Local Observability Analysis of Star Sensor Installation Errors in a SINS/CNS Integration System for Near-Earth Flight Vehicles

Yang, Yanqiang; Zhang, Chunxi; Lu, Jiazhen

doi:10.3390/s17010167

Cited by 27 publications

(18 citation statements)

References 17 publications

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“…In a loosely integrated navigation system, the inertial and star sensors work independently, where the measurement of the integrated navigation system is subject to the navigation error of both [ 16 ]. In a tightly integrated navigation system, the star sensor is no longer used for independent navigation calculation but, instead, is used as a sensor to measure the star vector; the system then sets up the measurement equation based on the star vector error [ 17 , 18 ]. The tightly integrated mode can save computational time at the sensor level, but it needs more time to update the measurements in the navigation filter.…”

Section: Introductionmentioning

confidence: 99%

INS/CNS Deeply Integrated Navigation Method of Near Space Vehicles

Sun

et al. 2020

Sensors

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Celestial navigation is required to improve the long-term accuracy preservation capability of near space vehicles. However, it takes a long time for traditional celestial navigation methods to identify the star map, which limits the improvement of the dynamic response ability. Meanwhile, the aero-optical effects caused by the near space environment can lead to the colorization of measurement noise, which affects the accuracy of the integrated navigation filter. In this paper, an INS/CNS deeply integrated navigation method, which includes a deeply integrated model and a second-order state augmented H-infinity filter, is proposed to solve these problems. The INS/CNS deeply integrated navigation model optimizes the attitude based on the gray image error function, which can estimate the attitude without star identification. The second-order state augmented H-infinity filter uses the state augmentation algorithm to whiten the measurement noise caused by the aero-optical effect, which can effectively improve the estimation accuracy of the H-infinity filter in the near space environment. Simulation results show that the proposed INS/CNS deeply integrated navigation method can reduce the computational cost by 50%, while the attitude accuracy is kept within 10” (3 ). The attitude root mean square of the second-order state augmented H-infinity filter does not exceed 5”, even when the parameter error increases to 50%, in the near space environment. Therefore, the INS/CNS deeply integrated navigation method can effectively improve the rapid response ability of the navigation system and the filtering accuracy in the near space environment, providing a reference for the future design of near space vehicle navigation systems.

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Section: Introductionmentioning

confidence: 99%

INS/CNS Deeply Integrated Navigation Method of Near Space Vehicles

Sun

et al. 2020

Sensors

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“…Hence, the CNS is frequently used for the purpose of assisting the SINS in the aerospace field, utilizing the SINS/CNS integrated navigation system. The SINS/CNS integrated navigation system can correct the attitude error and the gyro drift using the starlight information and greatly improve navigation accuracy [ 11 ]. At present, it is an important developing direction for missile, airplane, and spacecraft navigation technology [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Maximum Correntropy Unscented Kalman Filter for Ballistic Missile Navigation System based on SINS/CNS Deeply Integrated Mode

Hou

Liu³

et al. 2018

Sensors

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Strap-down inertial navigation system/celestial navigation system (SINS/CNS) integrated navigation is a high precision navigation technique for ballistic missiles. The traditional navigation method has a divergence in the position error. A deeply integrated mode for SINS/CNS navigation system is proposed to improve the navigation accuracy of ballistic missile. The deeply integrated navigation principle is described and the observability of the navigation system is analyzed. The nonlinearity, as well as the large outliers and the Gaussian mixture noises, often exists during the actual navigation process, leading to the divergence phenomenon of the navigation filter. The new nonlinear Kalman filter on the basis of the maximum correntropy theory and unscented transformation, named the maximum correntropy unscented Kalman filter, is deduced, and the computational complexity is analyzed. The unscented transformation is used for restricting the nonlinearity of the system equation, and the maximum correntropy theory is used to deal with the non-Gaussian noises. Finally, numerical simulation illustrates the superiority of the proposed filter compared with the traditional unscented Kalman filter. The comparison results show that the large outliers and the influence of non-Gaussian noises for SINS/CNS deeply integrated navigation is significantly reduced through the proposed filter.

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“…In [12], with the aid of a star sensor and the two positions on the ground, the attitude and position errors can be greatly reduced by Kalman filtering, and the biases of three gyros and accelerometers can also be estimated. In [13], an integration algorithm based on the star vector observations is derived considering the star sensor installation error. Then, the star sensor installation error is on-line accurately estimated based on the filter.…”

Section: Introductionmentioning

confidence: 99%