Data fusion of redundant MEMS inertial sensors has become a new method for reducing sensor drift error and enhancing the accuracy of navigation systems. In this paper, a redundant MEMS gyroscope system having an optimal configuration structure is constructed. Firstly, the evaluation index of a redundant gyroscope system configuration is established, and the influencing factors of the configuration are analyzed under the condition of noise correlation in the component gyroscopes. Then redundant 4,5,6-gyro systems are designed. Secondly, the optimal KF algorithm for multi-signal fusion of the redundant gyroscope system is designed to estimate the three-dimensional orthogonal angular rate. Finally, a redundant 4-IMU system is designed. The simulation results showed that the input rate signal in the body coordinate XbYbZb can be accurately estimated and the drift error in single gyroscope can be remarkably reduced by fusing the redundant measurements. The experimental results illustrated that the ARW and RRW noise on the Xb, Yb and Zb axes were reduced by a factor about 3.2 and 3.7 compared to the single gyroscope. In the swing test, the Xb and Yb axes’ 1σ estimated error was reduced by a factor about 4.7 using the optimal KF algorithm compared to the single gyroscope, while on the Zb axis there was a decrease by a factor about 2.6.

A high-precision integrated navigation method for a land vehicle using a strapdown inertial navigation system (SINS), an odometer, and Doppler radar is studied. Attitude outputs of the SINS, distance increment outputs of the odometer, and velocity outputs of Doppler radar are taken to accomplish position and velocity calculation. The error models of the odometer and Doppler radar are studied, and error models of position and velocity calculation are established. Errors including that of the SINS, odometer, and Doppler radar are taken as the system state of integrated navigation. The difference between the SINS position output and the previous position calculation output, as well as between the SINS velocity output and the previous velocity calculation output, is taken as the measurement information. On this basis, the Kalman filter algorithm is used in the design of a high-precision integrated navigation filter. The vehicle trajectory is designed and generated, and the researched navigation method is simulated and verified by MATLAB. Simulation results show that in the navigation time of 10800 s, the horizontal position accuracy is ±27.8 m, the altitude accuracy is ±11.2 m, and the error of position does not exceed 0.03% of the vehicle’s total driving distance. The horizontal attitude accuracy and heading accuracy are $\pm 0.3^\prime $ and $\pm 2.4^\prime $ , respectively. The horizontal and upward velocity have accuracy of ±0.05 m/s and ±0.01 m/s, respectively. When the odometer and Doppler radar fail to output for a long time, this integrated navigation method still maintains high navigation accuracy and shows superior reliability. Finally, the vehicular experiment is carried out, and the experimental results show that the actual application effect of the SINS/odometer/Doppler radar integrated navigation is as satisfactory as in the simulation experiment. During the navigation time of 3600 s, the heading error of the navigation system is less than $\pm 2.6^\prime $ , the horizontal attitude errors are less than $\pm 0.7^\prime $ , the horizontal position errors are ±14.9 m, the altitude error is ±13.0 m.

In order to solve the problem of poor anti-interference ability of GNSS/IMU ultra-tight integrated navigation system under high dynamic and harsh environments, a visual assisted GNSS/IMU ultra-tight integrated navigation method is studied. The attitude and position information provided by binocular vision are introduced into the GNSS/IMU ultra-tight integrated navigation system. The state equation of the traditional ultra-tight integrated navigation system is used as the state equation of the visual assisted GNSS/ IMU ultra-tight integrated navigation system. Meanwhile, the pseudorange of SINS and GNSS, the difference of pseudorange rate, the platform misalignment angle of SINS, the position and attitude error of SINS and vision are used as the measurement information of the ultra-tight combination of GNSS/IMU assisted by vision. On this basis, the fuzzy control method is used to replace the traditional federated Kalman filter to fuse the information of the navigation structure of the two self-filters, and the navigation method is simulated and verified by C #. The simulation results show that the computational system performance is improved 52% by using the fuzzy control method compared with the traditional federal Kalman filter method, and the attitude error is significantly reduced in the ultra-tight integrated navigation system with visual assistance. When the GNSS signal is interfered by strong noise, the tracking accuracy of GNSS/IMU ultra-tight integrated navigation system assisted by binocular vision can effectively reduce the navigation error. And the position and velocity errors of the system are kept within 5.0m/s and 0.3m/s, respectively, which effectively solves the navigation problem of low altitudeaircraft in the case of GNSS signal occlusion or interference.

Obtaining a correlation factor is a prerequisite for fusing multiple outputs of a mircoelectromechanical system (MEMS) gyroscope array and evaluating accuracy improvement. In this paper, a mathematical statistics method is established to analyze and obtain the practical correlation factor of a MEMS gyroscope array, which solves the problem of determining the Kalman filter (KF) covariance matrix Q and fusing the multiple gyroscope signals. The working principle and mathematical model of the sensor array fusion is briefly described, and then an optimal estimate of input rate signal is achieved by using of a steady-state KF gain in an off-line estimation approach. Both theoretical analysis and simulation show that the negative correlation factor has a favorable influence on accuracy improvement. Additionally, a four-gyro array system composed of four discrete individual gyroscopes was developed to test the correlation factor and its influence on KF accuracy improvement. The result showed that correlation factors have both positive and negative values; in particular, there exist differences for correlation factor between the different units in the array. The test results also indicated that the Angular Random Walk (ARW) of 1.57°/h^0.5 and bias drift of 224.2°/h for a single gyroscope were reduced to 0.33°/h^0.5 and 47.8°/h with some negative correlation factors existing in the gyroscope array, making a noise reduction factor of about 4.7, which is higher than that of a uncorrelated four-gyro array. The overall accuracy of the combined angular rate signal can be further improved if the negative correlation factors in the gyroscope array become larger.

As unmanned aerial vehicles and other small, low-flying, and low-speed aircrafts are being extensively used, studies on their detection are being extensively conducted in radar application research. However, weak echoes, low Doppler frequencies, and target echoes mixed with ground clutter can considerably degrade the detection performance. Therefore, specific methods for the detection of such targets should be devised. We propose herein a phase compensation and coherent accumulation algorithm based on the fractional Fourier transform (FRFT) for detection and speed estimation of this type of target. First, the energy of the target echo is converged using the FRFT. Next, the phase between the peaks of the target echo is analyzed. Phase compensation and coherent accumulation determined from the expected target speed in the fractional domain eliminate ground clutter and further improve the signal-to-interference-plus-noise ratio. Finally, constant false alarm rate detection is used to identify the target, for which radial speed can be estimated directly according to the peak coordinates. The validity of the algorithm is verified via data simulation and application to real data.