An architecture for a visual-based PNT alternative

Critchley-Marrows, Joshua; Wu, Xiaofeng; Cairns, Iver H.

doi:10.1016/j.actaastro.2023.05.022

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…In this case, the misalignment factor between the two sensors can be estimated up to the performance of each sensor acting as a star tracker. The performance of this correction is estimated to be 0.01 • [35,36], and so this will be used as the angle θ mis in this work. It is important to note that this estimate would require both sensors to have a star field in view, and so spacecraft pointing would be required.…”

Section: Star Trackermentioning

confidence: 99%

“…Errors in the estimated horizon point, such as stray light, optical noise, or large rugged terrain, are introduced in the estimated limb pixel (u, v). The estimated performance of a measured horizon coordinate can be assumed to be 0.1 px [11,36,40]. It is suggested that upon implementation the optical system hardware developed reflects this estimation resolution.…”

Section: Horizon Navigationmentioning

confidence: 99%

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Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System

Critchley-Marrows,

Wu,

Kawabata

et al. 2024

Aerospace

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In recent years, the number of expected missions to the Moon has increased significantly. With limited terrestrial-based infrastructure to support this number of missions, as well as restricted visibility over intended mission areas, there is a need for space navigation system autonomy. Autonomous on-board navigation systems in the lunar environment have been the subject of study by a number of authors. Suggested systems include optical navigation, high-sensitivity Global Navigation Satellite System (GNSS) receivers, and navigation-linked formation flying. This paper studies the interoperable nature and fusion of proposed autonomous navigation systems that are independent of Earth infrastructure, given challenges in distance and visibility. This capability is critically important for safe and resilient mission architectures. The proposed elliptical frozen orbits of lunar navigation satellite systems will be of special interest, investigating the derivation of orbit determination by non-terrestrial sources utilizing celestial observations and inter-satellite links. Potential orbit determination performances around 100 m are demonstrated, highlighting the potential of the approach for future lunar navigation infrastructure.

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Section: Star Trackermentioning

confidence: 99%

Section: Horizon Navigationmentioning

confidence: 99%

Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System

Critchley-Marrows,

Wu,

Kawabata

et al. 2024

Aerospace

Self Cite

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“…With the introduction of numerous new concepts, methods, and theories, image processing technology based on deep learning has progressively matured. Visual navigation technology is expected to be developed and widely used in the fields of aircraft, unmanned aerial vehicles, various cruise missiles, deep space probes, indoor mobile navigation, and so on [7]. Therefore, visual navigation technology has high research and application value in the field of navigation.…”

Section: Introductionmentioning

confidence: 99%

FLsM: Fuzzy Localization of Image Scenes Based on Large Models

Chen,

Miao,

Gui

et al. 2024

Electronics

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This article primarily focuses on the study of image-based localization technology. While traditional methods have made significant advancements in technology and applications, the emerging field of visual image-based localization technology demonstrates tremendous potential for research. Deep learning has exhibited a strong performance in image processing, particularly in developing visual navigation and localization techniques using large-scale visual models. This paper introduces a sophisticated scene image localization technique based on large models in a vast spatial sample environment. The study involved training convolutional neural networks using millions of geographically labeled images, extracting image position information using large model algorithms, and collecting sample data under various conditions in elastic scene space. Through visual computation, the shooting position of photos was inferred to obtain the approximate position information of users. This method utilizes geographic location information to classify images and combines it with landmarks, natural features, and architectural styles to determine their locations. The experimental results show variations in positioning accuracy among different models, with the most optimal model obtained through training on a large-scale dataset. They also indicate that the positioning error in urban street-based images is relatively small, whereas the positioning effect in outdoor and local scenes, especially in large-scale spatial environments, is limited. This suggests that the location information of users can be effectively determined through the utilization of geographic data, to classify images and incorporate landmarks, natural features, and architectural styles. The study’s experimentation indicates the variation in positioning accuracy among different models, highlighting the significance of training on a large-scale dataset for optimal results. Furthermore, it highlights the contrasting impact on urban street-based images versus outdoor and local scenes in large-scale spatial environments.

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