Research on vehicle attitude and heading reference system based on multi-sensor information fusion

Abstract

To improve the accuracy of attitude and heading reference systems for moving vehicles, an effective orientation estimation method is proposed. The method uses an odometer, a low-cost magnetic, angular rate, and gravity sensor. This study addresses the problems of non-orthogonal error, carrier magnetic field interference and calibration to obtain accurate, long-term, stable magnetic field strength. A neural network fusion 12-parameter ellipse fitting method is proposed to eliminate the soft magnetic field and hard magnetic field interference. The interference to the accelerometer from linear acceleration is eliminated by using an odometer and a gyroscope, and the high-frequency noise from the accelerometer is eliminated by using a low-pass filter. An improved method to evaluate vehicle attitude is proposed and utilized to compensate for filtered accelerometer measurement when the vehicle is moving at a uniform, accelerate and steering state. The proposed method uses an effective adaptive Kalman filter based on the error state model to reduce dynamic perturbations. Filter gain is adaptively tuned under different moving modes by adjusting the noise matrix. The effectiveness of the algorithm is verified by experiments and simulations in multiple operating conditions.

Digital Image Restoration Based on Multicontour Batch Scanning

“…The number of sample replications reflects the specific number of block replications in the repairing process; this parameter is determined by the average size of the replicated blocks, which can indirectly reflect texture complexity and structure information of the block to be repaired. The influence of the number of Poisson treatments to the number of sample replications on the repair time is also an important parameter worth referring to [22,23]. It can be seen from Table 2 that the repair time of the three images to be repaired is in the unit of 100 seconds, which is within the acceptable range, and the proportion of repaired pixels to the total pixels is 3.5%, 6.8%, and 20.0%, different loss ratios represent different inpainting difficulties, and all three groups of images to be inpainted with different ratios have been inpainted, indicating that the proposed inpainting algorithm has good versatility [24,25].…”

Indoor Mapping of Magnetic Fields Using UAV Equipped with Fluxgate Magnetometer

“…There are many calibration methods that can be used for magnetometer calibration [ 82 , 83 , 84 , 85 , 86 , 87 , 88 ]. In our case, the magnetometer was calibrated using the neural network algorithm [ 88 ] and noise analysis methodology [ 89 ] developed at our department, which was also applied for the onboard satellite magnetometer calibration, described in detail in [ 88 ], with 500 calibration points of the total magnetic field of 60 μT generated with equal distribution on a virtual sphere surface.…”

An Innovative multi-sensor data fusion method based on thrice deeply-fusion architecture for multi-object tracking

“…15 Another widely studied method is the kinematics and dynamics-based approach, where the typical representative is Kalman filter (KF)-based tracking method including state-vector fusion and measurement fusion, 16 which is usually not optimal due to the fact that with some nonzero probability, the measurement which was selected for the update did not originate from the object in track. 17 The covariance intersection of a distributed and decentralized KF has been discussed. 18 An unscented KF (UKF) fusion method was proposed to obtain the high-level fusion results.…”