- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Star sensor installation error calibration in stellar-inertial navigation system with a regularized backpropagation neural network
摘要： The star sensor is the attitude reference in a stellar-inertial navigation system. It is essential to acquire the star sensor installation error, which has a great influence on the system navigation performance. However, traditional methods have a poor tolerance for a large range of installation errors, especially when the system works under a separate installation mode. In this paper a novel calibration method, using a regularized backpropagation (BP) neural network, is proposed. With a specially designed calibration procedure, the neural network is structured with BP and the regularization is improved. The network training is conducted for parameter solidification. The calibration can be achieved without formula derivation and numerical calculation under both small and large installation errors. In the experiment, the calibration accuracy is about 5 arcsec under small installation errors and about 20 arcsec under large installation errors, which is much better than a Kalman filter. The proposed method has the potential to be a universal star sensor calibration method under integrative installation mode or separated installation mode with large installation error.
关键词： neural network,installation error calibration,star sensor
更新于2025-09-23 15:21:21
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Star Centroiding Based on Fast Gaussian Fitting for Star Sensors
摘要： The most accurate star centroiding method for star sensors is the Gaussian ?tting (GF) algorithm, because the intensity distribution of a star spot conforms to the Gaussian function, but the computational complexity of GF is too high for real-time applications. In this paper, we develop the fast Gaussian ?tting method (FGF), which approximates the solution of the GF in a closed-form, thus signi?cantly speeding up the GF algorithm. Based on the fast Gaussian ?tting method, a novel star centroiding algorithm is proposed, which sequentially performs the FGF twice to calculate the star centroid: the ?rst FGF step roughly calculates the Gaussian parameters of a star spot and the noise intensity of each pixel; subsequently the second FGF accurately calculates the star centroid utilizing the noise intensity provided in the ?rst step. In this way, the proposed algorithm achieves both high accuracy and high ef?ciency. Both simulated star images and star sensor images are used to verify the performance of the algorithm. Experimental results show that the accuracy of the proposed algorithm is almost the same as the GF algorithm, higher than most existing centroiding algorithms, meanwhile, the proposed algorithm is about 15 times faster than the GF algorithm, making it suitable for real-time applications.
关键词： Gaussian ?tting,real-time,star centroiding,star sensor
更新于2025-09-23 15:21:01