Session
Session 8: Frank J. Redd Student Competition
Abstract
We present Tetra, a star identification algorithm that uses the minimum possible computation time and number of database accesses to solve the calibrationless lost-in-space problem. To solve the calibrationless lost-in-space problem, a star tracker must determine its attitude with no a priori knowledge, not even lens parameters such as the field-of-view or distortions. Tetra is based on a directly-addressed hash table data structure, which enables it to identify star patterns with a single database access. We prove a tight bound on Tetra's false positive rate and empirically compare Tetra's runtime, centroiding error sensitivity, and field-of-view error sensitivity with earlier lost-in-space algorithms: Pyramid and Nondimensional Star ID. We also compare Tetra with hash table based modifications of Pyramid’s cross-referencing step and Nondimensional Star ID's database lookup, which improve Pyramid and Nondimensional Star ID's runtimes by an order of magnitude without otherwise impacting their performance.
Document Type
Event
Presentation
TETRA: Star Identification with Hash Tables
We present Tetra, a star identification algorithm that uses the minimum possible computation time and number of database accesses to solve the calibrationless lost-in-space problem. To solve the calibrationless lost-in-space problem, a star tracker must determine its attitude with no a priori knowledge, not even lens parameters such as the field-of-view or distortions. Tetra is based on a directly-addressed hash table data structure, which enables it to identify star patterns with a single database access. We prove a tight bound on Tetra's false positive rate and empirically compare Tetra's runtime, centroiding error sensitivity, and field-of-view error sensitivity with earlier lost-in-space algorithms: Pyramid and Nondimensional Star ID. We also compare Tetra with hash table based modifications of Pyramid’s cross-referencing step and Nondimensional Star ID's database lookup, which improve Pyramid and Nondimensional Star ID's runtimes by an order of magnitude without otherwise impacting their performance.
