to the real-world datasets, and the estimation results were found to be promising compared to the other state-of-the-art algorithms.

show abstract

Novel Adaptive Fuzzy Extended Kalman Filter for Attitude Estimation in Gps-Denied Environment

Cited by 9 publications

References 15 publications

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Adaptive H-infinity extended Kalman filtering for a navigation system in presence of high uncertainties

Cascaded Complementary Filter Architecture for Sensor Fusion in Attitude Estimation

Contact Info

Product

Resources

About