INS Error Estimation Based on an ANFIS and Its Application in Complex and Covert Surroundings

Duan, Yabo; Li, Huaizhan; Wu, Suqin; Zhang, Kefei

doi:10.3390/ijgi10060388

Cited by 10 publications

(8 citation statements)

References 51 publications

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“…Equations (10) and (11), show the values of the R and Q covariance matrices for tuning the UKF, highlighting, those that delivered the best correction response of the sensor data. The final response of the UKF is obtained and shown in Figure 9, where the correction made by the Kalman filter is observed.…”

Section: Kinematic Model Of Car and Tuning Of Ukfmentioning

confidence: 99%

“…For this, the same data of the fuzzy system was used (Table 2). 10) and (11), show the values of the R and Q covariance matrices for tuning the UKF, highlighting, those that delivered the best correction response of the sensor data.…”

Section: Kinematic Model Of Car and Tuning Of Ukfmentioning

confidence: 99%

“…In this work, the authors achieve a 30% improvement in the accuracy of vehicle trajectory tracking. In [11], a GPS sensor is used as a reference and an inertial measurement unit (IMU) delivers data to an inertial navigation system (INS) to reconstruct a trajectory. The INS by itself has a significant error and to reduce it an ANFIS is used which has as inputs the IMU data and the error between the INS and the GPS and as output delivers a corrected estimate of the INS.…”

Section: Introductionmentioning

confidence: 99%

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GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles

et al. 2021

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GPS sensors are widely used to know a vehicle’s location and to track its route. Although GPS sensor technology is advancing, they present systematic failures depending on the environmental conditions to which they are subjected. To tackle this problem, we propose an intelligent system based on fuzzy logic, which takes the information from the sensors and correct the vehicle’s absolute position according to its latitude and longitude. This correction is performed by two fuzzy systems, one to correct the latitude and the other to correct the longitude, which are trained using the MATLAB ANFIS tool. The positioning correction system is trained and tested with two different datasets. One of them collected with a Pmod GPS sensor and the other a public dataset, which was taken from routes in Brazil. To compare our proposal, an unscented Kalman filter (UKF) was implemented. The main finding is that the proposed fuzzy systems achieve a performance of 69.2% higher than the UKF. Furthermore, fuzzy systems are suitable to implement in an embedded system such as the Raspberry Pi 4. Another finding is that the logical operations facilitate the creation of non-linear functions because of the ‘if else’ structure. Finally, the existence justification of each fuzzy system section is easy to understand.

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Section: Kinematic Model Of Car and Tuning Of Ukfmentioning

confidence: 99%

Section: Kinematic Model Of Car and Tuning Of Ukfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles

et al. 2021

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“…Furthermore, the work in [25] utilized the ANFIS model to predict the dual-mass MEMS gyroscope's output drift caused by temperature. The work in [26] utilized the ANFIS model to enhance the navigation solution of the INS by training the ANFIS model on a differential GPS dataset as a reference position and evaluated the model on a raw public dataset (KITTI) with a trajectory that lasted from (140-300) s. Furthermore, the work in [27] utilized the ANFIS model as a solution for the navigation problem of a mobile robot.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for an Improved Inertial Navigation System Solution

Mahdi

Azouz

Abdalla

et al. 2022

Sensors

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The inertial navigation system (INS) is a basic component to obtain a continuous navigation solution in various applications. The INS suffers from a growing error over time. In particular, its navigation solution depends mainly on the quality and grade of the inertial measurement unit (IMU), which provides the INS with both accelerations and angular rates. However, low-cost small micro-electro-mechanical systems (MEMSs) suffer from huge error sources such as bias, the scale factor, scale factor instability, and highly non-linear noise. Therefore, MEMS-IMU measurements lead to drifts in the solutions when used as a control input to the INS. Accordingly, several approaches have been introduced to model and mitigate the errors associated with the IMU. In this paper, a machine-learning-based adaptive neuro-fuzzy inference system (ML-based-ANFIS) is proposed to leverage the performance of low-grade IMUs in two phases. The first phase was training 50% of the low-grade IMU measurements with a high-end IMU to generate a suitable error model. The second phase involved testing the developed model on the remaining low-grade IMU measurements. A real road trajectory was used to evaluate the performance of the proposed algorithm. The results showed the effectiveness of utilizing the proposed ML-ANFIS algorithm to remove the errors and improve the INS solution compared to the traditional one. An improvement of 70% in the 2D positioning and of 92% in the 2D velocity of the INS solution were attained when the proposed algorithm was applied compared to the traditional INS solution.

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“…Furthermore, the work in [23] utilized the ANFIS model to predict the dual-mass MEMS gyroscope's output drift caused by the temperature. While the work in [24] utilized the ANFIS model to enhance the navigation solution of the INS by training the ANFIS model by a differential GPS data set as a reference position and evaluating the model on a public raw data set (KITTI) with a trajectory that lasts between (140-300)s. Furthermore, the work in [25] utilized the ANFIS model as a solution for the navigation problem of a mobile robot.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Improved Inertial Navigation System Solution

Abosekeen¹,

Mahdi²,

Azouz³

et al. 2022

Preprint

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Inertial navigation system (INS) is a basic component for obtaining a continuous navigation solution in various applications. The INS navigation solution suffers from a growing error over time. In particular, its navigation solution depends mainly on the quality and grade of the inertial measuring unit (IMU) that provides the INS with both accelerations and angular rates. However, low-cost small micro-electro-mechanical systems (MEMS) suffer from tremendous error sources such as bias, scale factor, scale factor instability, and highly non-linear noise. Therefore, MEMS-IMU measurements lead to drifted solutions when used as a control input to the INS. Accordingly, several approaches have been introduced to model and mitigate the errors associated with IMU. In this paper, a machine learning-based adaptive neuro-fuzzy inference system (ML-ANFIS) is proposed to leverage the performance of low-grade IMUs in two phases. The first phase is training 50% of the low-grade IMU measurements with a high-end IMU to generate a suitable error model. The second phase involved testing the developed model on the remaining low-grade IMU measurements. A real road trajectory was conducted to evaluate the performance of the proposed algorithm. The results show the effectiveness of utilizing the proposed ML-ANFIS algorithm in removing errors and improving the INS solution compared to the traditional one. An improvement of 70% in the 2D-positioning and 92% in the 2D-velocity INS solution is attained when the proposed algorithm was applied compared to the traditional INS solution.

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INS Error Estimation Based on an ANFIS and Its Application in Complex and Covert Surroundings

Cited by 10 publications

References 51 publications

GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles

GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles

A Machine Learning Approach for an Improved Inertial Navigation System Solution

A Machine Learning Approach for Improved Inertial Navigation System Solution

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