Hyosang Yoon

New Star-Pattern Identification Using a Correlation Approach for Spacecraft Attitude Determination

PURPOSE: A method for recognizing a star of a star sensor using a correlation function is provided to recognize the identification number of stars in an original image by extracting an object image which maximizes a cost function and referring to the distribution pattern of the stars in the extracted image. CONSTITUTION: A star sensor generates a k-th object image about the surrounding stars and a k-th star through a modeling(120). The star sensor respectively generates first to N-th object images(130). The star sensor generates an original image through the modeling of the stars in a star image(140). The star sensor extracts an object image which maximizes a cost function(150). The star sensor recognizes stars in the original image by analyzing the extracted object image(160).

Laser-Guide-Star Satellite for Ground-Based Adaptive Optics Imaging of Geosynchronous Satellites

In this study, the feasibility and utility of using a maneuverable nanosatellite laser guide star from a geostationary equatorial orbit have been assessed to enable ground-based, adaptive optics im...

Kalman Filtering for Attitude and Parameter Estimation of Nanosatellites Without Gyroscopes

In this work, a Kalman filtering algorithm is proposed that estimates the spacecraft attitude and attitude parameters without gyroscope measurements for nanosatellites. The attitude parameters incl...

A General Solution for Update with Out-of-Sequence Measurements: The Augmented Fixed-Lag Smoother

Kalman Filters have been used as a means of estimation in the aerospace field for decades. By offering both vector modeling and recursive processing, these filters can be used to estimate state vectors. These state estimates rely on input data from sensors which may become available at irregular intervals. A past sensor measurement, for example, may arrive after the estimate for a time step has been computed using the other sensor data. In this paper, we introduce a general solution to update the Kalman filter with the outof-sequence measurements (OOSM) based on the fixed-lag smoother (FLS). It is able to generate a new state estimation at OOSM time using two adjacent estimations in FLS so that these OOSMs can be incorporated into future state estimates for improved accuracy. An inductive proof is presented to show the derivation and application of the new algorithm, called the Augmented Fixed-Lag Smoother.

Interpolation Method for Update with Out-of-Sequence Measurements: The Augmented Fixed-Lag Smoother

Nomenclature F = state transition matrix H = matrix giving the ideal/noiseless connection between measurements and states I = identity matrix k = time index L = noise sensitivity matrix in nonlinear systems N = lag time constant integer P = estimation-error covariance matrix s = arbitrary time step used in inductive proof xk = state at time index k x̂ = state estimate x̂k = a priori state estimate x̂ k = a posteriori state estimate yk = measurement at time index k