Transfer alignment is always a key technology in a strapdown inertial navigation system (SINS) because of its rapidity and accuracy. In this paper a transfer alignment model is established, which contains the SINS error model and the measurement model. The time delay in the process of transfer alignment is analyzed, and an H∞ filtering method with delay compensation is presented. Then the H∞ filtering theory and the robust mechanism of H∞ filter are deduced and analyzed in detail. In order to improve the transfer alignment accuracy in SINS with time delay, an adaptive H∞ filtering method with delay compensation is proposed. Since the robustness factor plays an important role in the filtering process and has effect on the filtering accuracy, the adaptive H∞ filter with delay compensation can adjust the value of robustness factor adaptively according to the dynamic external environment. The vehicle transfer alignment experiment indicates that by using the adaptive H∞ filtering method with delay compensation, the transfer alignment accuracy and the pure inertial navigation accuracy can be dramatically improved, which demonstrates the superiority of the proposed filtering method.
Transfer alignment on a moving base under a complex dynamic environment is one of the toughest challenges in a strapdown inertial navigation system (SINS). With the aim of improving rapidity and accuracy, velocity plus attitude matching is applied in the transfer alignment model. Meanwhile, the error compensation model is established to calibrate and compensate the errors of inertial sensors online. To suppress the filtering divergence during the process of transfer alignment, this paper proposes an improved adaptive compensation H∞ filtering method. The cause of filtering divergence has been analyzed carefully and the corresponding adjustment and optimization have been made in the proposed adaptive compensation H∞ filter. In order to balance accuracy and robustness of the transfer alignment system, the robustness factor of the adaptive compensation H∞ filter can be dynamically adjusted according to the complex external environment. The aerial transfer alignment experiments illustrate that the adaptive compensation H∞ filter can effectively improve the transfer alignment accuracy and the pure inertial navigation accuracy under a complex dynamic environment, which verifies the advantage of the proposed method.
High accuracy and reliable navigation in the underwater environment is very critical for the operations of autonomous underwater vehicles (AUVs). This paper proposes an adaptive federated interacting multiple model (IMM) filter, which combines adaptive federated filter and IMM algorithm for AUV in complex underwater environments. Based on the performance of each local system, the information sharing coefficient of the adaptive federated IMM filter is adaptively determined. Meanwhile, the adaptive federated IMM filter designs different models for each local system. When the external disturbances change, the model of each local system can switch in real-time. Furthermore, an AUV integrated navigation system model is constructed, which includes the dynamic model of the system error and the measurement models of strapdown inertial navigation system/Doppler velocity log (SINS/DVL) and SINS/terrain aided navigation (SINS/TAN). The integrated navigation experiments demonstrate that the proposed filter can dramatically improve the accuracy and reliability of the integrated navigation system. Additionally, it has obvious advantages compared with the federated Kalman filter and the adaptive federated Kalman filter.
Respiratory syncytial virus (RSV) is the key underlying cause of acute lower respiratory tract infection in infants; however, no licensed vaccine against RSV infection is currently available. This study was undertaken to assess the preventive effect of vaccine on RSV infection. In this metaanalysis, 1,792 published randomized clinical trials of RSV vaccines from Jan 1973 to Sep 2015 were examined. Among thirteen studies that met the inclusion criteria, eleven studies estimated the impact of RSV vaccines and four studies estimated the effect of adjuvants. The odds ratios (ORs) were 0.31 (95% CI, 0.15-0.67) and 0.62 (95% CI, 0.29-1.34), respectively. We found that RSV subunit vaccines can significantly reduce the incidence of RSV infection and that whether vaccination with adjuvant therapy was an effective strategy still remained to be studied. This analysis of the preventive effect of vaccines on RSV infection has direct applications for the prevention of RSV infections.
Accurate and rapid transfer alignment with large attitude errors under uncertain disturbances is crucial for the strapdown inertial navigation system (SINS). This paper proposes an adaptive UT-H∞ filter which combines UKF technology and a H∞ filter to increase the robustness of the nonlinear transfer alignment system. By focusing on the time-varying and the uncertain external disturbances, the robustness factor of the adaptive UT-H∞ filter can be adaptively adjusted to balance the robustness and filtering accuracy of the dynamic system. Then, the nonlinear error propagation model of the transfer alignment is established in detail, and the velocity plus attitude matrix measurement model is used to improve the performance of transfer alignment. Moreover, the sensor error compensation model is established to calibrate and compensate for the sensor errors of the gyros and accelerometers online during transfer alignment. The vehicle transfer alignment experiments show that the proposed adaptive UT-H∞ filter can significantly improve the transfer alignment accuracy and the pure inertial navigation accuracy compared with the existing filtering methods under uncertain disturbances.
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