Light Curve Analysis Using Wavelets

Dianetti, Andrew D.

doi:10.2514/6.2018-1605

Cited by 5 publications

(2 citation statements)

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“…This scaling function is time dependent and enables the temporal analysis. It has been previously shown that this temporal component enables wavelets to be used for assessment of SO attitude and rotation state changes [11,12].…”

Section: Introductionmentioning

confidence: 99%

Satellite attitude activity identification using change-point and wavelet analysis

Kwitowski,

Chapman,

Dianetti

et al. 2024

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

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The ability to detect satellite maneuvers is an integral part of space domain awareness. While satellite maneuver detection typically refers to orbital maneuvers, the ability to detect a change in attitude or spin state is equally important. In many cases, these attitude maneuvers are the predecessor to orbital maneuvers, and can serve as an indication of future behaviors. One means of assessing a satellites attitude and spin state involves analysis of the object's light curve, which is the temporal history of reflected light off the satellite and collected by an observer. Model-based approaches have been demonstrated to be excellent tools for eliciting attitude information from light curves, but the results are heavily dependent on the accuracy of the input model. An alternative approach, based on a wavelet decomposition of the light curve signal, has been used to identify attitude activity without the need for a well-defined satellite model. Wavelet decomposition allows the frequency of a signal to be assessed over time, providing critical insight into temporal changes of the signal. When applied to a light curve, these changes can be indicative of attitude activities. This paper focuses on methods to determine and assert that a change has occurred using the light curve signals and wavelet decomposition. Various approaches to identify changes are discussed and compared. The practical application of these methods on noisy sensor data under real collection scenarios is also discussed. Use cases considered include a simulation study as well as real world data collection on GOES-16, a Geostationary satellite, during multiple phases of its lifetime.

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Section: Introductionmentioning

confidence: 99%

Satellite attitude activity identification using change-point and wavelet analysis

Kwitowski,

Chapman,

Dianetti

et al. 2024

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

View full text Add to dashboard Cite

show abstract

“…At the same time, the use of hyperspectral technology and spectrometry have found important applications in astronomy, to classify and characterise asteroids [8] for example. In parallel, a body of work has focused on the use of light curve analysis to reconstruct the attitude motion or the shape of space objects [9][10][11][12][13][14]. More recently, Machine Learning (ML) was introduced to classify objects starting from their light curves [15,16].…”

Section: Introductionmentioning

confidence: 99%