The geomagnetic aided inertial navigation algorithm has become one of the current research hotspots in the field of aircraft. It can overcome the shortcomings of pure inertial navigation error accumulation over time, and achieve long-term, autonomous, and all-weather navigation. However, the traditional iterative closest contour point (ICCP) algorithm requires high sensor performance, has poor tolerance to noise, and has a complex algorithm with poor real-time performance. The timeliness of the traditional magnetic contour matching (MAGCOM) algorithm is related to the search area, and the matching search area is fixed, and the algorithm is inflexible. To address the above problems, this paper proposes a geomagnetic navigation method based on coarse estimation of navigation error and search area adaption. When the vehicle flies into the geomagnetic database region, the navigation error is roughly estimated based on the error characteristics of the inertial navigation system. Considering the influence of noise and uncertainty, three times the value of the estimated error is used as the edge length of the geomagnetic matching search area, and geomagnetic matching is performed for positioning. The product of velocity and time provided by the inertial navigation system is used as a constraint to determine whether the geomagnetic matching results are abnormal. If multiple consecutive geomagnetic matching results are normal, the navigation system automatically reduces the matching search area and search step to improve the matching accuracy and timeliness. Simulation experiments show that the accuracy of the adaptive search area geomagnetic matching method proposed in this paper can reach within 200 meters.

In this article, the attitude of the master inertial navigation system (INS) installed on the autonomous drone launcher and the slave INS installed on the autonomous drone are correlated by means of establishing a time-varying model of the relative attitude. And the coupling processes of the initial attitude error of slave INS and its gyro bias are modeled in the relative attitude. Then, according to the different characteristics of the two error coupling modes, the two types of error are decoupled by the equivalent rotation vector and the higher order small terms neglected, and the nonlinear dynamic accumulation model of the gyro bias of slave INS is established in the navigation coordinate system. Next, the linear accumulation model of the gyro bias of slave INS is obtained in its own coordinate system by multiplying the dynamic accumulation amount with the attitude matrix in real-time. Finally, the gyro bias of slave INS is estimated by means of the least squares algorithm. Simulation experiments show that the proposed method can estimate more than 80% of the gyro bias of slave INS. The results of the vehicle-mounted experiment show that the compensation of the gyro bias of slave INS leads to a significant decrease in the accumulation rate of the relative attitude error over time.

The periodic-calibration of missile borne strap-down inertial navigation system (SINS) is the key to realize missile precision attack, especially for strategic missile. However, the traditional periodic-calibration method can only be realized on the three-axis turntable by disassembly, which affects the fast launch ability. It is urgent to propose a periodic free disassembly self-calibration method for strap-down inertial navigation. The assembly strap-down inertial navigation system can only be sensitive to one-degree-of-freedom rotation in the storage period with the help of the rolling maneuver of the missile. It has so far been thought to be impossible in the absence of bi-axial rotation to realize the self-calibration of missile-borne SINS. In this paper, the skewed six axis redundancy configuration mode for strap-down inertial navigation system and observability optimal allocation method have been proposed to solve the self-calibration problem of strap-down inertial navigation. Multiple calculations of variety configurations are conduct to accomplish the calibration, instead of single calculation in traditional method. Eventually, the hardware-in-the-loop simulations results, including the quantities of calibrated sensors and observability degree of main errors, were superior to traditional calibration method reported so far. The method presented here provides a new perspective to realize self-calibration of missile-borne INS during the storage period in pure strap-down way.

A celestial navigation system (CNS) can be integrated in vehicles to suppress the diverging error of the inertial navigation system (INS). However, the CNS is subjected to aero-thermo-optic effects in hypersonic environments that introduce large-scale centroiding errors in star sensor images and greatly reduce the star sample size, limiting the usefulness of the CNS for hypersonic vehicles. In this study, these difficulties were addressed by the development of a statistical model for measurement noise based on the angular distance so that the standard deviation of the measurement noise can be correlated with the star sample size. To compensate for the centroiding error, a variable-dimension pseudo-transient adaptive filter was designed where the initial R matrix is set according to the star sample size. A semi-physical experiment was conducted to evaluate the performance of the proposed method, and the results indicated that stable centroiding error compensation was achieved for extremely small star sample sizes. In particular, the proposed method compensated for up to 66.24% of random positioning error, which is a performance improvement of about 10% compared with conventional compensation methods.

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).

In single-leader mode, if only relative ranging information is available between drones, the traditional cooperative navigation (CN) algorithm suffers from the problem of incomplete observability of the position state quantity. To address this problem, this article describes the CN process using an objective optimization function, and adds the constraints of time-series information of inertial trajectories and relative spatial relationship to the objective function, which narrows the solution space of the position state quantity. Then, for realizing the decoupling of the optimization objective function, this article further simplifies the CN process into an optimization problem that only needs to compute two parameters such as the optimal rotation matrix and the optimal translation matrix. Second, this article proposes a concept of planal gridding and adopts the traversal search instead of the traditional partial differential calculation of the extrema to obtain these two key parameters, which simplifies the calculation process of the model. Finally, the process of noise reduction for CN results is given. Simulation results show that the proposed method can suppress the divergence of navigation error of follower drones equipped with different levels of inertial navigation systems (INSs). The higher the accuracy of INS, the better the error suppression effect. In a flight experiment of about 700 s, the proposed method resulted in a CN accuracy of better than 250 m for a follower drone, which was equipped with an INS with a gyro bias stability of 8°/h.