Abstract
In order to achieve a highly autonomous and reliable navigation system for aerial vehicles that involves the spectral redshift navigation system (SRS), the inertial navigation (INS)/spectral redshift navigation (SRS)/celestial navigation (CNS) integrated system is designed and the spectral-redshift-based velocity measurement equation in the INS/SRS/CNS system is derived. Furthermore, a new chi-square test-based robust Kalman filter (CSTRKF) is also proposed in order to improve the robustness of the INS/SRS/CNS navigation system. In the CSTRKF, the chi-square test (CST) not only detects measurements with outliers and in non-Gaussian distributions, but also estimates the statistical characteristics of measurement noise. Finally, the results of our simulations indicate that the INS/SRS/CNS integrated navigation system with the CSTRKF possesses strong robustness and high reliability.
Highlights
For hypersonic cruise vehicles (HCVs), a highly autonomous and reliable navigation system is needed [1,2]
Based on the above research, this paper deduces the linear relationship equation based on velocity in the east-north-up frame and the redshift of the observed vehicle, and establishes the inertial navigation system (INS)/spectral redshift navigation system (SRS)/celestial navigation system (CNS) integrated navigation model
The linear relationship equation between the velocity in the ENU-frame and the redshift of an observed vehicle was deduced and the INS/SRS/CNS integrated navigation model was established based on this relationship for the purposes of improving the autonomy and reliability in the navigation of HCVs
Summary
For hypersonic cruise vehicles (HCVs), a highly autonomous and reliable navigation system is needed [1,2]. The authors of [25,26] estimated scaling factors for the covariance of measurement noise to further adjust the Kalman gain to maintain robustness This method may lead to a suboptimal filtering solution because the scaling factors are determined empirically. Based on the above research, this paper deduces the linear relationship equation based on velocity in the east-north-up frame and the redshift of the observed vehicle, and establishes the INS/SRS/CNS integrated navigation model. Where vINS is the velocity obtained by the INS in the ENU-frame and VSRS is the noise matrix of the SRS. (λINS , LINS ) denotes the longitude and latitude outputs of the INS in the ENU-frame; and VCNS denotes the measurement noise matrix of the CNS.
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