SINS/BDS tightly coupled integrated navigation algorithm for hypersonic vehicle

Chen, Kai; Pei, Sensen; Zeng, Cheng-zhi; Ding, Gang

doi:10.1038/s41598-022-10063-9

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…They have their own advantages and disadvantages in application. The former has strong autonomy, high short-time accuracy and continuous output, but the positioning error accumulates with time 3 – 6 ; the latter has high positioning and speed measurement accuracy, and the positioning error does not accumulate with time, but the output information is discontinuous and vulnerable to interference 7 – 9 . The combination of these two instrument can achieve complementary advantages, which can significantly improve the comprehensive performance of the navigation system.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network based on strong track and square root UKF for INS/GNSS intelligence integrated system during GPS outage

Yang,

Wang,

Zhang

et al. 2024

Sci Rep

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When INS/GNSS (inertial navigation system/global navigation satellite system) integrated system is applied, it will be affected by the insufficient number of visible satellites, and even the satellite signal will be lost completely. At this time, the positioning error of INS accumulates with time, and the navigation accuracy decreases rapidly. Therefore, in order to improve the performance of INS/GNSS integration during the satellite signals interruption, a novel learning algorithm for neural network has been presented and used for intelligence integrated system in this article. First of all, determine the input and output of neural network for intelligent integrated system and a nonlinear model for weighs updating during neural network learning has been established. Then, the neural network learning based on strong tracking and square root UKF (unscented Kalman filter) is proposed for iterations of the nonlinear model. In this algorithm, the square root of the state covariance matrix is used to replace the covariance matrix in the classical UKF to avoid the filter divergence caused by the negative definite state covariance matrix. Meanwhile, the strong tracking coefficient is introduced to adjust the filter gain in real-time and improve the tracking capability to mutation state. Finally, an improved calculation method of strong tracking coefficient is presented to reduce the computational complexity in this algorithm. The results of the simulation test and the field-positioning data show that the proposed learning algorithm could improve the calculation stability and robustness of neural network. Therefore, the error accumulation of INS/GNSS integration is effectively compensated, and then the positioning accuracy of INS/GNSS intelligence integrated system has been improved.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network based on strong track and square root UKF for INS/GNSS intelligence integrated system during GPS outage

Yang,

Wang,

Zhang

et al. 2024

Sci Rep

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show abstract

“…In recent years, many scholars and researchers have conducted in-depth research on the fault-tolerant of GNSS/SINS integrated navigation system [11,12]. The research is mainly to construct a fault detection function by analyzing the data characteristics to accurately detect the system faults.…”

Section: Introductionmentioning

confidence: 99%

A GNSS/SINS fault detection and robust adaptive algorithm based on sliding average smooth bounded layer width

Zhao,

Wang,

Gao

et al. 2024

Meas. Sci. Technol.

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The Global Navigation Satellite System/Strapdown Inertial Navigation System (GNSS/SINS) integrated navigation system is an important technology for UAV measurement and vehicle movement measurement. But in the operational process of the GNSS/SINS integrated navigation system, the Global Navigation Satellite System (GNSS) signal is vulnerable to external interference, resulting in abnormal system measurement data, and system faults. These faults will reduce the navigation and positioning performance of the system and reduce the measurement accuracy of the system. Aiming at this problem, a GNSS/SINS fault detection and robust adaptive algorithm based on sliding average smooth bounded layer width is proposed. The algorithm evaluates the system measurement data based on the innovation residual and incorporates the sliding average filter to design the fault detection function based on the smooth bounded width layer. Accurate detection of system faults using fault detection function. The fault detection function value is used to construct the robust cofactor matrix to correct the measurement error in real-time, to improve the accuracy and robustness of the state estimation. The experimental results show that: The proposed algorithm in the paper compares with two traditional robust adaptive algorithms based on smooth bounded layer fault detection and residual chi-square fault detection. The fault detection rates for small step faults show an increase of more than 44.26% and 9.54%, respectively. Similarly, for slowly varying faults, the fault detection rates exhibit an increase of more than 29.32% and 13.56%, respectively. Throughout the fault, the filtering accuracy demonstrates an increase of more than 16.52% and 15.47%, respectively. The algorithm effectively improves the measurement accuracy of the GNSS/SINS integrated navigation system.

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“…Zhang et al proposed that the thermal adaptability of photoelectric sensors could be improved by optimizing their structural design [16]. Chen et al [17] and Zhao et al [18] conducted prototype operation tests and wind tunnel tests but did not sufficiently assess the steady background radiation due to aerothermal conditions. It is necessary to evaluate the observation capability of star sensors under intense radiation near the optical aperture; this could provide quantitative guidance for improving the external environment of the observation window and optimizing sensor performance.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation Model of Star Sensor Observation Capability Under Hypersonic Aerothermal Conditions

Chen

Zheng

et al. 2023

IEEE Access

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During hypersonic flight in the atmosphere, severe aerothermal effects occur on the optical hood or observation window of an aircraft, keeping the optical window and surface flow field in a hightemperature state, as well as causing a large amount of radiation and strong background noise. These phenomena have serious effects on the observation capability of star sensors. An evaluation model of star sensor observation capability under hypersonic aerothermal conditions was constructed in this study, using a definition of the signal-to-noise ratio considering defocused intense radiation sources. Furthermore, a method of calculating the observable limiting magnitude under aerothermal conditions is given. The model was used in simulation tests involving different flight states and temperature conditions. The results showed that the thermal radiation of the aircraft observation window has the greatest influence, which is more than two orders higher than that of the outer flow near the window; when the window temperature was controlled below 750 K, for a star sensor with an SNR threshold of 3, navigation stars with magnitudes brighter than 5 could be captured in the visible band of 0.35-0.7 μm, and the near-infrared band of 0.9-1.4 μm was unavailable.

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SINS/BDS tightly coupled integrated navigation algorithm for hypersonic vehicle

Cited by 6 publications

References 15 publications

Artificial neural network based on strong track and square root UKF for INS/GNSS intelligence integrated system during GPS outage

Artificial neural network based on strong track and square root UKF for INS/GNSS intelligence integrated system during GPS outage

A GNSS/SINS fault detection and robust adaptive algorithm based on sliding average smooth bounded layer width

An Evaluation Model of Star Sensor Observation Capability Under Hypersonic Aerothermal Conditions

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