Inertial frame alignment for strapdown inertial navigation systems is widely considered to be a promising method due to its robustness to disturbances and noise. By using the velocity data from a Doppler velocity log, the inertial frame alignment can be performed while the vehicle is in motion. However, the current work reveals that the initial velocity error of the Doppler velocity log at the startup continuously and considerably affects the precision of the in-motion inertial frame alignment. Based on the formula derived with other sensor errors isolated, the azimuth error is proportional to the initial velocity error and is inversely proportional to the square of time, e.g., a 0.3 m/s measurement error in the initial velocity from the Doppler velocity log leading to a considerable azimuth error of more than 1.78° in five minutes. Moreover, the azimuth error becomes bulky if the latitude is high. The modified cost function with a constant vector is used to replace the cost function from the well-known Wahba’s problem. Thus, an alignment result with a much higher precision and with the initial velocity error suppressed can be obtained in a short period of time. The vehicular experimental results further verify the error analysis and the improved method.

The dither mechanism in a ring laser gyroscope (RLG) produces a lateral deformation when subjected to external acceleration, which is called the g-sensitive misalignment. This article considers the g-sensitive misalignment mechanism, examines the calibration coupling error and the navigation error resulting from it, and proposes a system-level calibration method based on the g-sensitive misalignment. The feasibility of the proposed calibration method is verified by the calibration simulations and experiments using a turntable. The results of a laboratory navigation test and an in-vehicle navigation experiment show that when the g-sensitive misalignment is considered and compensated in the calibration process, the navigation error can be significantly reduced. This article concludes that the g-sensitive misalignment of the RLG should not be disregarded. Finally, it is shown that using an efficient calibration method considering the g-sensitive misalignment can enhance the navigational performance of a high-precision RLG rotational inertial navigation system.

Dual-axis rotational inertial navigation system (DRINS) can achieve self-calibration of error parameters through a reasonable rotation scheme. However, the traditional self-calibration methods rely on the external reference information, which fail in global navigation satellite system (GNSS)-denied environments. In long-endurance marine navigation applications, carriers are usually equipped with multiple sets of DRINS. The geometric relationship between the DRINSs can serve as constraint observation for error parameter estimation. Therefore, this article proposes a collaborative calibration method with the navigation information fusion of dual DRINSs in GNSS-denied environments. Considering scale factor error, installation error, gyro drift, accelerometer bias, and accelerometer size-effect error introduced by the rotation of dual DRINSs, a 66-D Kalman filter is established based on the geometric constraint observation. Then, a novel collaborative calibration scheme is designed through analyzing the principles of error state observability for dual DRINSs. Simulations and experiments show that all error parameters can be precisely estimated by the proposed method with the designed calibration scheme and the calibration accuracy could satisfy the demand of long-endurance marine navigation.

In a high-precision ring laser gyro (RLG) inertial navigation system (INS), the g-sensitive misalignment caused by external acceleration represents one of the main error sources except for the conventional inertial measurement unit (IMU) errors (e.g., gyro drift, accelerometer bias, installation error, and scale factor error) and, thus, cannot be ignored. However, in related literature, there have been no effective suppression methods for the g-sensitive misalignment. To solve this shortcoming, this paper proposes an innovative dual-axis rotation modulation navigation scheme to suppress the velocity and position errors caused by the g-sensitive misalignment, as well as conventional IMU errors. First, an error model of the g-sensitive misalignment is constructed. Then, this model is used to analyze the error propagation law of the g-sensitive misalignment in a conventional dual-axis rotation scheme. Further, based on the error propagation characteristics of each error source, the design principles of rotational modulation schemes are defined. Moreover, a 32-position dual-axis rotational modulation scheme is designed to reduce all errors, including the g-sensitive misalignment in RLG INSs. The effectiveness of the proposed rotation scheme is verified by simulations and experiments. The laboratory experimental results show that the navigation accuracy of the proposed method in this paper is improved by 29.8% and 28.3% compared to the conventional 16-position scheme and 64-position scheme, respectively. The in-vehicle experiments show that the proposed method improves the accuracy by 32.7% over the 64-position scheme. The results indicate that the g-sensitive misalignment suppression is necessary in the RLG INS long-endurance autonomous navigation, and a reasonable rotational modulation scheme can effectively improve navigation accuracy.

In the field of unmanned aerial vehicle (UAV) control, high-precision navigation algorithms are a research hotspot. To address the problem of poor localization caused by non-line-of-sight (NLOS) errors in ultra-wideband (UWB) systems, an UWB/MIMU integrated navigation method was developed, and a particle filter (PF) algorithm for data fusion was improved upon. The extended Kalman filter (EKF) was used to improve the method of constructing the importance density function (IDF) in the traditional PF, so that the particle sampling process fully considers the real-time measurement information, increases the sampling efficiency, weakens the particle degradation phenomenon, and reduces the UAV positioning error. We compared the positioning accuracy of the proposed extended Kalman particle filter (EKPF) algorithm with that of the EKF and unscented Kalman filter (UKF) algorithm used in traditional UWB/MIMU data fusion through simulation, and the results proved the effectiveness of the proposed algorithm through outdoor experiments. We found that, in NLOS environments, compared with pure UWB positioning, the accuracy of the EKPF algorithm in the X- and Y-directions was increased by 35% and 39%, respectively, and the positioning error in the Z-direction was considerably reduced, which proved the practicability of the proposed algorithm.

In the Ring Laser Gyro Inertial Navigation System (RLG INS), the temperature characteristics of the accelerometer can directly influence the measurement results. In order to improve navigation accuracy in long-endurance marine navigation, the operating temperature of the accelerometer should be precisely controlled. Based on thermal studies on the accelerometer, temperature control precision should be better than 0.01 °C to achieve 1 × 10⁻⁵ m/s² output accuracy of the accelerometer. However, this conclusion is obtained by approximate calculations and cannot be directly applied to different inertial navigation systems. In order to verify this thermal conclusion and broaden its application, the Back Propagation Neural Network (BP-NN) algorithm is adopted to validate the feasibility of temperature control in this paper. In addition, a multi-level temperature control system is also set up and carefully designed to support the validation and experiments under different conditions. Test results of the temperature control system prove that operating temperature variation can be reduced to 0.01 °C. Meanwhile, the standard deviation per hundred seconds of the accelerometer outputs, after temperature control, reaches 1 × 10⁻⁵ m/s². Static altitude and navigation results were improved by 41.97% and 62.91%, respectively, with the precision temperature control system, which meets the long-endurance marine navigation requirements.