Fuzzy adaptive integration scheme for low-cost SINS/GPS navigation system

Nourmohammadi, Hossein; Keighobadi, Jafar

doi:10.1016/j.ymssp.2017.06.030

Cited by 83 publications

(32 citation statements)

References 17 publications

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“…The microelectromechanical systems-based (MEMS-based) relative localization problem is a recent topic, which has been widely investigated in many areas including robotics and control [1][2][3][4][5][6][7][8], healthcare and rehabilitation [9][10][11], consumer electronics mobile devices [12][13][14], and automated driving and navigation [15][16][17][18], both in industry and in scientific research. Independent from the application, accurate and robust attitude estimation is a crucial task to be solved, especially if the results are to be incorporated into unstable closed-loop systems, such as the control algorithms of mobile robots and unmanned aerial vehicles (UAVs) [1].…”

Section: Survey On Attitude Estimationmentioning

confidence: 99%

“…As the sampling time in the ROS-based framework was relatively low (T s = 1ms), the adaptive strategy could be executed with bigger window size of L = 400; moreover, the length of the transform was L FFT = 2 9 and the threshold oscillation frequency and amplitude were f thr = 10 Hz and |Ω| thr = 0.26 rad/s, respectively. The process noises µ q k and ν k in Equation (16) were considered to be statistically independent [1,19,41]; therefore, diagonal matrices were applied for both the process and measurement noise covariances with the following characteristics,…”

Section: Tuning Of Filter Parametersmentioning

confidence: 99%

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

Odry

Kecskés

Šarčević

et al. 2020

Sensors

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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...

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Section: Survey On Attitude Estimationmentioning

confidence: 99%

Section: Tuning Of Filter Parametersmentioning

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

“…The relationship between the two coordinate systems is shown in Figure 2. The mapping between them can be expressed as a rotation matrix [7]…”

Section: Selection and Establishment Of Coordinate Systemsmentioning

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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“…In [36]- [39] is seen the use of adaptive networks to perform the Multisensor Data Fusion. Additionally, [41,42] apply Fuzzy Inference Systems to perform the fusion. Finally, it is seen in [43] the use of ANFIS as a sub-remedy system to temporarily replace the GPS positioning estimation used as input to a Kalman filter during GPS outages.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Adaptive Computational Intelligence-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

Paulino

Guimarães

Shiguemori

2019

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Nowadays, there is a remarkable world trend in employing UAVs and drones for diverse applications. The main reasons are that they may cost fractions of manned aircraft and avoid the exposure of human lives to risks. Nevertheless, they depend on positioning systems that may be vulnerable. Therefore, it is necessary to ensure that these systems are as accurate as possible, aiming to improve navigation. In pursuit of this end, conventional Data Fusion techniques can be employed. However, its computational cost may be prohibitive due to the low payload of some UAVs. This paper proposes a low-cost Multisensor Data Fusion application based on Hybrid Adaptive Computational Intelligence (HACI) -the cascaded use of Fuzzy C-Means Clustering (FCM) and Adaptive-Network-Based Fuzzy Inference System (ANFIS) algorithms -that have been shown able to improve considerably the accuracy of current positioning estimation systems for real-time UAV autonomous navigation, reducing the error in approximately 300cm 2 . In addition, the proposed methodology outperformed two other Computational Intelligence methodologies -Artificial Neural Networks and Regression Models, with 18 different tested approaches -in estimating an UAV position considering the Root-Mean-Square Error against the real trajectory. The generated Fuzzy Inference System has proved to be effective in providing an improved positioning estimation with a low computational burden, about 600 times faster than the fastest embedded sensor refresh rate.

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Fuzzy adaptive integration scheme for low-cost SINS/GPS navigation system

Cited by 83 publications

References 17 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

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

Hybrid Adaptive Computational Intelligence-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

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