یک تکنیک شناسایی ستاره ای قوی برای حضور بالای اشیاء کاذب: الگوریتم چند قطب

This work proposes a novel technique for the star pattern recognition for the Lost in Space, named Multi-Poles Algorithm. This technique is especially designed to ensure a reliable identification of stars when there is a large number of false objects in the image, such as Single Event Upsets, hot pixels or other celestial bodies. The algorithm identifies the stars using three phases: the acceptance phase, the verification phase and the confirmation phase. The acceptance phase uses a polar technique to yield a set of accepted stars. The verification phase performs a cross-check between two sets of accepted stars providing a new set of verified stars. Finally, the confirmation phase introduces an additional check to discard or to keep a verified star. As a result, this procedure guarantees a high robustness to false objects in the acquired images. A reliable simulator is developed to test the algorithm to obtain accurate numerical results. The star tracker is simulated as a 1024×1024 Active Pixel Sensor with a 20° Field of View. The sensor noises are added using suitable distribution models. The stars are simulated using the Hipparcos catalog with corrected magnitudes accordingly to the instrumental response of the sensor. The Single Event Upsets are modeled based on typical shapes detected from some missions. The tests are conducted through a Monte Carlo analysis covering the entire celestial sphere. The numerical results are obtained for both a fixed and a variable attitude configuration. In the first case, the angular velocity is zero and the simulations give a success rate of 100% considering a number of false objects up to six times the number of the cataloged stars in the image. The success rate decreases at 66% when the number of false objects is increased to fifteen times the number of cataloged stars. For moderate angular velocities, preliminary results are given for constant rate and direction. By increasing the angular rate, the performances of the proposed algorithm decrease, since the location errors of the stars become much higher.