In order to improve the accuracy and reliability of micro-electro mechanical systems (MEMS) navigation systems, an orthogonal rotation method-based nine-gyro redundant MEMS configuration is presented. By analyzing the accuracy and reliability characteristics of an inertial navigation system (INS), criteria for redundant configuration design are introduced. Then the orthogonal rotation configuration is formed through a two-rotation of a set of orthogonal inertial sensors around a space vector. A feasible installation method is given for the real engineering realization of this proposed configuration. The performances of the novel configuration and another six configurations are comprehensively compared and analyzed. Simulation and experimentation are also conducted, and the results show that the orthogonal rotation configuration has the best reliability, accuracy and fault detection and isolation (FDI) performance when the number of gyros is nine.

This paper presents modification of Radial Basis Function Artificial Neural Network (RBF ANN)-based temperature compensation models for Interferometric Fiber Optical Gyroscopes (IFOGs). Based on the mathematical expression of IFOG output, three temperature relevant terms are extracted, which include: (1) temperature of fiber loops; (2) temperature variation of fiber loops; (3) temperature product term of fiber loops. Then, the input-modified RBF ANN-based temperature compensation scheme is established, in which temperature relevant terms are transferred to train the RBF ANN. Experimental temperature tests are conducted and sufficient data are collected and post-processed to form the novel RBF ANN. Finally, we apply the modified RBF ANN based on temperature compensation model in two IFOGs with temperature compensation capabilities. The experimental results show the proposed temperature compensation model could efficiently reduce the influence of environment temperature on the output of IFOG, and exhibit a better temperature compensation performance than conventional scheme without proposed improvements.

MEMS/GPS integrated navigation system has been widely used for land-vehicle navigation. This system exhibits large errors because of its nonlinear model and uncertain noise statistic characteristics. Based on the principles of the adaptive Kalman filtering (AKF) and unscented Kalman filtering (AUKF) algorithms, an adaptive unscented Kalman filtering (AUKF) algorithm is proposed. By using noise statistic estimator, the uncertain noise characteristics could be online estimated to adaptively compensate the time-varying noise characteristics. Employing the adaptive filtering principle into UKF, the nonlinearity of system can be restrained. Simulations are conducted for MEMS/GPS integrated navigation system. The results show that the performance of estimation is improved by the AUKF approach compared with both conventional AKF and UKF.

Traditional generalized likelihood test method can detect faults while can not isolate multiple faults in redundant strapdown inertial navigation system. In this paper, a multi-faults detection and isolation method based on generalized likelihood test and linear prediction approach is proposed. A generalized likelihood test method is used to detect the fault of the system, and the linear prediction method is used to estimate the values of inertial sensors. The faulty inertial sensors can be isolated through comparing the estimated values with measurements of inertial sensors, and the faulty information is recorded for system reconstruction. Through simulation and experiment, it is proved that new method can accurately detect and isolate multiple faults and thus guarantee the reliability of strapdown inertial navigation system.

For long-time operation applications based on single-axis rotation inertial navigation system (SRINS), the estimation and compensation of gyroscope's constant drift along the rotational axis in comprehensive calibration is the most important problem. In this paper, a novel comprehensive calibration method for SRINS is presented. During this process, SRINS is operating in the outer level damping mode. Kalman filter has been designed based on the SRINS model with the damping network. The velocity and position provided by differential GPS (DGPS) are introduced as reference information for the estimation of rotational axis gyroscope drift. For stationary scenario, the observability degree of system states are given by an observability analysis based on singular value decomposition. The validity of this method is confirmed by simulations. The influence of linear accelerated motion on the estimation of drift is obtained.

Soft faults in navigation sensors will lead to the degradation of the accuracy and reliability of integrated navigation system. To solve this problem, a wavelet analysis and signal singularities based soft fault detection method are given out. To find signal singularities and detect the faults, the modulus maxima values are calculated after the wavelet transform of original signal. By calculating the Lipschitz exponent using the modulus maxima value at the fault point, the fault types are distinguished. Then, a fault-tolerant federated filtering algorithm for the calibration of INS/GPS/DVL integrated navigation system is proposed. Simulations are conducted and results show that sensor soft faults can be detected accurately. By effectively isolating the fault and refactoring information, the accuracy and reliability of navigation system are improved.