SINS/RCNS integrated navigation method based on LSTM algorithm for aerospace vehicle

doi:10.23919/JSEE.2026.000054

Abstract

Abstract:

We propose a deep-learning-assisted strapdown inertial navigation system (SINS)/refraction celestial navigation system (RCNS) integrated navigation method to control the adverse effects of atmospheric density errors on the accuracy of stellar refraction navigation and enhance the reliability of SINS/RCNS integrated navigation for aerospace vehicles. This method utilizes satellite navigation data and a long short-term memory network to establish a mapping relationship between the navigation moments, refraction angles, and the apparent height errors. Using deep learning algorithm to address complex time-series prediction problems, thereby compensates the impact of atmospheric density deviations on star sensor measurements. Simulation experiments of vehicle navigation in scenarios with atmospheric density errors are conducted using this method. The results show that the deep learning scheme can effectively resist the adverse effects of atmospheric density errors on navigation, demonstrating strong reliability.

Key words: deep learning, long short-term memory (LSTM) network, stellar refraction navigation, atmospheric density error, inertial navigation

Shuning YANG, Dingjie WANG, Hongbo ZHANG, Guojian TANG. SINS/RCNS integrated navigation method based on LSTM algorithm for aerospace vehicle[J]. Journal of Systems Engineering and Electronics, 2026, 37(3): 1030-1041.

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Device	Parameter	Value
SINS	Accelerometer bias	1×10⁻⁵ g
	Accelerometer bias stability	1×10⁻⁵ g
	Gyroscope bias/(°/h)	1×10⁻²
	Gyroscope bias stability/(°/h)	5×10⁻³
GNSS	Pseudo range/m	5
GNSS	Pseudo range variability/(m/s)	0.2
Star sensor	Field/(°)	12×12
	Pixel	1024×1024
	Installation misalignment angle accuracy/(″)	10
	Star pixel coordinate extraction accuracy/pixel	0.01

Error	Positioning/m	Velocity/(m/s)	Attitude/(″)
No error	89.75	0.11	6.28
3% error	444.20	0.50	5.74
10% error	863.71	1.04	6.34

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