A Hybrid Adaptive Optics System for Space Situational Awareness (2019)
We are developing and will commission a space debris and object detection system in New Zealand that will provide high resolution capability to examine orbiting near Earth objects using a simplified, low-cost approach, where acquired data on new candidate objects are updated on a database. We will use a modular wide-field adaptive optics (AO) system to determine spatially variant distortion functions from multiple natural guide stars to compensate for atmospheric turbulence. To achieve this, our custom designed geometric wavefront sensor will provide estimates of phase distortion from each source object. Orbiting satellites and large space debris objects are detected within a wide field of natural stars, where a combination of closed- and open-loop adaptive optics are applied. A closed loop tip/tilt mirror system removes low-order aberrations in real-time, whereas an open-loop system will use deconvolution from wavefront sensing with batch processing to remove high-order distortion. Fast moving target objects will be imaged using a separate detector, where high frame rate-images of relatively bright objects will be used to minimise motion blur, and where synchronisation with AO cameras will allow removal of both low- and high-order aberrations from captured images. The hybrid AO system described in this paper will provide a platform to test novel methods for the detection of small, faint, fast-moving objects using alternative methods to atmospheric tomography.
CitationWeddell S, Clare R, Muruganandan V, Lambert A (2019). A Hybrid Adaptive Optics System for Space Situational Awareness. Delft, Netherlands: Adaptive Optics in Industry and Medcine. 21/10/2019-25/10/2019. The XII International Workshop on the Adaptive Optics in Industry and Medicine.
This citation is automatically generated and may be unreliable. Use as a guide only.
ANZSRC Fields of Research51 - Physical sciences::5109 - Space sciences::510906 - Space instrumentation
46 - Information and computing sciences::4603 - Computer vision and multimedia computation::460301 - Active sensing
40 - Engineering::4009 - Electronics, sensors and digital hardware::400909 - Photonic and electro-optical devices, sensors and systems (excl. communications)
Showing items related by title, author, creator and subject.
Artificial Intelligence Based Insulin Sensitivity Prediction for Personalized Glycaemic Control in Intensive Care Benyó B; Paláncz B; Szlávecz Á; Szabó B; Anane Y; Kovács K; Chase, Geoff (Elsevier BV, 2020)Stress-induced hyperglycaemia is a frequent complication in the intensive therapy that can be safely and efficiently treated by using the recently developed model-based tight glycaemic control (TGC) protocols. The most ...
Campbell JD; Holder-Pearson L; Benton C; Chase, Geoff; Pretty, Christopher; Knopp, Jennifer (Elsevier BV, 2020)Abstract: Currently, there are no continuous, non-invasive blood glucose monitors. With over 366 million people worldwide expected to be diagnosed as diabetic by 2030, an alternative to the current invasive methods is ...
Campbell JD; Holder-Pearson L; Pretty CG; Bones P; Chase, Geoff (Elsevier BV, 2020)Pulse wave velocity (PWV) is frequently used as an early indicator of risk of cardiovascular disease. Conventional methods of PWV measurement are invasive and measure the regional PWV, introducing errors from unknown ...