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.
This paper develops an enhanced fault-tolerant strap-down inertial navigation (SINS)/spectral redshift navigation (SRS)/celestial navigation (CNS) integration system. In the developed system, a new SRS observation equation based on Doppler shift is established to improve the accuracy and anti-interference performance of SINS/SRS/CNS integration. Subsequently, an adaptive fault-tolerant cubature Kalman filter (AFTCKF) is proposed to inhibit the effect of noise uncertainty and outliers in observations on state estimation. The AFTCKF promotes the robustness of the cubature Kalman filter, in which the maximum likelihood method is adopted for the online estimation of observation noise statistics, and the sequential probability ratio test and the chi-square test are employed in the determination of Kalman gain to further resist the outliers in the filtering procedure. The developed SINS/SRS/CNS integration system not only has the capability to maintain the stability of the navigation system in high-dynamic circumstances, but also is robust against the observation uncertainty. Simulations and comprehensive analysis have been conducted to verify the effectiveness of developed SINS/SRS/CNS integration.
As an emerging means of transportation for the intelligent transportation system (ITS) in aviation and aerospace, hypersonic cruise vehicles (HCVs) have received numerous research interests during the past several decades. However, the navigation and positioning strictly limit the progress and application of HCVs due to their special characteristics on dynamics and environments. To improve the stability of navigation in HCVs, a chi-square test-based adaptive federated cubature Kalman filter (CAFCKF) is proposed in this paper. In the proposed approach, the chi-square test is adopted for the estimation of the measurement noise statistics firstly. Subsequently, a new adaptive information fusion factor is designed for the federated filter to adjust the contribution of each subsystem. Finally, the information sharing factor, which is used for the amendment of the state covariance of each subsystem, is refined based on the judging index of the chi-square test accordingly. Simulation results show that the proposed CAFCKF can be used to improve the accuracy and stability of the navigation system.
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