Lei Wang

Gyro drift aligned with Earth’s spin axis leads to longitude error divergence and latitude error oscillation, limiting the long-endurance performance of inertial navigation system in global navigation satellite system-denied environments. Traditional dual-axis alternating multi-sequence rotation schemes can modulate three-axis sensor errors, but their intermittent sensitive axis rotation weakens the modulation of slow-varying gyro drift. To address this problem, this study proposes a dual-axis simultaneous rotation scheme that continuously modulates drift in all three sensitive directions, with a system structure that provides favorable dynamic characteristics. This article introduces the system structure and theoretical basis of the proposed scheme and compares it with the traditional approach. Then, the simulation of the navigation experiment shows that the proposed scheme improves the modulation effect on the gyro slow-varying drift. To further verify its effectiveness, a series of experiments, including a 24 h static navigation comparison, a 120 h long-endurance static test, and a 48 h on-vehicle dynamic comparison experiment, were conducted. The results of comparative experiment indicate that the static and dynamic positioning accuracy improved by 72.8 % and 76.6 % , respectively, demonstrating the effectiveness and superiority of the proposed scheme.

To address the problem in rotational inertial navigation systems (RINS) where residual sensor calibration errors lead to limited alignment convergence speed and insufficient instantaneous output accuracy, this paper proposes a multi-sensor error online identification and compensation method based on state-expanded self-alignment. Through theoretical analysis, the propagation mechanism of sensor errors under rotational modulation is clarified. On this basis, the traditional self-alignment model is expanded, and a more complete measurement equation is constructed. The QR-RLS algorithm is employed to achieve high-precision online identification and decoupling of errors. Simulation and experimental results show that this method can effectively realize online identification of various sensor errors in RINS, reducing platform misalignment angle estimation oscillations by 70% during alignment and improving the alignment speed by more than 40%. Subsequent navigation experiments show that, after decoupling and compensating for various sensor errors using this method, the velocity and azimuth attitude oscillations within the rotation period are reduced by approximately 60% and 50%, respectively. This study provides a systematic technical approach for online identification of multi-sensor errors in RINS, while improving the convergence speed of alignment and the instantaneous output accuracy of the inertial navigation system, and has important engineering value for enhancing the performance of high-precision inertial navigation systems.

Rotation modulation technology significantly enhances the navigation accuracy of inertial navigation systems (INS), thereby facilitating the widespread application of rotational inertial navigation systems (RINS). In marine or airborne applications, it is common to install two or more RINS units on the same carrier. By employing information fusion techniques, the navigation performance can be significantly enhanced. This paper proposes an online error estimation method for two RINSs. This method is based on the information fusion of two systems. It employs two RINSs with rotation strategies around different axes and utilizes rotation modulation technology to improve the observability of inertial sensor errors. Through Kalman filtering, the online optimal estimation of the gyro drift, accelerometer bias, scale factors and installation misalignment angles of two systems can be achieved. The simulation and experimental results demonstrate the effectiveness of the proposed method in performing online estimation of inertial sensors errors for the two RINSs. Specifically, the estimation accuracy reaches about 0.001°/h for gyroscope drift, about 2μg for accelerometer bias, about 2″for misalignment angles, and about 2 ppm for scale factors in both static and dynamic conditions. Through error compensation and information fusion, the navigation accuracy has increased by 62% in a static state and by 57% in a dynamic condition, thereby validating the efficacy of the proposed methodology.

The attitude accuracy of dual-axis rotational inertial navigation systems (RINS) is often degraded by periodic oscillations stemming from attitude demodulation errors. A primary cause, largely unaddressed by conventional static models, is the dynamic precession of the rotation axes. To address this, this paper proposes a systematic, stepwise decoupling selfcalibration scheme to identify and compensate for these complex errors. The core of this method is an innovative timevarying error model that describes non-orthogonal axes as a superposition of constant and angle-dependent sinusoidal terms, accurately capturing the axis precession effects. The calibration scheme first resolves grating measurement errors (both functional and sampling asynchrony) and then uses single-axis rotations to isolate the non-orthogonal axis errors. These are estimated using a Recursive Least Squares (RLS) algorithm that fuses multi-source data from gyroscopes and accelerometers . Simulation and prototype experiment results demonstrate that the proposed method effectively suppresses the second-harmonic oscillations missed by traditional techniques. For instance, experiments showed a reduction in peakto-peak pitch oscillation from 8 to 2 arcseconds during outer gimbal rotation, restoring attitude accuracy to the theoretical level determined by sensor noise. This work provides both a practical compensation strategy and a diagnostic tool for evaluating the mechanical quality of RINS indexing mechanisms.

In order to avoid the decrease in navigation accuracy and damage to system brought by severe vibration, airborne, and missile-borne. Inertial Navigation System (INS) is often mounted with vibration isolators. However, if the elastic center of the vibration damping system does not coincide with the center of mass of the INS, when only linear vibration input is available, the introduction of the shock absorber will produce angular vibration, and angular vibration and linear vibration will be coupled, that is, coupled angular vibration will be generated, resulting in additional navigation errors. This paper aims at solving this problem for a rotational INS (RINS). Firstly, to theoretically analyze the cause of coupled angular vibration, the six degree of freedom coupled angular vibration model of INS is derived. We also analyze the relationship between the misalignment angle of vibration isolators and vertical angular vibration. Secondly, a vibration suppression method for RINS is proposed. We use the angular velocity measured by the gyro as feedback to control the rotational gimbals of RINS to isolate the coupled angular motion. Experimental results show that the velocity oscillation of RINS caused by the vibration is greatly suppressed and thus proves the validity of the proposed method.

The rotational inertial navigation system (RINS) modulates horizontal sensor errors by rotating the inertial measurement unit (IMU) around the vertical axis. In applications demanding high azimuth accuracy, it is essential to mitigate azimuth errors caused by vertical sensor inaccuracies through horizontal rotational modulation. Therefore, this paper proposes a novel rotational strategy that rotates the IMU around the horizontal east axis. A challenge with this approach is that constant biases of the eastward-facing inertial measurement sensors cannot be modulated. This issue is particularly pronounced during long-duration navigation, where accelerometer bias varies with temperature, leading to significant velocity oscillation error and positioning error. To address this problem, this paper primarily investigates methods for the online calibration and compensation of temperature-related errors of eastward accelerometer bias under this new rotational strategy. Simulation and experimental results demonstrate that using the proposed method significantly reduces the velocity oscillations and improves the navigation accuracy of the RINS.

With the increasing prominence of GNSS defects in recent years, high-precision inertial navigation systems (INS) with autonomy are gradually undertaking UAV navigation tasks and are commonly fused with barometric altimeter (BA) to damp the error growth in the vertical channel without GNSS. However, anomalies or failures in barometric altitude lead to the divergence of vertical errors, while indirectly adversely affecting horizontal navigation, bringing new challenges to UAV autonomous navigation. In this paper, a vertical channel enhanced fusion method based on a high-precision rotational inertial navigation system (RINS) and BA is proposed to enhance the reliability of the RINS vertical channel and correct horizontal navigation information in GNSS denial environments. On the one hand, nonlinear autoregressive with exogenous input (NARX) models for vertical errors are constructed based on Kalman filter (KF) fusion. And the models are identified by least squares support vector machine (LSSVM) with particle swarm optimization (PSO), referred to as NPLSSVM/KF. On the other hand, vertical errors are predicted and compensated by the trained NPLSSVM models during the BA failure, and horizontal navigation errors are also corrected. Finally, the feasibility and superiority of the proposed method are validated by simulation and UAV flight experiment.

The rotational inertial navigation system (INS) uses rotational alignment to improve azimuth alignment accuracy significantly. However, due to the existence of magnetic drifts in the fiber optic gyro, under the comprehensive influence of outside conditions, there will be residual drifts that cannot be averaged out along the body frame or the geographic frame, which will have a significant impact on azimuth alignment accuracy. An innovative and effective calibration compensation strategy is proposed by analyzing the generation mechanism of residual drifts and their influence on alignment accuracy, which can significantly improve azimuth alignment accuracy. With the aid of a high-precision total station, the experimental results of initial alignment in four directions show that the accuracy of azimuth alignment is improved from 16.47 ′ ′ ( 1 σ ) to 10.34 ′ ′ ( 1 σ ) by compensating residual drifts, which has significant theoretical significance and practical value in engineering applications for improving the overall performance of the INS.

It is analyzed that for a nested rotation inertial navigation system (NRINS) constructed from three ring fiber optic gyros nested within each other the main residual errors after rotation modulation are gyro angle random walk (ARW) and non-modulated gyro drift. It is pointed out that different orientations of the gyro sensitive axis lead to different equivalent ARWs, and the non-modulated gyro drift caused by the nested installation of three ring gyros is also different. The mechanism of generation of non-modulated drift in the embedded system and the general relationship with the axis sequence configuration are expounded. The equivalent ARW and non-modulated gyro drift in the inner frame rotation modulation mode and their influence on the alignment and navigation of the system are analyzed and deduced. Finally, taking the two typical applications of short-time and long-time navigation as examples, the influence of different gyro axis sequence configuration schemes on the navigation accuracy of the NRINS is verified through static navigation experiments, and the best axis sequence configuration scheme under the two applications is determined. By optimizing the gyro axis sequence configuration, the accuracy of navigation can be improved by about two times, which is of great significance. This work has important reference value for further improving the accuracy of NRINS and maximizing the accuracy advantage of gyros in different applications.