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
针对无人机飞行时模型预测和量测噪声变化不规律等问题,提出了一种基于位姿调节因子的模糊自适应扩展卡尔曼滤波方法(Fuzzy Adaptive Extended Kalman Filter based on Position and Attitude factors,APFAEKF)。通过利用滤波器的多维...针对无人机飞行时模型预测和量测噪声变化不规律等问题,提出了一种基于位姿调节因子的模糊自适应扩展卡尔曼滤波方法(Fuzzy Adaptive Extended Kalman Filter based on Position and Attitude factors,APFAEKF)。通过利用滤波器的多维残差信息,结合模糊推理系统得出位置调节因子和姿态调节因子,进一步反馈到滤波器中在线连续修正噪声统计特性,以提高组合导航系统解算精度。通过对INS/GPS/CNS组合导航系统的仿真,验证了算法对提高精度的作用。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.
文摘针对无人机飞行时模型预测和量测噪声变化不规律等问题,提出了一种基于位姿调节因子的模糊自适应扩展卡尔曼滤波方法(Fuzzy Adaptive Extended Kalman Filter based on Position and Attitude factors,APFAEKF)。通过利用滤波器的多维残差信息,结合模糊推理系统得出位置调节因子和姿态调节因子,进一步反馈到滤波器中在线连续修正噪声统计特性,以提高组合导航系统解算精度。通过对INS/GPS/CNS组合导航系统的仿真,验证了算法对提高精度的作用。
