Benefiting from frame structure, RINS can improve the navigation accuracy by modulating the inertial sensor errors with proper rotation scheme. In the traditional motor control method, the measurements of the photoelectric encoder are always adopted to drive inertial measurement unit (IMU) to rotate. However, when carrier conducts heading motion, the inertial sensor errors may no longer be zero-mean in navigation coordinate. Meanwhile, some high-speed carriers like aircraft need to roll a certain angle to balance the centrifugal force during the heading motion, which may result in non-negligible coupling errors, caused by the FOG installation errors and scale factor errors. Moreover, the error parameters of FOG are susceptible to the temperature and magnetic field, and the pre-calibration is a time-consuming process which is difficult to completely suppress the FOG-related errors. In this paper, an improved motor control method with the measurements of FOG is proposed to address these problems, with which the outer frame can insulate the carrier's roll motion and the inner frame can simultaneously achieve the rotary modulation on the basis of insulating the heading motion. The results of turntable experiments indicate that the navigation performance of dual-axis RINS has been significantly improved over the traditional method, which could still be maintained even with large FOG installation errors and scale factor errors, proving that the proposed method can relax the requirements for the accuracy of FOG-related errors.
Zero velocity Update (ZUPT) is an effective method to restrain the error divergence of the Inertial Navigation System (INS). Right detection of zero velocity points and appropriate filtering algorithm are the key factors for the success of ZUPT. In this paper, a ZUPT method for vehicle mounted INS based on neural network and Kalman Filter is proposed. The efficiency and accuracy of the zero velocity detection is improved by neural network. The precision of the proposed method can reach 99.19%, and the recall rate is improved by 24% compared with the method based on SVM. And this method has similar accuracy and better real-time performance with the method based on LSTM. Based on the zero velocity detection by neural network, the navigation error is estimated and compensated by Kalman Filter. The effectiveness of the proposed method is proved by vehicular experiment which shows that the velocity error is reduced to 24.2% and the position error is reduced to 9.5%.
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