Zhiyong Shi

The redundant micro inertial attitude and heading reference system (AHRS) is designed to improve the accuracy of the MEMS inertial navigation system. However, the MEMS sensor in the redundant AHRS has zero bias, scale factor error, installation error, and misalignment error. Therefore, the deterministic error compensation is very important to improve the output accuracy of a redundant inertial navigation system. Aiming at the problem that the error correction of redundant accelerometer in the redundant AHRS is complex and highly dependent on high-precision turntable and navigation system, the error correction method of redundant accelerometer based on maximum likelihood estimation/improved Levenberg–Marquardt (MLE/IL-M) algorithm is proposed. Based on the accelerometer’s noise statistical characteristics and maximum likelihood estimation theory, an objective function containing the accelerometer error parameters is established, and then the standard L-M algorithm is improved to solve the unconstrained optimization problem accurately, thereby achieving high-precision compensation for deterministic errors in acceleration. The experimental results show that the improved method can avoid the dependence on the high-precision turntable and navigation reference system. After calibration using the MLE/IL-M algorithm, the accuracy has improved by 94.27% compared with before calibration, and it has improved by 21.51% compared with using the standard L-M algorithm. Therefore, it can improve the calibration accuracy, and effectively avoid the misalignment error between sensitive axes.

The magnetic gradient tensor system (MGTS) is disturbed by the measurement noise and electromagnetic field, which affects its accuracy of calibration and target tracking seriously. Hence, we proposed an improved variational modal decomposition (IVMD) noise reduction method of for measurement and calibration of MGTS. First, continuously sampling around the center of MGTS to obtain a sufficient amount of three-axis magnetic component signals, and the total magnetic intensity (TMI) and cosine signals are vector-synthesized to avoid distortion. Then, the end-point extension of the signal is performed using the spectral cyclic correlation coefficient, and variational modal decomposition (VMD) on the synthesized signal and kernel independent component analysis (KICA) are performed on its principal components to eliminate interference noise and modal aliasing. Finally, the system is calibrated with the integrated compensation and rotation alignment (ICRA) method to eliminate sensors systemic error, array misalignment error and hard or soft magnetic interference. Experiments show that, compared with ICRA direct calibration, the proposed method reduces the root mean square error (RMSE) of the MGT component from 102.2 nT/m to 5.6nT/m in the indoor electromagnetic field environment. In this scene, the tracking accuracy RMSE of the magnet target is reduced from 0.16 m to 0.07 m.

Quickly grasping the surrounding environment’s information and the location of the vehicle is the key to achieving automatic driving. However, accurate and robust localization and mapping are still challenging for field vehicles and robots due to the characteristics of emptiness, terrain changeability, and Global Navigation Satellite System (GNSS)-denied in complex field environments. In this study, an LVI-SAM-based lidar, inertial, and visual fusion using simultaneous localization and mapping (SLAM) algorithm was proposed to solve the problem of localization and mapping for vehicles in such open, bumpy, and Global Positioning System (GPS)-denied field environments. In this method, a joint lidar front end of pose estimation and correction was designed using the Super4PCS, Iterative Closest Point (ICP), and Normal Distributions Transform (NDT) algorithms and their variants. The algorithm can balance localization accuracy and real-time performance by carrying out lower-frequency pose correction based on higher-frequency pose estimation. Experimental results from the complex field environment show that, compared with LVI-SAM, the proposed method can reduce the translational error of localization by about 4.7% and create a three-dimensional point cloud map of the environment in real time, realizing the high-precision and high-robustness localization and mapping of the vehicle in complex field environments.

Vehicle dynamics model-aided inertial navigation system (INS) is an effective approach for autonomous navigation when the Global Navigation Satellite System fails, However, the accuracy of the vehicle dynamics model (VDM) will decrease, or even diverge, in low-speed and high-dynamic situations, which leads to the performance degradation of the whole system. The traditional VDM-aided INS uses the velocity information from VDM to estimate and correct INS errors, but most previous research ignored that the acceleration and angular velocity from INS are more accurate than VDM. To reach the full potential of INS and VDM, this paper proposed a three-layer structure for the VDM-aided INS. In the proposed method, the navigation information is divided into the sensor layer, system layer I, and system layer II. In the sensor layer, VDM is corrected by INS through the strong tracking cubature Kalman filter, and the chi-square test is adopted to avoid redundant computation. In the system layer I, INS is aided by the corrected velocity information from VDM. In the system layer II, the position is updated with the velocity and attitude that are corrected in the system layer I. As the result, VDM and INS can be corrected in different layers simultaneously, and the accuracy and robustness of the whole system can be improved. The simulation experiment proved that, with the sensor layer filter, the velocity errors of VDM can be reduced by 35% in low-speed and high-dynamic situations, and reduced by 20% in the normal situation. Then, a 31-minute field test shows that the maximum horizontal position error of the proposed method is 241.8 m, while those of two traditional VDM-aided INS methods are more than 500 m.

The navigation accuracy is an important performance indicator for intelligent vehicles. Errors of the inertial navigation system (INS) diverge fast in the electromagnetic signal denial environment, and the vehicle model aid is a common approach to solve it. However, the accuracy of the vehicle model will be influenced by the road condition, vehicle motion state, and other interferences, so precision degradation and gross error are frequent. Against this problem, this article proposes the robust multilayer vehicle kinematics model (VKM)-aided INS. First, the gross error detection and isolation method is proposed based on the singular value decomposition (SVD) and chi-square test, and problems caused by the inadequate measurement redundancy are analyzed and solved by the “model redundancy.” Second, the multi-layer vehicle model-aided INS is proposed, which contains the sensor layer, the system layer I, and the system layer II. In the sensor layer, the soft and hard constraints of the vehicle model are defined, and the vehicle velocities are estimated with the constraints. Then, in the system layer I, the vehicle velocities from the sensor layer are used to estimate and correct INS errors. At last, the position is updated in the system layer II with the corrected attitude and velocity, so that the navigation performance can be improved. The simulation and field experiments proved that the proposed method has higher navigation accuracy and better robustness against gross error and high-dynamics situation and that its position error is less than 20 m in the 7-min field test.

The positioning of multiple magnetic sources with different depths, distributions and magnetic moments is extremely difficult. The improved tilt angle for target area recognition can be well adapted to the self-adaptive fuzzy c-means (SAFCM) clustering algorithm for grid measurement of magnetic gradient tensors (MGT), which provides a feasible solution to solve the complex problem of multi-target inversion. First, we improve the tilt angle with the normalized source strengths and the tensor contraction, which can accurately identify the two-dimensional area boundary of objects with different buried depths regardless of the magnetization direction. The grid measurements of magnetic gradient tensor systems (MGTS) enable this process. Then, with the tensor spacial invariant relations, we calculate the source initial position coordinates corresponding to each node in the recognition area, thereby forming a dense point cloud in the space near the target real position. Finally, we introduce a penalty function into the clustering validity function to eliminate the monotonous decreasing trend of the cluster number optimization process, thus the SAFCM algorithm can accurately and automatically detect the number and position of centroids. The simulation shows that with the Gaussian noise of 0 mean and 5 nT/m variance, the targets number recognition accuracy of 20 magnetic dipoles is 97.6%, and the horizontal position and buried depth estimation accuracy is greater than 91.7% and 85.6% respectively. In the real survey areas of 2.1m×2.1m and 1.2m×1.2m, the coordinates’ estimation deviation of nine small magnets is less than 0.091m.

In this brief, a centered error entropy based variational Bayesian adaptive and robust Kalman filter (CEEVBKF) is proposed to suppress outlier noise and estimate the unknown noise covariance adaptively. The derived CEEVBKF contains three steps: one-step prediction, centered error entropy (CEE) based outlier suppression, and variational Bayesian (VB) inference. The CEE criterion is first used to suppress outlier noise and obtain rough state estimation value, then they are set as a priori value in VB inference step for accurate a posteriori state estimation. The joint estimation of CEE and VB improves the iterative efficiency and reduces the parameter sensitivity. The simulation results show the effectiveness of CEEVBKF.

Aiming at the problem of large navigation errors for unmanned vehicle based on MEMS inertial navigation systems(MEMS-INS, MINS) due to low output accuracy of MEMS inertial sensors, a redundant MINS is designed and a high precision positioning algorithm is proposed. In redundant MINS, the output of the redundant gyroscopes is estimated by the improved virtual gyro algorithm based on improved current statistical model and interacting multiple model Kalman filter (ICS-IMMKF). The weighted fusion algorithm is used to fuse the redundant accelerometer information, thus the virtual micro inertial navigation system (Virtual-MINS) is obtained. In the process of vehicle positioning based on virtual MINS, the centripetal acceleration is solved based on the kinematic constraints. Combined with the navigation information of Virtual-MINS and odometer (OD), the difference of centripetal acceleration and velocity are used as observation, and a Virtual-MINS/OD positioning algorithm based on kinematic nonholonomic constraints (NHC/Virtual-MINS/OD) is proposed. The simulation results show that the ICS-IMMKF algorithm can achieve high precision fusion of redundant gyro. In the test, azimuth maneuver is not constrained by the ground, therefore the ICS-IMMKF algorithm can obtain the azimuth rate with high precision. The experimental results show that the root mean square (RMS) of the East and North positioning errors, as well as the RMS of pitch, roll and azimuth estimation error based on the NHC/Virtual-MINS/ OD positioning algorithm are reduced to 34.15%, 22.65%, 55.11%, 64.93% and 41.37% of the NHC/#1-MINS/OD positioning algorithm, respectively. The method will provide a solution for high-precision positioning when GNSS fails.

The magnetic gradient tensor system (MGTS) based on the fluxgate sensors array replaces partial differentials with the sensors reading difference to approximate the magnetic field gradient, but it introduces structural errors at the mean time. A good structure design with reasonable parameters can reduce structural errors and expand the theoretical detection range of the MGTS. From the analysis of the complete 18-uniaxial MGTS, five simplified structures of the MGTS were summarized, which include the plane cross, equilateral triangle, square, right-angle tetrahedron, and regular tetrahedron. The differential magnetic gradient tensor measurement matrixes of different structures were also provided, where we found the plane-cross structure produces the smallest structural error. Moreover, the theoretical detection limits expression of the MGTS to the magnetic dipole was derived. The detection limit boundary of the MGTS is related to the sensor’s accuracy, the array baseline distance, and the magnetization direction and magnetic moment magnitude of the source. In actual heading-line surveys, the magnetic moments of four typical small magnets were successfully estimated by the magnetic dipole positioning method, and the theoretical detection limits of the built plane-cross MGTS aiming at the four magnets were estimated. The results show that the estimation accuracy of the theoretical detection limits of MGTs is ±0.5 m.