Star sensor is a preferred attitude measurement device for its extremely high accuracy. Star acquisition is the essential and critical procedure, which is aiming at acquiring accurate star areas. However, degenerated acquisition results under complex conditions become one of the major restrictions for modern star sensor. In this paper, an accurate and autonomous star acquisition method is proposed. Mathematical morphology and variable thresholding are combined for accurate star extraction; motion PSF is estimated in frequency domain and nonlinear filter is adopted for star restoration. Accurate star acquisition can be achieved based on only one star image. Simulations and laboratory experiments are conducted for verification. Several existing methods are also reproduced for comparison. Acquisition results demonstrate that the proposed method is effective and an excellent performance can be achieved autonomously under complex conditions, along with more detected stars and improved acquisition accuracy.

The star sensor installation error has a great influence on the attitude accuracy of the stellar-inertial navigation system and should be precisely calibrated. However, it is difficult to realize the installation error without a proper reference on a swaying base, which becomes a limitation for operation accuracy and reaction ability improvement. In this paper, a star sensor installation error calibration method on a swaying base is proposed. The measurement equation is established using accelerometer information, gravity and direct star vector observations in the inertial frame. The state equation is deduced according to the navigation error model. The installation error can be calibrated in the inertial frame instead of the zero-velocity information with respect to the Earth. The simulations and experimental results indicate that the proposed method performs much better than the traditional method on a swaying base. The calibration accuracy can be within 1 arc-s in simulation, and the stability is approximately 5 arc-s in the experiment. High accuracy can be achieved without precise external devices and artificial references. The proposed method is very suitable for mooring ships and for readiness on duty vehicles. The on-line accuracy and rapid-response ability can be significantly improved.

This study is focused on addressing the problem of delayed measurements and contaminated Gaussian distributions in navigation systems, which both have a tremendous deleterious effect on the performance of the traditional Kalman filtering. We propose a non-linear, multiple-step, randomly delayed, robust filter, referred to as the multiple-step, randomly delayed, dynamic-covariance-scaling cubature Kalman filter (MRD-DCSCKF). First, Bernoulli random variables are adopted to describe the measurement system in the presence of multiple-step random delays. Then, the MRD-DCSCKF uses the framework of the multiple-step randomly delayed filter, based on a state-augmentation approach, to address the problem of delayed measurements. Meanwhile, it depends on a dynamic-covariance-scaling (DCS) robust kernel to reject the outliers in the measurements. Consequently, the proposed filter can simultaneously address the problem of delayed measurements and inherit the virtue of robustness of the DCS kernel function. The MRD-DCSCKF has been applied to vision-based spacecraft-relative navigation simulations, where quaternions are adopted to represent spacecraft’s attitude kinematics, and the attitude update is completed with quaternions and generalized Rodrigues parameters. Monte Carlo simulations have illustrated that MRD-DCSCKF is superior to other well-known algorithms by providing high-accuracy position and attitude estimations in an environment with different delay probabilities and/or different outlier-contamination probabilities. Therefore, the proposed filter is robust to delayed measurements and can suppress outliers.

The verticality of the borehole determines the firmness of the superstructure, so it is very important to measure the verticality of the borehole. However, the existing static manual measurement technology has low efficiency and poor accuracy. In order to realize dynamic measurement, reduce cost and improve measurement accuracy, this paper proposes the error mutual correction method for low-cost IMU according to the working characteristics of dynamic and static combination of drill pipe, and establishes an error online compensation model and verticality measurement model. The mutual compensation method of gyroscope and accelerometer errors suppresses the accumulation of measurement errors. In addition, the pile digger works in a complex environment, and a verticality measurement sensor that is waterproof, vibration-proof, high-temperature-proof, with online solution and wireless transmission is designed to improve the reliability of the device. Experiments show that the mutual correction verticality measurement method in this paper can achieve a measurement accuracy of 7% (3σ).

The scheme of three-self FOG Inertial Navigation System (INS) has been explored as a potential alternative to provide the desired self-calibration property, and Fiber Optic Gyroscope (FOG) is the main component of the corresponding INS. However, the existence of FOG dead band will cause a severe INS accuracy degradation in process of gyro bias calibration and alignment accuracy. In this paper, a correspondence was derived, that is, the ratio of the intersection angle between the FOG input axis and the east direction to the theoretical output of FOG is a constant value in various approximately eastward. Nevertheless, when the intersection angle changes, the ratio also would be significant change because of FOG working in dead band. Thus, a dead band self-test method at system-level was proposed and the corresponding verification experiments were carried out. The experimental results indicate that the dead band of the Y-axis FOG is accurately tested as -0.0125°/h by the proposed method. In conclusion, our work opens up a new perspective regarding the self-test method of dead band at system-level, and the innovative self-test method is effective and feasible.