Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Massimi, Federica; Ferrara, Pasquale; Benedetto, Francesco

doi:10.3390/s23010124

Cited by 7 publications

(6 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of this study will be used for implementation onboard a satellite for real-time resident space object (RSO) detection in the long term. The non-AI-based methods outlined in [ 20 ] have not been evaluated with space situational awareness (SSA) images. Given the current limitations, a direct comparison among the existing algorithms is not feasible at this time.…”

Section: Rso Detection Methodsmentioning

confidence: 99%

“…The application of most of the methods described above to RSO detection has not been extensively studied, and no conclusive results are available. A recent paper by Massimi et al offered the most comprehensive survey on the subject [ 20 ]. The authors classified methods into non-AI- and AI-based methods.…”

Section: Survey Of Object Detection Algorithms In Ssa Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Comparative Analysis of Resident Space Object (RSO) Detection Methods

Suthakar,

Sanvido,

Qashoa

et al. 2023

Sensors

View full text Add to dashboard Cite

In recent years, there has been a significant increase in satellite launches, resulting in a proliferation of satellites in our near-Earth space environment. This surge has led to a multitude of resident space objects (RSOs). Thus, detecting RSOs is a crucial element of monitoring these objects and plays an important role in preventing collisions between them. Optical images captured from spacecraft and with ground-based telescopes provide valuable information for RSO detection and identification, thereby enhancing space situational awareness (SSA). However, datasets are not publicly available due to their sensitive nature. This scarcity of data has hindered the development of detection algorithms. In this paper, we present annotated RSO images, which constitute an internally curated dataset obtained from a low-resolution wide-field-of-view imager on a stratospheric balloon. In addition, we examine several frame differencing techniques, namely, adjacent frame differencing, median frame differencing, proximity filtering and tracking, and a streak detection method. These algorithms were applied to annotated images to detect RSOs. The proposed algorithms achieved a competitive degree of success with precision scores of 73%, 95%, 95%, and 100% and F1 scores of 68%, 77%, 82%, and 79%.

show abstract

Section: Rso Detection Methodsmentioning

confidence: 99%

Section: Survey Of Object Detection Algorithms In Ssa Applicationsmentioning

confidence: 99%

Comparative Analysis of Resident Space Object (RSO) Detection Methods

Suthakar,

Sanvido,

Qashoa

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Convolutional Neural Networks (CNNs) are commonly used in SSA because they excel in image recognition and object detection tasks, which are crucial in identifying and tracking SO. Convolutional Neural Networks are capable of learning complex features and patterns from large amounts of data, making them well‐suited for analysing images captured by telescopes or radar systems [21]. In ref.…”

Section: Related Workmentioning

confidence: 99%

Deep learning‐based space debris detection for space situational awareness: A feasibility study applied to the radar processing

Massimi,

Ferrara,

Petrucci

et al. 2024

IET Radar Sonar & Navi

View full text Add to dashboard Cite

The increasing number of space objects (SO), debris, and constellation of satellites in Low Earth Orbit poses a significant threat to the sustainability and safety of space operations, which must be carefully and efficiently addressed to avoid mutual collisions. The space situational awareness is currently addressed by an ensemble of radar and radio‐telescopes that detect and track SO. However, a large part of space debris is composed of very small and tiny metallic objects, very difficult to detect. The authors demonstrate the benefits of using deep learning (DL) architectures for small space object detection by radar observations. TIRA radio telescope has been simulated to generate range‐Doppler maps, then used as inputs for object detection exploiting You‐Only‐Look‐Once (YOLO) frameworks. The results demonstrate that the object detection by using YOLO algorithms outperform conventional target detection approaches, thus indicating the potential benefits of using DL techniques for space surveillance applications.

show abstract

“…Many IoT applications use Deep Neural Networks (DNNs), especially Convolutional Neural Networks (CNNs) to provide Artificial Intelligence (AI) services, such as image classification, object detection, text processing, etc. [31][32][33]. Complex CNN faces significant demands for computing resources, and it generally cannot be executed by an edge server independently.…”

Section: Introductionmentioning

confidence: 99%

Edge-Distributed IoT Services Assist the Economic Sustainability of LEO Satellite Constellation Construction

Zhang,

Shi,

2024

Sustainability

View full text Add to dashboard Cite

There are thousands or even tens of thousands of satellites in Low Earth Orbit (LEO). How to ensure the economic sustainability of LEO satellite constellation construction is an important issue currently. In this article, we envision integrating the popular and promising Internet of Things (IoT) technology with LEO satellite constellations to indirectly provide economic support for LEO satellite construction through paid IoT services. Of course, this can also bring benefits to the development of IoT. LEO Satellites can provide networks for IoT products in areas with difficult conditions, such as deserts, oceans, etc., and Satellite Edge Computing (SEC) can help to reduce the service latency of IoT. Many IoT products rely on Convolutional Neural Networks (CNNs) to provide services, and it is difficult to perform CNN inference on an edge server solely. Therefore, in this article, we use edge-distributed inference to enable the IoT services in the SEC scenario. How to perform edge-distributed inference to shorten inference time is a challenge. To shorten the inference latency of CNN, we propose a framework based on a joint partition, named EDIJP. We use a joint partition method combining data partition and model partition for distributed partition. We model the data partition as a Linear Programming (LP) problem. To address the challenge of trading off computation latency and communication latency, we designed an iterative algorithm to obtain the final partitioning result. By maintaining the original structure and parameters, our framework ensures that the inference accuracy will not be affected. We simulated the SEC environment, based on two popular CNN models, VGG16 and AlexNet, the performance of our method is varified. Compared with local inference, EdgeFlow, and CoEdge, the inference latency by using EDIJP is shorter.

show abstract

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Cited by 7 publications

References 94 publications

Comparative Analysis of Resident Space Object (RSO) Detection Methods

Comparative Analysis of Resident Space Object (RSO) Detection Methods

Deep learning‐based space debris detection for space situational awareness: A feasibility study applied to the radar processing

Edge-Distributed IoT Services Assist the Economic Sustainability of LEO Satellite Constellation Construction

Contact Info

Product

Resources

About