A hybrid error modeling for MEMS IMU in integrated GPS/INS navigation system

Abstract

Continuity of accurate navigational data for intelligent transportation applications has been widely provided by utilizing low-cost navigation systems through integrating GPS with micro-electro-mechanical-system (MEMS) inertial sensors. To achieve the required accuracy, augmentation of Kalman filter (KF) with nonlinear error modeling techniques such as fast orthogonal search (FOS) was introduced to enhance the navigational solution by estimating and eliminating a great part of both linear and nonlinear errors of azimuth angle sensed by MEMS gyro. Although this augmented approach enhanced the overall navigational accuracy to some extent, it still suffers from some drawbacks that diverge the system accuracy during GPS long outage periods. These drawbacks stem from the wide-variational behavior and high nonlinearities of the errors in MEMS gyros which make it difficult to depend on the non-adaptive linear error model provided by KF to model the two types of MEMS azimuth errors. In this paper we tried to minimize the effect of uncertainties associated with the KF azimuth prediction during the absence of GPS by introducing a hybrid error model which employs support vector machine (SVM) to model the KF output and FOS, based on autoregressive (AR) concept, to model the nonlinear azimuth errors. The performance of the proposed hybrid SVM-FOS approach is evaluated for GPS/ RISS (Reduced inertial sensor system integrated system) and the results were compared with the conventional KF and augmented KF-FOS approaches.

MIMU/Odometer Fusion with State Constraints for Vehicle Positioning during BeiDou Signal Outage: Testing and Results

“…Grip proposed an exponentially stable attitude and gyroscope bias estimation method in GNSS/INS integration [9]. Machine learning (LS-SVM, LSTM-RNN) methods were employed for modeling the errors [10][11][12][13]. Some calibration methods were also proposed to improve positioning accuracy [14][15][16][17][18].…”

A Multiposition Initial Alignment Method of Portable MIMU/FOG Compound Navigation System

“…Due to its outstanding advantages [4], [5], MIMU has become a research focus in the navigation field. For MIMU, the initial alignment cannot be directly achieved because microelectromechanical system (MEMS) sensors have high noise and instability characteristics [6], [7]. Many studies have been carried out to improve the MIMU performance to accomplish the initial The associate editor coordinating the review of this manuscript and approving it for publication was Ehsan Asadi .…”