Rotation modulation is an effective method to enhance the accuracy of an inertial navigation system (INS) by modulating the gyroscope drifts and accelerometer bias errors into periodically varying components. The typical RINS drives the inertial measurement unit (IMU) rotation along the vertical axis and the horizontal sensors’ errors are modulated, however, the azimuth angle error is closely related to vertical gyro drift, and the vertical gyro drift also should be modulated effectively. In this paper, a new rotation strategy in a dual-axis rotational INS (RINS) is proposed and the drifts of three gyros could be modulated, respectively. Experimental results from a real dual-axis RINS demonstrate that the maximum azimuth angle error is decreased from 0.04° to less than 0.01° during 1 h. Most importantly, the changing of rotation strategy leads to some additional errors in the velocity which is unacceptable in a high-precision INS. Then the paper studies the basic reason underlying horizontal velocity errors in detail and a relevant new calibration method is designed. Experimental results show that after calibration and compensation, the fluctuation and stages in the velocity curve disappear and velocity precision is improved.

Rotating modulation technique is a mature method which has been widely used in the rotational inertial navigation system (RINS). Inertial Measurement Unit (IMU) can be driven to rotate to modulate the inertial devices’ errors, in this way the position accuracy of RINS can be greatly improved. However, the outputs of attitude are easily affected for the existence of rotation mechanism. The rotation mechanism of tri-axis RINS consists of three pairs of motors and photoelectric encoders. The errors of the rotation mechanism mainly contain the installation errors of the gimbals, the installation errors of photoelectric encoder and the zero-position errors of the system; they have different effects on the outputs of the attitude. This paper researches the principle of attitude output accuracy loss in tri-axis RINS, the effects of different errors on attitude are analyzed and a self-calibration method based on recursive least squares (RLS) is proposed to calibrate these errors. Simulation and experiment results prove that the analyses is right and experiment results show that the proposed attitude compensation method can improve short-term attitude accuracy greatly, after the compensation, the attitude accuracy of the rotation direction is less than 12” , and the attitude accuracy of the other direction is less than 8” .

Fiber optic gyroscope (FOG) plays a critical role in aerospace, marine transportation, geological exploration, and other fields because of its advantages of low cost and broad development prospects. In recent years, there has been a variety of temperature drift compensation methods overcoming the temperature instability of FOGs. However, with the advent of large-diameter high-precision FOGs (diameter greater than 200 mm), which are more susceptible to unstable ambient temperature, few previous studies have been conducted from the perspective of complex multivariate temperature field. In this article, a temperature drift compensation method based on a multivariate temperature field is proposed to fill this gap. Combining the theoretical basis of FOG and the structure of a large-diameter high-precision FOG, a multivariate temperature drift model is analyzed and established, and support vector regression (SVR) is utilized to train the temperature drift model. To improve the modeling capability, variational mode decomposition (VMD) is introduced to accurately extract the temperature drift signal, and the model parameters are optimized by particle swarm optimization (PSO). The results of multichannel variable temperature experiments verified the feasibility and superiority of this method, which is expected to lay the foundation for the application of this kind of FOG in inertial navigation systems.

The calibration of an inertial measurement unit (IMU) is a key technique to improve the preciseness of the inertial navigation system (INS) for missile, especially for the calibration of accelerometer scale factor. Traditional calibration method is generally based on the high accuracy turntable, however, it leads to expensive costs and the calibration results are not suitable to the actual operating environment. In the wake of developments in multi-axis rotational INS (RINS) with optical inertial sensors, self-calibration is utilized as an effective way to calibrate IMU on missile and the calibration results are more accurate in practical application. However, the introduction of multi-axis RINS causes additional calibration errors, including non-orthogonality errors of mechanical processing and non-horizontal errors of operating environment, it means that the multi-axis gimbals could not be regarded as a high accuracy turntable. As for its application on missiles, in this paper, after analyzing the relationship between the calibration error of accelerometer scale factor and non-orthogonality and non-horizontal angles, an innovative calibration procedure using the signals of fiber optic gyro and photoelectric encoder is proposed. The laboratory and vehicle experiment results validate the theory and prove that the proposed method relaxes the orthogonality requirement of rotation axes and eliminates the strict application condition of the system.

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.