Convolutional Neural Networks for Inference of Space Object Attitude Status

Badura, Gregory; Valenta, Christopher R.; Gunter, B. C.

doi:10.1007/s40295-022-00309-z

Cited by 5 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The motion and brightness of each object over time is key to classifying the object class. Badura’s [ 13 ] implementation of CNN demonstrates how recent advances in artificial-intelligence techniques can be used for SDA applications, by classifying objects and their attitude status with light curves from images. It is also clear that RSO position and velocity can be estimated [ 7 , 14 ].…”

Section: Relevant Workmentioning

confidence: 99%

RSOnet: An Image-Processing Framework for a Dual-Purpose Star Tracker as an Opportunistic Space Surveillance Sensor

Dave

Clark

Lee

2022

Sensors

View full text Add to dashboard Cite

A catalogue of over 22,000 objects in Earth’s orbit is currently maintained, and that number is expected to double within the next decade. Novel data collection regimes are needed to scale our ability to detect, track, classify and characterize resident space objects in a crowded low Earth orbit. This research presents RSOnet, an image-processing framework for space domain awareness using star trackers. Star trackers are cost-effective, flight proven, and require basic image processing to be used as an attitude-determination sensor. RSOnet is designed to augment the capabilities of a star tracker by becoming an opportunistic space-surveillance sensor. Our research demonstrates that star trackers are a feasible source for RSO detections in LEO by demonstrating the performance of RSOnet on real detections from a star-tracker-like imager in space. RSOnet convolutional-neural-network model architecture, graph-based multi-object classifier and characterization results are described in this paper.

show abstract

Section: Relevant Workmentioning

confidence: 99%

RSOnet: An Image-Processing Framework for a Dual-Purpose Star Tracker as an Opportunistic Space Surveillance Sensor

Dave

Clark

Lee

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…10,11 High fidelity physics-based models for generation of spectral signatures as a function of time (spectro-temporal signature) can yield significant improvements in machine learning model performance, and also lead to predictable false alarm rates for certain observer-illuminator geometries. 12,13 However, extending terrestrial approaches to remote sensing of URSOs is challenged by the limited number of observations when compared to terrestrial applications. For instance, as illustrated by members of the team in, 14 the high spectral resolution spectro-temporal signature of a URSO when compared to the spectro-spatial information on terrestrial images may not be informative enough to perform a common task in hyperspectral remote sensing like unsupervised unmixing.…”

Section: Introductionmentioning

confidence: 99%

Using neural networks to classify hyperspectral signatures of unresolved resident space objects

Cedillo,

Acosta,

Velez-Reyes

et al. 2024

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX

View full text Add to dashboard Cite

This work advances Space Situational Awareness (SSA) by analyzing ground-based hyper/multispectral images of Unresolved Resident Space Objects (URSO). Machine-learning models are constructed for satellite classification using unresolved spectral imagery. The study uses simulated data of observations of nine distinct satellites retrieved from the U.S. Space Force Unified Data Library (UDL). The dataset consists of unresolved hyperspectral imagery of satellites in different poses collected with a slitless spectrograph imager. The slitless spectrograph allows the collection of the spectral signature of the URSO with a single focal plane array in the 630 nm to 980 nm spectral range. In practice, the expectation is to have more unlabeled than labeled samples. Thus, the proposed classifier leverages the concept of Semi-Supervised machine learning. The network architecture leverages image reconstruction via a Convolutional AutoEncoder (CAE) and Multi-layer Perceptron (MLP) for classification. The architecture consists of three submodels: an Encoder, a Decoder (the two traditional components of a CAE), and a separate MLP. The Decoder and MLP use the Encoder’s low-dimensional representation of the samples as their input. The Encoder performs the task of creating the shared latent space via dimensionality reduction of the spectral data. The CAE (Encoder/Decoder) can be trained on labeled and unlabeled data, while the MLP requires labeled data for training. The newly developed classification models achieve a mean validation accuracy of 84% and a mean testing accuracy of 82%, utilizing 10-fold cross-validation. Additionally, the decoder achieves low image reconstruction error with a mean test error of 0.008, measured by Mean Squared Error, over the cross-validation folds. This network architecture demonstrates the ability to map slitless spectral data for accurate identification of RSO despite the challenges of using a limited-size and unbalanced dataset.

show abstract

“…Light curves have also been used for estimating ballistic coefficients for LEO RSOs, as proven in [ 6 ]. Additionally, light curves can be simulated as was performed by [ 7 , 8 , 9 , 10 ], which allows researchers to obtain large quantities of data on which to perform analyses.…”

Section: Introductionmentioning

confidence: 99%

“…There are many studies on light curve simulations, such as those in [ 7 , 8 , 9 , 10 ]. Several light curve classifiers are trained on simulated data with the expectation that it would closely match real light curves but, as indicated in [ 19 ], this is hardly the case.…”

Section: Introductionmentioning

confidence: 99%

Classification of Low Earth Orbit (LEO) Resident Space Objects’ (RSO) Light Curves Using a Support Vector Machine (SVM) and Long Short-Term Memory (LSTM)

Qashoa,

Lee

2023

Sensors

View full text Add to dashboard Cite

Light curves are plots of brightness measured over time. In the field of Space Situational Awareness (SSA), light curves of Resident Space Objects (RSOs) can be utilized to infer information about an RSO such as the type of object, its attitude, and its shape. Light curves of RSOs in geostationary orbit (GEO) have been a main research focus for many years due to the availability of long time series data spanning hours. Given that a large portion of RSOs are in low Earth orbit (LEO), it is of great importance to study trends in LEO light curves as well. The challenge with LEO light curves is that they tend to be short, typically no longer than a few minutes, which makes them difficult to analyze with typical time series techniques. This study presents a novel approach to observational LEO light curve classification. We extract features from light curves using a wavelet scattering transformation which is used as an input for a machine learning classifier. We performed light curve classification using both a conventional machine learning approach, namely a support vector machine (SVM), and a deep learning technique, long short-term memory (LSTM), to compare the results. LSTM outperforms SVM for LEO light curve classification with a 92% accuracy. This proves the viability of RSO classification by object type and spin rate from real LEO light curves.

show abstract

Convolutional Neural Networks for Inference of Space Object Attitude Status

Cited by 5 publications

References 32 publications

RSOnet: An Image-Processing Framework for a Dual-Purpose Star Tracker as an Opportunistic Space Surveillance Sensor

RSOnet: An Image-Processing Framework for a Dual-Purpose Star Tracker as an Opportunistic Space Surveillance Sensor

Using neural networks to classify hyperspectral signatures of unresolved resident space objects

Classification of Low Earth Orbit (LEO) Resident Space Objects’ (RSO) Light Curves Using a Support Vector Machine (SVM) and Long Short-Term Memory (LSTM)

Contact Info

Product

Resources

About