In order to reduce the computational complexity, and improve the pitch/roll estimation accuracy of the low-cost attitude heading reference system (AHRS) under conditions of magnetic-distortion, a novel linear Kalman filter, suitable for nonlinear attitude estimation, is proposed in this paper. The new algorithm is the combination of two-step geometrically-intuitive correction (TGIC) and the Kalman filter. In the proposed algorithm, the sequential two-step geometrically-intuitive correction scheme is used to make the current estimation of pitch/roll immune to magnetic distortion. Meanwhile, the TGIC produces a computed quaternion input for the Kalman filter, which avoids the linearization error of measurement equations and reduces the computational complexity. Several experiments have been carried out to validate the performance of the filter design. The results demonstrate that the mean time consumption and the root mean square error (RMSE) of pitch/roll estimation under magnetic disturbances are reduced by 45.9% and 33.8%, respectively, when compared with a standard filter. In addition, the proposed filter is applicable for attitude estimation under various dynamic conditions.

Accurately obtaining the navigation information of the device is crucial for realizing various emerging Internet of Things (IoT) applications, and a multi-source fusion navigation system is the key to achieving this goal. A distributed integrated inertial navigation system (INS), polarization compass (PC), and geomagnetic compass (MAG) enhanced direction approach is presented to improve the accuracy and robustness of the multisource fusion navigation system in complex environments. To estimate the time-varying measurement noise covariance in a nonlinear multi-source fusion navigation system, the traditional federated Kalman filter (FKF) is improved. In the FKF framework, the third-order spherical radial cubature rule and variational Bayesian theory are introduced, and a variational Bayesian federated cubature Kalman filter (VBFCKF) is proposed. Furthermore, a distributed information monitoring and compensation algorithm based on residuals is developed to address issues like anomalous measured values and asynchronous multi-rate problems. Finally, an experimental platform for unmanned vehicle navigation is designed, and the tests are conducted to confirm the efficacy of the suggested approach. The experimental results show that the system can precisely estimate values based on the measurement quality of sub-filters during navigation. It effectively adjusts measurement noise covariance during updates, thereby mitigating the negative impact of interferences like occlusions and electromagnetic noise on the multi-source fusion navigation system in complex environments. This can strengthen the accuracy and robustness of the navigation system.

To improve the navigation performance of the navigation system combining inertial navigation system (INS) and global positioning system (GPS) under complicated environments, especially GPS outages, a navigation method - wavelet neural network based on random forest regression (RFR-WNN) to assist adaptive Kalman filter (AKF) - is proposed. AKF is employed to correct INS errors, the Kalman filter is improved by introducing adaptive factor, to suppress the influence of the complex environment and random errors on the filtering accuracy; RFR-WNN is used to construct a high-precision prediction model when GPS works well, and to provide the required observations for AKF update when GPS outages. To solve the problem that the single neural network structure is easy to cause the overfitting, unstable and low prediction accuracy due to the lack of comprehensive training samples, RFR is introduced to optimize the single WNN, which can improve the generalization ability and prediction accuracy. In order to verify the effectiveness and advancement of the proposed method, vehicle navigation experiments were carried out, the results indicate that the proposed method has better navigation accuracy and performance than compared methods during GPS outages, and this advantage is more obvious in the case that fewer samples are collected.

A proposal for a high-precision accelerometer based on a silicon optical microelectromechanical system (MEMS) is presented in this paper. The device operating principle is that a sensitive mechanical structure senses external vibrations and then the vibrations of the mechanical component cause regular changes in the target modes in the optical system. Through a judicious design of the optical system, we obtain a wide photonic band gap (PBG) (approximately 1.1-2.75 μm) as well as two target modes in the PBG for modulation. Comparison with previously reported works shows that the proposed double-mode accelerometer displays better performance with respect to properties such as sensitivity and resolution. Accurate simulation results obtained by finite element analysis and rigorous coupled wave analysis have obtained the following results: an optical sensitivity of 4.42, a natural frequency of 1032 Hz, a bandwidth from 0 to approximately 900 Hz, a mechanical sensitivity of 0.242 μm/g, an accelerometer sensitivity of 1.017 μm/g, a linear measurement range of 0.517 g and a resolution of 933.7 ng (= 1 pm). Due to these optimized performance characteristics, the proposed accelerometer can achieve high precision detection in microvibration applications such as seismic detection.