The inertial measurement unit (IMU) biases, DVL lever arm and installation misalignment angles between IMU and doppler velocity log (DVL) have a great influence on in-motion initial alignment for DVL-aided low-cost strap-down inertial navigation system (SINS). A new Kalman filter-based initial alignment method is proposed in this paper. To weaken the effects of IMU biases, DVL lever arm and installation misalignment angles between IMU and DVL, a closed-loop scheme is presented to simultaneous estimate and compensate these parameter errors and the body attitude matrix based on a linear state-space model, which improves the accuracy of vector observations. The constant matrix from initial body frame to initial navigation frame can be determined based on the vector observations by Davenport's q-method. Simulation results illustrate that, for the DVL-aided low-cost SINS, the alignment performance of the proposed initial alignment method is better than that of the compared initial alignment methods.

In this paper, an on-line calibration method for strap-down inertial navigation system (SINS)/doppler velocity log (DVL) based integrated navigation system is proposed to jointly estimate the calibration parameters of SINS and DVL, where the DVL lever arm and gyroscope scale factor error are considered. The full-parameters, including the gyroscope scale factor error, gyroscope bias, accelerometer bias, DVL installation misalignment angle and DVL lever arm, are modeled as calibration parameters. A new state-space model for on-line calibration of SINS/DVL integrated navigation system is established, which also introduces the measurements of global navigation satellite system (GNSS) and celestial navigation system (CNS) to improve the observability of calibration parameters. To suppress the effects of nonlinear error introduced by large installation error between SINS and DVL, a full-parameters feedback correction scheme is proposed. In addition, a special trajectory for the proposed on-line calibration method is designed based on the observability analysis results and the actual motion conditions of the ship, which ensure those calibration parameters in the calibration model can be observed. Simulation results and semi-physical simulation results illustrate that the proposed on-line calibration method can achieve better calibration results than existing methods compared in this paper, and the full-parameters can be jointly calibrated online, even in the case of large installation errors between SINS and DVL.

The grid strapdown inertial navigation system (SINS) used in polar navigation also includes three kinds of periodic oscillation errors as common SINS are based on a geographic coordinate system. Aiming ships which have the external information to conduct a system reset regularly, suppressing the Schuler periodic oscillation is an effective way to enhance navigation accuracy. The Kalman filter based on the grid SINS error model which applies to the ship is established in this paper. The errors of grid-level attitude angles can be accurately estimated when the external velocity contains constant error, and then correcting the errors of the grid-level attitude angles through feedback correction can effectively dampen the Schuler periodic oscillation. The simulation results show that with the aid of external reference velocity, the proposed external level damping algorithm based on the Kalman filter can suppress the Schuler periodic oscillation effectively. Compared with the traditional external level damping algorithm based on the damping network, the algorithm proposed in this paper can reduce the overshoot errors when the state of grid SINS is switched from the non-damping state to the damping state, and this effectively improves the navigation accuracy of the system.

In this paper, a new position loci-based in-motion initial alignment method is proposed for a lowcost attitude and heading reference system (AHRS). A new sliding-window-based position integration method is proposed to construct the position vector observations, which weakens the accumulated errors induced by inertial measurement unit (IMU) bias errors in vector observations. Also, a new closed-loop method is proposed to jointly estimate the time-varying body attitude matrix and the unknown IMU biases and global position system (GPS) lever-arm based on the Kalman filter. The estimates of the timevarying body attitude matrix, IMU biases and GPS lever-arm are all used to construct and compensate the position vector observations, which improve the alignment accuracy. Both the simulation and carmounted test results illustrate that the proposed alignment method can achieve better alignment accuracy than these existing methods for a position loci-aided low-cost AHRS.

An improved coarse alignment (ICA) algorithm is proposed in this paper with a focus on improving alignment accuracy of odometer-aided strapdown inertial navigation system (SINS) under variable velocity and variable acceleration condition. In the proposed algorithm, the outputs of inertial sensors and odometer in a sampling interval are linearized rather than assumed to be a constant, which improves the accuracy of the vector observations and the precision of coarse alignment. Simulation and field test results illustrate that, under variable velocity and variable acceleration condition, the proposed algorithm can obtain a better alignment performance than conventional coarse alignment method.

In this article, a multibranching streamer discharge model considering random fluctuations of carrier density is established based on the electrohydrodynamic discharge model, and the multibranching streamer discharge under impulse voltage is carried out by finite element method. The morphologies of positive and negative streamers with multiple branches are obtained. The randomness branching characteristics of positive and negative streamers under the same voltage are explored, and the differences in branching characteristics of positive and negative streamers are analyzed. Results show that carrier density fluctuations difference significantly influences the positive and negative streamer branches, and the random fluctuations of carrier density are an essential factor for the formation of negative streamer branches. The simulation results in this article are in good reflect of the formation characteristics of positive and negative streamer branches obtained in the experiments, which can better explain the morphological differences between the positive and negative streamers.