Diffusion-based cooperative space object tracking

Jia, Bin; Pham, Khanh; Blasch, Erik; Chen, Genshe; Shen, Dan

doi:10.1117/1.oe.58.4.041607

Cited by 10 publications

(2 citation statements)

References 27 publications

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“…In the future, a multi-player game theory with adversarial network with SSA will be employed to enhance the deep learning for sensor management, combined tracking, as well as secure communications. Methods for diffusion-based cooperative space object tracking [16] and block chain [17] are emerging as methods for game-theoretical methods.…”

Section: Discussionmentioning

confidence: 99%

Game Theoretic Training Enabled Deep Learning Solutions for Rapid Discovery of Satellite Behaviors

Shen¹,

Sheaff²,

Chen³

et al. 2021

Satellite Systems - Design, Modeling, Simulation and Analysis

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The chapter presents a game theoretic training model enabling a deep learning solution for rapid discovery of satellite behaviors from collected sensor data. The solution has two parts, namely, Part 1 and Part 2. Part 1 is a PE game model that enables data augmentation method, and Part 2 uses convolutional neural networks (CNNs) for satellite behavior classification. The sensor data are propagated with the various maneuver strategies from the proposed space game models. Under the PE game theoretic framework, various satellite behaviors are simulated to generate synthetic datasets with labels for the training to detect space object behaviors. To evaluate the performance of the proposed PE model, a CNN model is designed and implemented for satellite behavior classification. Python 3 and TensorFlow are used in this implementation. The simulation results show that the trained machine learning model can efficiently and correctly classify the satellite behaviors up to 99.8%.

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Section: Discussionmentioning

confidence: 99%

Game Theoretic Training Enabled Deep Learning Solutions for Rapid Discovery of Satellite Behaviors

Shen¹,

Sheaff²,

Chen³

et al. 2021

Satellite Systems - Design, Modeling, Simulation and Analysis

Self Cite

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“…To evaluate the performance of UKF algorithm, three sample LEO debris are selected upon available TLEs, as listed in TABLE 2. As mentioned earlier, the challenge of precise initial orbit determination finds a practical solution by employing DPOV scheme within a distributed network of space-borne optical sensors [49,50]. This approach significantly improves the initial orbit determination accuracy due to the increased observability provided by the tracking system.…”

Section: Estimation Algorithm Performance Evaluationmentioning

confidence: 99%

A PCA-SVM Regression Model for LEO Space Debris Orbit Prediction in an Optical Space-Based Space Surveillance Network

Hamidian,

Kosari

2024

IEEE Access

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The primary objective of this study is to investigate the efficacy of Support Vector Machine (SVM) regression method to enhance the accuracy of Low Earth Orbit (LEO) space debris orbit prediction using the historical data. Principal Component Analysis (PCA) is employed to efficiently reduce the dimensionality of dataset's feature space and hence optimize the model's performance. This investigation is motivated by the limitations of conventional orbit prediction methods, which often rely on dynamic models with unknown coefficients of perturbation forces and other relevant characteristics of space debris, leading to errors during the prediction process. On the other hand, while the Collision Avoidance Maneuver (CAM) strategy remains crucial for mitigating the threat posed by such debris, precise knowledge of debris coordinates is essential for effective CAM implementation. However, traditional ground-based optical equipment encounters challenges in observing fastmoving debris within the dynamic LEO environment, including atmospheric interference and limited Field of View (FOV). To address these limitations, the secondary objective of this study involves exploring the potential of an in-orbit optical space surveillance network as a promising solution. The system utilizes optical sensors distributed across multiple spacecraft within the Above the Horizon (ATH) constellation, specifically designed to continuously monitor the most densely populated altitude band in LEO. Simulations under different conditions demonstrate that the proposed scheme successfully complements groundbased equipment and dynamic models for debris tracking, thereby improving orbit prediction accuracy. The results of simulations under different conditions demonstrate that proposed scheme successfully complements ground-based equipment and dynamic models for debris tracking, and improving orbit prediction accuracy.

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Dynamic Data-Driven Distribution Tracking of Nanoparticle Morphology

Park

Ding

2020

Lecture Notes in Computer Science

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Diffusion-based cooperative space object tracking

Cited by 10 publications

References 27 publications

Game Theoretic Training Enabled Deep Learning Solutions for Rapid Discovery of Satellite Behaviors

Game Theoretic Training Enabled Deep Learning Solutions for Rapid Discovery of Satellite Behaviors

A PCA-SVM Regression Model for LEO Space Debris Orbit Prediction in an Optical Space-Based Space Surveillance Network

Dynamic Data-Driven Distribution Tracking of Nanoparticle Morphology

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