Noise suppression algorithm of short-wave infrared star image for daytime star sensor

Wang, Wenjie; Wei, Xinguo; Li, Jian; Wang, Gangyi

doi:10.1016/j.infrared.2017.08.002

Cited by 32 publications

(10 citation statements)

References 14 publications

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“…Lots of data processing methods for 2D data can be employed. For example, the dim target detection can be used to identify the Phobos and the Deimos of Mars in space; the image denoising [56] can be considered for the noise inhibition; the spectral analysis can be employed to assist the atmosphere component analysis; and the image quality evaluation can also be used for the celestial body recognition. The visual navigation also adopts the imaging sensor to percept the environment.…”

Section: The Autonomous Navigation Algorithmsmentioning

confidence: 99%

Autonomous Navigation for Mars Exploration

Liu¹

2020

Mars Exploration - A Step Forward

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The autonomous navigation technology uses the multiple sensors to percept and estimate the spatial locations of the aerospace prober or the Mars rover and to guide their motions in the orbit or the Mars surface. In this chapter, the autonomous navigation methods for the Mars exploration are reviewed. First, the current development status of the autonomous navigation technology is summarized. The popular autonomous navigation methods, such as the inertial navigation, the celestial navigation, the visual navigation, and the integrated navigation, are introduced. Second, the application of the autonomous navigation technology for the Mars exploration is presented. The corresponding issues in the Entry Descent and Landing (EDL) phase and the Mars surface roving phase are mainly discussed. Third, some challenges and development trends of the autonomous navigation technology are also addressed.

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Section: The Autonomous Navigation Algorithmsmentioning

confidence: 99%

Autonomous Navigation for Mars Exploration

Liu¹

2020

Mars Exploration - A Step Forward

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“…The three-axis attitude and spatial position of the star sensor can be calculated based on the reference of stars recognized in the digitized star image. Being able to accurately collect and process a star image at all times, however, is one of the challenges in the application of a star sensor due to the brightness of the sky’s background and the complicated mixture of noise [ 1 ]. These conditions lead to a low Signal-to-Noise Ratio (SNR) in the star image obtained in the daytime, and thus have negative influences on the calculation of attitude and position.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, it is an important basis for the star sensor to work efficiently that noise is effectively suppressed and the star targets are accurately recognized in the star image. Researchers have proposed various types of algorithms to suppress the effects of salt-and-pepper noise, strip noise, speckle noise, and defective pixels [ 1 , 2 , 3 , 4 , 5 ]. Nevertheless, mixed Poisson–Gaussian noise remains after these denoising methods are applied and affects the extraction of the true value of star points [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Frame Star Image Denoising Algorithm Based on Deep Reinforcement Learning and Mixed Poisson–Gaussian Likelihood

Xie

Zhang

Zheng

et al. 2020

Sensors

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Mixed Poisson–Gaussian noise exists in the star images and is difficult to be effectively suppressed via maximum likelihood estimation (MLE) method due to its complicated likelihood function. In this article, the MLE method is incorporated with a state-of-the-art machine learning algorithm in order to achieve accurate restoration results. By applying the mixed Poisson–Gaussian likelihood function as the reward function of a reinforcement learning algorithm, an agent is able to form the restored image that achieves the maximum value of the complex likelihood function through the Markov Decision Process (MDP). In order to provide the appropriate parameter settings of the denoising model, the key hyperparameters of the model and their influences on denoising results are tested through simulated experiments. The model is then compared with two existing star image denoising methods so as to verify its performance. The experiment results indicate that this algorithm based on reinforcement learning is able to suppress the mixed Poisson–Gaussian noise in the star image more accurately than the traditional MLE method, as well as the method based on the deep convolutional neural network (DCNN).

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“…Celestial navigation calculates the attitude of aircraft by measuring stars which are firmly fixed in the inertial space, such that navigation error does not accumulate with time. Star sensors are widely used in modern celestial navigation systems, which capture images of stars and calculate the attitude according to the star point locations [ 12 ]. However, star sensors are vulnerable to environmental impacts.…”

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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Noise suppression algorithm of short-wave infrared star image for daytime star sensor

Cited by 32 publications

References 14 publications

Autonomous Navigation for Mars Exploration

Autonomous Navigation for Mars Exploration

Multi-Frame Star Image Denoising Algorithm Based on Deep Reinforcement Learning and Mixed Poisson–Gaussian Likelihood

INS/CNS Deeply Integrated Navigation Method of Near Space Vehicles

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