A Novel Multi-Level Integrated Navigation System for Challenging GNSS Environments

Abosekeen, Ashraf; Iqbal, Umar; Noureldin, Aboelmagd; Korenberg, Michael J.

doi:10.1109/tits.2020.2980307

Cited by 33 publications

(27 citation statements)

References 43 publications

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“…Despite the advantage of the INS having a high short-term accuracy, it suffers from the drift accumulation of the biases over time. The accuracy of the INS's navigation solution and the ability to reduce the errors accumulated over time depend on the type of inertial measurement unit (IMU) [2,3]. Recently, the utilization of micro-electro-mechanical systems (MEMSs) has been introduced for inertial sensor systems with the advantages of low cost, small size, and low power consumption [4].…”

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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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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show abstract

“…However, the advantage of the INS is that it has high short-time accuracy because it suffers from drift accumulation of biases over time. The accuracy of the INS's navigation solution and the ability to reduce the errors accumulated over time depends on the type of inertial measuring unit (IMU) [2]. Lately, the utilization of micro-electro-mechanical systems (MEMS) has been developed for inertial sensor systems with the advantages of low cost, small size, and low power consumption [3].…”

Section: Introductionmentioning

confidence: 99%

“…The difficulty in modeling these errors was due to the existence of non-linear errors. These errors cannot be modeled by the traditional techniques such as the Kalman filter (KF), the extended KF (EKF), the unscented Kalman filter (UKF), or even by the particle filter (PF) [2,5]. Accordingly, there is a great need to find an alternative to traditional methods that do not require the difficulty and complexity of error modeling.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, the work [17] introduced a non-linear autoregressive neural network with external inputs (NARX) combined with UKF to enhance the position and velocity accuracy of the INS/GNSS integration. Furthermore, the work in [2] proposed a fast orthogonal search (FOS) model to reduce and compensate the unmodeled residual non-linear errors of a Mag/Radar/RISS/GPS integration system to improve the navigation solution during the GPS outages. While the work in [18]utilizes the FOS model as a GPS swept anti-jamming technique to discriminate between the GPS authentic signal and the interference by the chirp frequency jammer.…”

Section: Introductionmentioning

confidence: 99%

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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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“…In [ 46 , 47 ], two machine learning methods were proposed to achieve the nonlinear initial alignment of SINS under the condition of large misalignment angles, of which one was based on Gaussian process regression (GPR) [ 46 ], the other utilized a combination of Gaussian mixture model (GMM), expectation–maximization (EM), and UKF filter [ 47 ]. In order to reduce the effects of nonlinear errors, the nonlinear error modeling technique based fast orthogonal search (FOS) was introduced, which have been applied to the radar (RAD)/reduced inertial sensor system (RISS) integration [ 48 ], fine frequency estimation of time and code division-orthogonal frequency division multiplexing (TC-OFDM) receivers [ 49 ], INS/global navigation satellite system (GNSS) integrated navigation systems [ 50 ] and MEMS inertial sensors in mobile devices [ 51 ]. However, almost all of these nonlinear alignment methods mentioned above have some shortcomings such as complex algorithm, heavy computational load, difficulties in parameter optimization, insufficient stability, and poor accuracy, etc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS

Zhao

Yan

Yong-yuan

et al. 2020

Sensors

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In order to achieve the fine alignment of strapdown inertial navigation (SINS) under large misalignment angles, a novel filtering alignment method is proposed based on the second-order extended Kalman filter (EKF2) and adaptive fuzzy inference system (AFIS). Firstly, the quaternion is employed to represent the attitude errors of SINS. A second-order nonlinear state equation is made based on the nonlinear velocity error model and attitude error model, and the linear measurement equation is based on the velocity outputs from SINS. Then, the filtering scheme is designed based on EKF2 and AFIS. The error estimation and fine alignment can be achieved by using the proposed filtering scheme. The results of Monte Carlo Simulation show that the errors of pitch, roll and yaw misalignment angles quickly decrease to about 14″, 15″ and 7.62′ respectively in 350 s under the condition of any misalignment angles with pitch error from −40° to 40°, roll error from −40° to 40°, and yaw error from −50° to 50°. Even when the initial misalignment angles are all very large such as (80°, 120°, 170°), the proposed nonlinear alignment method still can converge normally by utilizing the adaptive fuzzy inference system (AFIS) to adjust the covariance matrix Pk/k−1. Finally, the turntable experiment was performed, and the effectiveness and superiority of the proposed method were further verified by compared with other nonlinear methods.

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A Novel Multi-Level Integrated Navigation System for Challenging GNSS Environments

Cited by 33 publications

References 43 publications

A Machine Learning Approach for an Improved Inertial Navigation System Solution

A Machine Learning Approach for an Improved Inertial Navigation System Solution

A Machine Learning Approach for Improved Inertial Navigation System Solution

A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS

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