Active Debris Removal Mission Planning Method Based on Machine Learning

Xu, Yingjie; Liu, Xiaolu; He, Renjie; Zhu, Yue-he; Zuo, Yahui; He, Lei

doi:10.3390/math11061419

Cited by 5 publications

(4 citation statements)

References 52 publications

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“…However, it is important to acknowledge the study's limitations, including potential challenges in accurately representing complex orbital debris distributions with neural networks. Another study (Xu et al, 2023) conducted an analysis of active debris removal (ADR) mission planning, aiming to generate optimal debris removal plans. They established a two-layer time-dependent traveling salesman problem (TSP) mathematical model to address debris removal sequence and transfer trajectory planning.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Space Debris detection and monitoring using a hybrid Bi-LSTM- CNN and Bayesian Optimization

Priyadarshini

2024

Preprint

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Monitoring space debris is vital for ensuring the safety of space missions and satellite operations amid the increasing number of satellites and spacecraft in orbit. The study addresses this challenge by proposing a novel approach based on a hybrid Bi-LSTM-CNN architecture optimized using Bayesian Optimization. Through extensive analysis utilizing machine learning and deep learning techniques, the study develops a robust space debris detection system capable of classifying both the object type and Radar Cross Section (RCS) size. The proposed method outperforms existing approaches by demonstrating superior performance across multiple evaluation metrics, including accuracy, precision, recall, and F1 score. Moreover, the study considers the practical aspect of training time, ensuring efficiency in real-time applications. Empirical validation on real-world datasets confirms the effectiveness and efficiency of the hybrid model in accurately detecting and predicting space debris types. Overall, this research significantly advances space debris monitoring capabilities, mitigating risks associated with space exploration and satellite operations, and offers comprehensive insights into potential hazards and optimizing mitigation strategies.

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Section: Related Workmentioning

confidence: 99%

Enhanced Space Debris detection and monitoring using a hybrid Bi-LSTM- CNN and Bayesian Optimization

Priyadarshini

2024

Preprint

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“…The energy function to take the shortest path and satisfy all constraints [45,46] is shown in Equation (25). Where V xi denotes that the xth city is visited on the ith order and d xy is the distance between cities x and y.…”

Section: The Effect Of Blue Noise On the Optimization Power Of Neural...mentioning

confidence: 99%

“…In this paper, we use 10 cities after classical normalization, with the following coordinates: (0. shortest loop required to visit each city once and return to the starting city, given the distance between two cities. The energy function to take the shortest path and satisfy all constraints [45,46] is shown in Equation (25). Where Vxi denotes that the xth city is visited on the ith order and dxy is the distance between cities x and y.…”

Section: The Effect Of Blue Noise On the Optimization Power Of Neural...mentioning

confidence: 99%

“…Let the simulation program take the 30 cities after classical normalization. Let the emulator take the classic 30 cities [48], and the coordinates are as follows: (41,94); (37,84) (45,21); (41,26); (44,35); and (4,50). The shortest path of the 30-city TSP problem is 423.7406, and its shortest path is shown in Figure 8.…”

Section: Chaotic Neural Network Model Based On Blue Noisementioning

confidence: 99%

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The Effect of Blue Noise on the Optimization Ability of Hopfield Neural Network

Zhang

Chen

et al. 2023

Applied Sciences

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Noise is ubiquitous in the real-world environment. At present, most scholars only include the stage of Gaussian white noise when applying noise in neural networks and regard white noise as a tool to optimize the network model, which is far from enough, because noise not only affects the optimization ability of the Hopfield neural network but can also better fit the needs of the actual use of the scene. Therefore, according to the problems in the existing research, a method is proposed to combine the neural network with colored noise according to the signal-to-noise ratio. Taking blue noise as an example, the anti-interference ability of the Hopfield neural network regarding colored noise is studied. The results show that for the Hopfield neural network driven by blue noise, by adjusting the neural network step size, excitation function and signal-to-noise ratio, it not only provides ideas for adding colored noise to the neural network but also enables the neural network model to have better optimization-seeking ability. The research results have some reference significance for improving the practical application of neural networks in noisy environments.

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