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For indoor close-range high-accuracy attitude measurement using inertial and vision sensors, the major challenge is to achieve a reliable attitude in various scenarios, specifically in the absence of vision information. To address this challenge, we present a fusion strategy for reliable attitude measurement using a micro electro mechanical system (MEMS) gyroscope and camera, particularly during discontinuous vision observations. The proposed algorithm consecutively executes update and prediction modes. In the update mode, when both inertial and visual data are available, the complete Kalman Filter (KF) conducts the entire filtering process and updates residual and state errors. In the absence of visual data, the partial KF operates only when the time update and filtering state vectors are compensated by the transfer residual, which is estimated through self-propagating state error. The parameters of least-squares support vector machine (LSSVM-NARX) continuously update until the next discontinuous vision observation occurs, with the desired outputs and inner inputs being the error angles and the exogenous inputs being the gyroscope values and time. In the prediction mode, without visual data, LSSVM-NARX can provide estimated angle errors between the complete and partial KFs, which are used to eliminate errors from partial KF output vectors to obtain a reliable and accurate attitude solution. Comparisons and extensive semi-physical simulation experiments under various motion trajectories were performed to validate the effectiveness and superiority of the proposed scheme in an accurate and reliable attitude measurement capability associated with discontinuous observations. [ABSTRACT FROM AUTHOR]

• A dual-model instead of traditional single model comprised of CKF-MLP and RBF-CKF algorithms is proposed for position and velocity error compensation during GPS outages. • CKF is added in MLP to reduce network errors and improve modelling accuracy by estimating the weights of MLP adaptively for training process which is termed as CKF-MLP algorithm. • RBF is added in CKF to predict the errors caused by filter itself and improve INS error compensation accuracy which is termed as RBF-CKF algorithm. To improve the performance of a microelectromechanical-system (MEMS)-based inertial navigation system (INS)/Global Positioning System (GPS) integrated navigation system, a dual optimization scheme comprising a cubature Kalman filter (CKF)-multiple layer perceptron (MLP) and radial basis function (RBF)-CKF is proposed for the compensation of position and velocity errors during GPS outages. The proposed method has advantages: (i) The generalization ability of the CKF-MLP is much better than that of other methods, such as the extended Kalman filter (EKF)-MLP and unscented Kalman filter (UKF)-MLP, and the proposed CKF-MLP provides highly accurate position information even during long GPS outages; (ii) The accuracy of the error estimation of the RBF-CKF is higher than that of other neural-network-assisted CKF methods, such as the Adaboost-CKF and random forest (RF)-CKF, and the proposed CKF-MLP can estimate the weights of the MLP adaptively and achieve an appropriate internal structure when the GPS signal is available; (iii) The RBF is added to the CKF to establish the relationship between filter parameters and estimation error in the proposed RBF-CKF; (iv) During a GPS outage, position errors and velocity errors are predicted and compensated for in the proposed dual optimization scheme. Field test data are collected to evaluate the proposed solution. Experimental results show that (i) the root-mean-square error of the velocity for an eastern position is reduced by 77% to 0.34 m/s using the proposed CKF-MLP strategy; (ii) the root-mean-square error of a northern position determined by the proposed RBF-CKF remains at 23.11 m even during long outages of 500 s, and the RBF-CKF effectively suppresses the divergence seen for other methods; and (iii) the dual optimization process using different estimators provides better error compensation results than a single optimization method, which demonstrates that the proposed solution leads to the better performance of a MEMS-based INS/GPS navigation system. [ABSTRACT FROM AUTHOR]

• MEMS-INS, GPS and polarization compass are integrated together for navigation. • A multi-rate algorithm is proposed to solve the problem caused by different sampling frequencies. • Singular value decomposition is used to improve the numerical stability of the algorithm. The microelectromechanical system-inertial navigation system (MEMS-INS) is one of the most widely used sensor systems in navigation but must be used with other sensors because of its error accumulation characteristics. The information fusion method for integrated navigation systems based on filtering technology is thus very important. This paper introduces a new adaptive Kalman filter for nonlinear integrated systems. An improved multi-rate strong tracking square-root cubature Kalman filter (MR-STSCKF) for a MEMS-INS/Global Positioning System (GPS)/polarization compass integrated navigation system is proposed. The proposed filter is used to estimate the system covariance adaptively. The proposed approach can overcome the problem of the inconsistency between the sampling frequencies of different sensors while maintaining the high precision of the integrated navigation results. Experimental results demonstrate that the proposed MR-STSCKF algorithm is effective in improving the accuracy of the MEMS-INS/GPS/polarization compass integrated navigation system with a high sampling frequency. [ABSTRACT FROM AUTHOR]