Stimation; (d) the relative error of accelerometer bias estimation. of accelerometer bias estimation.As can be noticed in Figure 4a, among the attitude errors, the relative yaw error is the biggest. five. Conclusions yaw error reaches five `without covariance transformation. The integrated The relative The benefit with covariance transformation process is the fact that yaw error of 0.2`. navigation outcome of thecovariance transformation includes a less relative it establishes the As transformation partnership of theposition error is 12 m, without having amongst the n-frame plus the shown in Figure 4b, the relative integrated navigation filter covariance transformation. G-frame. It fundamentally solves the issue of transformation shows superior stability along with a integrated navigation outcome with covariance filter overshoot and error discontinuity. brought on by the change in navigation eight m. As enhancing navigation accuracy when crossing the smaller relative position error of frame, shown in Figure 4c,d, the maximum bias error with the polar area. Besides this, the covariance transformation technique will not transform h, gyroscope with and devoid of covariance transformation reached 0.001 h and 0.02 the existing navigation algorithm. The resultsthe the flight experimentand without the need of covariance respectively. The maximum bias error of of accelerometer, with and semi-physical simulation show that the covariance transformation approach is successful at any latitude. As transformation, reached 0.1 ug and 25 ug, respectively. the latitude increases, the horizontal element on the earth’s rotational angular velocity decreases, resulting in weaker observability of your yaw angle error. The error fluctuation five. Conclusions triggered by the frame switching will increase. Within this case, the covariance transformation The benefit with the covariance transformation method is that it establishes the strategy nevertheless makes a smooth transformation by way of the integrated navigation filter. transformation relationship in the integrated navigation filter amongst the n-frame and Gframe. It fundamentally solves the problem of curation, Y.Z.; formal analysis, Y.Z.; investigaAuthor Contributions: Conceptualization, L.W.; data filter overshoot and error discontinuity. brought on tion,by the transform in navigation frame, enhancing navigation draft, Y.Z.;when crossing the polar C.G.; methodology, L.W.; supervision, G.W.; writing–original accuracy writing–review and region. In addition to this, the covariance transformation process doesn’t modify the current editing, L.W. All authors have read and agreed to the Ceforanide In stock published version from the manuscript. navigation algorithm. The results from the flight experiment and semi-physical simulation show Funding: This study was funded by the National Choline (bitartrate) Formula Nature Science Foundation of China, grant that the covariance transformation system is helpful at any latitude. Because the latitude quantity 62003360, plus the Standard Research Project of College of Advanced Interdisciplinary Studies, increases, the horizontal component on the earth’s rotational angular velocity decreases, grant quantity ZDJC19-07. resulting in weaker observability with the yaw angle error. The error fluctuation brought on by the frame switching will increase. In this case, the covariance transformation approach still makes a smooth transformation by way of the integrated navigation filter.Author Contributions: Conceptualization, L.W.; data curation, Y.Z.; formal analysis, Y.Z.; investigation, C.G.; methodology, L.W.; supervision, G.W.; writing–original d.
