The integration of an inertial navigation system(INS) and a celestial navigation system(CNS) has the superiority of high autonomy. However, its reliability and accuracy are permanently impaired under poor observation ...The integration of an inertial navigation system(INS) and a celestial navigation system(CNS) has the superiority of high autonomy. However, its reliability and accuracy are permanently impaired under poor observation conditions. To address this issue, the present paper proposes a tightly coupled INS/CNS/spectral redshift(SRS) integration framework based on the spectral redshift error measurement. In the proposed method, a spectral redshift error measurement equation is investigated and embedded in the traditional tightly coupled INS/CNS integrated navigation system to achieve better anti-interference under complicated circumstances. Subsequently, the inaccurate redshift estimation from the low signal-to-noise ratio spectrum is considered in the integrated system, and an improved chi-square test-based covariance estimation method is incorporated in the federated Kalman filter, allowing to deal with measurement outliers caused by the inaccurate redshift estimation but not influencing the effect of other correct redshift measurements in suppressing the error of the navigation parameter on the filtering solution. Simulations and comprehensive analyses demonstrate that the proposed tightly coupled INS/CNS/SRS integrated navigation system can effectively handle outliers and outages under hostile observation conditions, resulting in improved performance.展开更多
This paper presents a new random weighting estimation method for dynamic navigation positioning. This method adopts the concept of random weighting estimation to estimate the covariance matrices of system state noises...This paper presents a new random weighting estimation method for dynamic navigation positioning. This method adopts the concept of random weighting estimation to estimate the covariance matrices of system state noises and observation noises for controlling the disturbances of singular observations and the kinematic model errors. It satisfies the practical requirements of the residual vector and innovation vector to sufficiently utilize observation information, thus weakening the disturbing effect of the kinematic model error and observation model error on the state parameter estimation. Theories and algorithms of random weighting estimation are established for estimating the covariance matrices of observation residual vectors and innovation vec- tors. This random weighting estimation method provides an effective solution for improving the positioning accuracy in dynamic navigation. Experimental results show that compared with the Kalman filtering, the extended Kalman filtering and the adaptive windowing filtering, the proposed method can adaptively determine the covariance matrices of observation error and state error, effectively resist the disturbances caused by system error and observation error, and significantly improve the positioning accu- racy for dynamic navigation.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42004021&41904028)the Shenzhen Science and Technology Program(Grant No.JCYJ20210324121602008)the Shaanxi Natural Science Basic Research Project,China(Grant No.2022-JM313)。
文摘The integration of an inertial navigation system(INS) and a celestial navigation system(CNS) has the superiority of high autonomy. However, its reliability and accuracy are permanently impaired under poor observation conditions. To address this issue, the present paper proposes a tightly coupled INS/CNS/spectral redshift(SRS) integration framework based on the spectral redshift error measurement. In the proposed method, a spectral redshift error measurement equation is investigated and embedded in the traditional tightly coupled INS/CNS integrated navigation system to achieve better anti-interference under complicated circumstances. Subsequently, the inaccurate redshift estimation from the low signal-to-noise ratio spectrum is considered in the integrated system, and an improved chi-square test-based covariance estimation method is incorporated in the federated Kalman filter, allowing to deal with measurement outliers caused by the inaccurate redshift estimation but not influencing the effect of other correct redshift measurements in suppressing the error of the navigation parameter on the filtering solution. Simulations and comprehensive analyses demonstrate that the proposed tightly coupled INS/CNS/SRS integrated navigation system can effectively handle outliers and outages under hostile observation conditions, resulting in improved performance.
基金National Natural Science Foundation of China(60574034)Aeronautical Science Foundation of China(20080818004)
文摘This paper presents a new random weighting estimation method for dynamic navigation positioning. This method adopts the concept of random weighting estimation to estimate the covariance matrices of system state noises and observation noises for controlling the disturbances of singular observations and the kinematic model errors. It satisfies the practical requirements of the residual vector and innovation vector to sufficiently utilize observation information, thus weakening the disturbing effect of the kinematic model error and observation model error on the state parameter estimation. Theories and algorithms of random weighting estimation are established for estimating the covariance matrices of observation residual vectors and innovation vec- tors. This random weighting estimation method provides an effective solution for improving the positioning accuracy in dynamic navigation. Experimental results show that compared with the Kalman filtering, the extended Kalman filtering and the adaptive windowing filtering, the proposed method can adaptively determine the covariance matrices of observation error and state error, effectively resist the disturbances caused by system error and observation error, and significantly improve the positioning accu- racy for dynamic navigation.
