No abstract

When noise statistical characteristics of the system are unknown and there are outliers in the measurement information, the filtering accuracy of the strap-down inertial navigation system/geomagnetic navigation system (SINS/GNS) tightly integrated navigation system would decrease, and the filtering may diverge in severe cases. To solve this problem, a robust residual-based adaptive estimation Kalman filter (RRAEKF) method is proposed. In the RRAEKF method, the covariance matching technique is employed to detect whether the system is abnormal or not. When the system is judged to be abnormal, a weighted factor is constructed to identify and weight the wild value in the measurement information, eliminating the influence of the outliers on the filtering accuracy. To further improve the filtering accuracy of the integrated navigation system, a contraction factor is introduced to adaptively adjust the gain matrix of the filter algorithm, obtaining the optimal estimate of the state vector and covariance matrix. Simulation results demonstrate that compared with the standard extended Kalman filter method and residual-based adaptive estimation method, the space position errors of the SINS/GNS tightly integrated navigation system based on the proposed method are improved by 63.37% and 56.93%, respectively, in the case of time-varying noise and the presence of outliers.

No abstract

To solve the problem that geomagnetic signals are susceptible to random noise and instantaneous pulse interference in geomagnetic navigation, a geomagnetic signal de-noising method based on improved empirical mode decomposition (IEMD) and morphological filtering (MF) is proposed. The instantaneous pulse interference is eliminated by designing different structural elements according to the characteristics of the pulse signal. The signal after filtering the instantaneous pulse interference is decomposed by EMD, and the intrinsic mode functions (IMFs) obtained from the decomposition are determined as two modes (i.e. noise IMFs and mixed IMFs) by the cross-correlation coefficient criterion. The noise IMFs are removed directly, and a normalized least means square filter (NLMS) is designed to remove noise from mixed IMFs, which can adaptively adjust the filtering parameters according to the noise level of different IMF components. The noise-reduced mixed IMFs and residual are reconstructed to obtain the final geomagnetic signal. Experiment results illustrate that the proposed MF-IEMD method can effectively achieve noise reduction. Comparing with the traditional EMD and MF-EMD de-noising methods, the root mean square errors(RMSE) decreased by 49.27% and 24.79%, respectively.

The digital measurement and processing is an important direction in the measurement and control field. The quantization error widely existing in the digital processing is always the decisive factor that restricts the development and applications of the digital technology. In this paper, we find that the stability of the digital quantization system is obviously better than the quantization resolution. The application of a border effect in the digital quantization can greatly improve the accuracy of digital processing. Its effective precision has nothing to do with the number of quantization bits, which is only related to the stability of the quantization system. The high precision measurement results obtained in the low level quantization system with high sampling rate have an important application value for the progress in the digital measurement and processing field.

No abstract