Nowadays, Magnetic and Inertial Measurement Units (MIMUs) have become useful tools for determining the attitude of moving objects. An Attitude Calculation Algorithm (ACA) is programmed into a MIMU, which determines the attitude by fusing three built-in sensors, namely, gyroscope, accelerometer, and magnetometer. Attitude is often represented by Euler angles, quaternion, or rotation matrix. Despite that great development has been achieved, how to eliminate the negative effect of magnetic disturbance on the pitch and roll estimation is still difficult for a ACA. This paper aims to propose an Extended Kalman Filter (EKF) based approach for problem solving. Inspired by the fact that the Euler angles are computed by using some elements of the rotation matrix, a Diagonalizable Regulation Matrix (DRM) is multiplied to the right half part of the gain matrix (which is multiplied to the residual of the magnetometer measurement in the update stage of EKF). The determination of DRM is based on the principle that these elements can be appropriately regulated by the DRM so as to make the magnetometer measurement only participate in the computation of azimuth. No further revision to EKF is made except the Kalman gain matrix, hence the proposed EKF behaves similar as traditional EKF, except its ability of eliminating the negative effect of magnetic disturbance on the pitch and roll estimation. The superiority of the proposed EKF is validated by comparing it with four other competing algorithms through simulation and experimental experiments.

Magnetic and Inertial Measurement Units (MIMUs) are mainly used for determining the attitude of moving bodies. The Extended Kalman Filter (EKF) and the Complementary Filters (CFs) are the most commonly applied algorithms for calculating the attitude of the body that a MIMU is attached. Despite its much higher accuracy than CFs, the time-consuming EKF may not be competent for the work of attitude calculation if a microcomputer with limited computation power is used. To any attitude calculating algorithm, its Posterior Attitude (PA) is computed as: PA = prior attitude + Gain Matrix (GM) × innovation. The prior attitude and the innovation are derived from the measurements of the gyroscope, the accelerometer and the magnetometer. Detailed analysis in this study found that the reason for the performance difference between EKF and CFs is their different structures of the GMs, which promotes the authors to construct a new GM. Based on some reasonable assumptions, the GM of EKF is simplified under the principle that the computation accuracy of PA is decreased as little as possible, but its computation burden is reduced as much as possible. After the replacement of the GM of the EKF by the innovatively designed GM, a new complementary filter, named TCF, is proposed. The simulation and experimental results show that TCF performs better than two CFs and is close to EKF in terms of attitude estimation accuracy, but uses nearly the same computation time as the two CFs, and about half of the computation time required by EKF.

As a sensor unit, a magnetic and inertial measurement unit (MIMU) is mainly composed of a tri-axis gyroscope, a tri-axis accelerometer and a tri-axis magnetometer. The Extended Kalman Filter (EKF) is the currently preferred algorithm used to fuse the outputs of the MIMU sensor in order to determine the attitude of the MIMU’s carrier platform. One of the main problems of the EKF is its high computational cost, which is largely due to solving the matrix inversion in the Kalman gain. This paper simplifies the Kalman gain by ensuring it does not contain any matrices that would require an inversion operation. This is achieved by simplifying the linearized measurement matrix (the Jacobian matrix) through orthogonalizing the observation and reference vectors. The inversion of the matrix contained in the Kalman gain can then be mathematically derived and the derived matrix can eventually replace the matrix inversion, in order to avoid the inversion operation when the EKF is executed. The simplified EKF is compared to the traditional EKF experimentally and the results show that the simplified method has similar accuracy to the traditional method, but with a faster execution time.

The direction of arrival (DOA) and number of sound sources is usually estimated by short-time Fourier transform and the conjugate cross-spectrum. However, the ability of a single AVS to distinguish between multiple sources will decrease as the number of sources increases. To solve this problem, this paper presents a multimodal fusion method based on a single acoustic vector sensor (AVS). First, the output of the AVS is decomposed into multiple modes by intrinsic time-scale decomposition (ITD). The number of sources in each mode decreases after decomposition. Then, the DOAs and source number in each mode are estimated by density peak clustering (DPC). Finally, the density-based spatial clustering of applications with the noise (DBSCAN) algorithm is employed to obtain the final source counting results from the DOAs of all modes. Experiments showed that the multimodal fusion method could significantly improve the ability of a single AVS to distinguish multiple sources when compared to methods without multimodal fusion.

Despite the magnetometer in a magnetic and inertial measurement unit is used to estimate the azimuth, its measurements can also negatively affect the computation of inclination angles when magnetic disturbance appears. It is challenging to eliminate that negative effect. For problem solving, a new complementary filter is proposed. The filter is divided into two stages. In the first stage, the attitude represented by quaternion is predicted by gyroscope, then the predicted quaternion is converted to Euler angles. In the second stage, the predicted Euler angles are compensated by employing the accelerometer and the magnetometer, then the after-compensated Euler angles are converted back to quaternion. To solve the problem of “gimbal lock”, two sets of Euler angles with different definition of the rotation sequence are used. The simulation and experiment results demonstrate that the proposed filter outperforms six other competing filters, revealing its potential usage in such applications where magnetic disturbance is not allowed to affect the computation of inclination angles.

Aiming at the problems of low efficiency, reliability, and safety of manual construction for demolition of old lines, a design method of an intelligent ropeway type line changing robot based on lifting force control and synovial film controller is proposed. First, the mechanical model of robot load and line sag is established, and the sag of the overhead line where the robot is located is used to calculate the jacking force that the jacking device needs to provide to the robot. Then, by introducing the radial basis function (RBF) neural network adaptive algorithm into the synovial controller, an adaptive sliding mode position control algorithm based on the RBF neural network is designed to achieve high-precision motion control of the robot in complex operating environments. Finally, based on the compactness, weight, and reliability of the robot, the optimal design is carried out from four aspects of topology, size, shape and morphology, and the design scheme of the robot for wire removal is proposed, and the robot is produced. The developed robot and the other three robots are compared and analyzed under the same conditions through simulation experiments. The results show that the maximum operating time, maximum climbing angle, and maximum traveling speed of the robot developed in this study are all optimal, which are 45 min, 10°, and 1 m/s respectively, and the performance is better than the other three comparison algorithms.