Attitude Determination from Light Curves

Wetterer, Charles J.; Jah, Moriba

doi:10.2514/1.44254

Cited by 64 publications

(19 citation statements)

References 5 publications

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“…The synthetically generated measurements are corrupted by timeinvariant, zero-mean Gaussian white noise whose covariance is represented in the visual magnitude scale based on historical observations. The authors of that work concluded that more accurate measurement models could alleviate discrepancies between observational and simulated data [12]. To mitigate these issues, this paper expands upon [13] to define a new radiometric measurement noise model, based on SO and environmental parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The first published work outlining the potential application of light-curve inversion to SO characterization was given by Hall et al in 2005 [6]. Wetterer and Jah [12] provided simulated results for nonmaneuvering SOs using a single unscented Kalman filter (UKF) to estimate the SO attitude, the shape model, or both simultaneously. The synthetically generated measurements are corrupted by timeinvariant, zero-mean Gaussian white noise whose covariance is represented in the visual magnitude scale based on historical observations.…”

Section: Introductionmentioning

confidence: 99%

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Three-Degree-of-Freedom Estimation of Agile Space Objects Using Marginalized Particle Filters

Coder

Holzinger

Linares

2018

Journal of Guidance, Control, and Dynamics

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Several innovations are introduced for space object attitude estimation using light-curve measurements. A radiometric measurement noise model is developed to define the observation uncertainty in terms of optical, environmental, space object, and sensor parameters and is validated using experimental data. Additionally, a correlated process noise model is introduced to represent the angular acceleration dynamics. This model is used to account for the unknown inertia and body torques of agile space objects. This linear dynamics model enables the implementation of marginalized particle filters, affording computationally tractable three-degree-of-freedom Bayesian estimation. The synthesis of these novel approaches enables the estimation of attitude and angular velocity states of maneuvering space objects without a priori knowledge of initial attitude while maintaining computational tractability. Simulated results are presented for the full three-degree-of-freedom agile space object attitude estimation problem.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three-Degree-of-Freedom Estimation of Agile Space Objects Using Marginalized Particle Filters

Coder

Holzinger

Linares

2018

Journal of Guidance, Control, and Dynamics

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“…Attitude estimation using light curve data has been demonstrated in Ref. 17. The main goal of the present work is to use light curve data to, autonomously and in near realtime, estimate the shape of an RSO along with its rotational (including attitude) and translational states.…”

Section: Introductionmentioning

confidence: 99%

Space Object Shape Characterization and Tracking Using Light Curve and Angles Data

Linares

Jah

Crassidis

et al. 2014

Journal of Guidance, Control, and Dynamics

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This paper presents a new method, based on a multiple-model adaptive estimation approach, to determine the most probable shape of a resident space object among a number of candidate shape models while simultaneously recovering the observed resident space object's inertial orientation and trajectory. Multiple-model adaptive estimation uses a parallel bank of filters, each operating under a different hypothesis to determine an estimate of the physical system under consideration. In this work, the shape model of the resident space object constitutes the hypothesis. Estimates of the likelihood of each hypothesis given the available measurements are provided from the multiple-model adaptive estimation approach. The multiplemodel adaptive estimation state estimates are determined using a weighted average of the individual filter estimates, whereas the shape estimate is selected as the shape model with the highest likelihood. Each filter employs the Unscented estimation approach, reducing passively-collected electrooptical data to infer the unknown state vector comprised of the resident

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“…One such attribute of interest is the spacecraft's attitude state. Traditional estimation methods such as Kalman Filtering have been used to estimate attitude from light curve measurements [1]. However, such an approach requires knowledge of the object's shape, as well as an assumed reflectance model.…”

Section: Introductionmentioning

confidence: 99%

Light Curve Analysis Using Wavelets

Dianetti

2018

2018 AIAA Guidance, Navigation, and Control Conference

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A method is presented that uses wavelet decomposition in the analysis of light curves. Wavelet decomposition allows the frequency of a signal to be determined over time, and when applied to the light curve reflected by a space object, is used to determine the angular rate over time. This allows for detection in changes of the spacecraft's attitude state. Several cases of attitude maneuvers are presented, and the wavelet decomposition process is demonstrated using simulated and experimentally obtained data. The process is also applied to real observations of the GOES-16 satellite. Wavelets allow for better temporal and frequency resolution compared to traditional frequency-domain methods such as the Short-Time Fourier Transform.

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Attitude Determination from Light Curves

Cited by 64 publications

References 5 publications

Three-Degree-of-Freedom Estimation of Agile Space Objects Using Marginalized Particle Filters

Three-Degree-of-Freedom Estimation of Agile Space Objects Using Marginalized Particle Filters

Space Object Shape Characterization and Tracking Using Light Curve and Angles Data

Light Curve Analysis Using Wavelets

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