Position and Time Determination without Prior State Knowledge via Onboard Optical Observations of Delta Scuti Variable Stars
We present a navigation concept for solving the lost in space and time problem using optical observations of $\delta$ Scuti variable stars. Only a small number of techniques exist that allow a spacecraft to recover from being lost in both space and time, which can be caused by a failure of the onboard clock and navigation systems. Optical observations of $\delta$ Scuti stars, which can be collected onboard from star trackers or navigation cameras, may enable autonomous position and time determination without requiring additional equipment or external communication. Our results indicate that less than one day of observation by the OSIRIS-APEX PolyCam may enable position and time determination accuracy within 0.03 au (3$\sigma$) and 3 seconds (3$\sigma$).
Code (0)
등록된 구현이 없습니다.
Tasks
PositionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Time series forecasting with Gaussian Processes needs priors
Automatic forecasting is the task of receiving a time series and returning a forecast for the next time steps without any human intervention. Gaussian Processes (GPs) are a powerful tool for modeling time series, but so …
Gaussian ProcessesTime SeriesTime Series AnalysisTime Series ForecastingInitial Orbit Determination for the CR3BP using Particle Swarm Optimization
This work utilizes a particle swarm optimizer (PSO) for initial orbit determination for a chief and deputy scenario in the circular restricted three-body problem (CR3BP). The PSO is used to minimize the difference betwee…
GPUPositionConstrained Wrapped Least Squares: A Tool for High Accuracy GNSS Attitude Determination
Attitude determination is a popular application of Global Navigation Satellite Systems (GNSS). Many methods have been developed to solve the attitude determination problem with different performance offerings. We develop…
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models
Latent variable models are ubiquitous in the exploratory analysis of neural population recordings, where they allow researchers to summarize the activity of large populations of neurons in lower dimensional ‘latent’ spac…
Variational InferenceCNMBI: Determining the Number of Clusters Using Center Pairwise Matching and Boundary Filtering
One of the main challenges in data mining is choosing the optimal number of clusters without prior information. Notably, existing methods are usually in the philosophy of cluster validation and hence have underlying assu…