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Register a new userArtificial guide stars for adaptive optics using unmanned aerial vehicles
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Astronomical adaptive optics systems are used to increase effective telescope resolution. However, they cannot be used to observe the whole sky since one or more natural guide stars of sufficient brightness must be found within the telescope field of view for the AO system to work. Even when laser guide stars are used, natural guide stars are still required to provide a constant position reference. Here, we introduce a technique to overcome this problem by using rotary unmanned aerial vehicles (UAVs) as a platform from which to produce artificial guide stars. We describe the concept, which relies on the UAV being able to measure its precise relative position. We investigate the adaptive optics performance improvements that can be achieved, which in the cases presented here can improve the Strehl ratio by a factor of at least 2 for a 8~m class telescope. We also discuss improvements to this technique, which is relevant to both astronomical and solar adaptive optics systems.
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Adaptive optics (AO) is a key technology for ground-based optical and infrared astronomy, providing high angular resolution and sensitivity. AO systems employing laser guide stars (LGS) can achieve high sky coverage, but their performance is limited by LGS return flux. We examine the potential of two new approaches that might produce high-intensity atmospheric laser beacons. Amplified spontaneous emission could potentially boost the intensity of beacons produced by conventional resonant excitation of atomic or molecular species in the upper atmosphere. This requires the production of a population inversion in an electronic transition that is optically-thick to stimulated emission. Potential excitation mechanisms include continuous wave pumping, pulsed excitation and plasma generation. Alternatively, a high-power femtosecond pulsed laser could produce a white-light supercontinuum high in the atmosphere. The broad-band emission from such a source could also facilitate the sensing of the tilt component of atmospheric turbulence.
The capabilities of autonomous flight with unmanned aerial vehicles (UAVs) have significantly increased in recent times. However, basic problems such as fast and robust geo-localization in GPS-denied environments still remain unsolved. Existing research has primarily concentrated on improving the accuracy of localization at the cost of long and varying computation time in various situations, which often necessitates the use of powerful ground station machines. In order to make image-based geo-localization online and pragmatic for lightweight embedded systems on UAVs, we propose a framework that is reliable in changing scenes, flexible about computing resource allocation and adaptable to common camera placements. The framework is comprised of two stages: offline database preparation and online inference. At the first stage, color images and depth maps are rendered as seen from potential vehicle poses quantized over the satellite and topography maps of anticipated flying areas. A database is then populated with the global and local descriptors of the rendered images. At the second stage, for each captured real-world query image, top global matches are retrieved from the database and the vehicle pose is further refined via local descriptor matching. We present field experiments of image-based localization on two different UAV platforms to validate our results.
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Unmanned Aerial Vehicles (UAV)-based civilian or military applications become more critical to serving civilian and/or military missions. The significantly increased attention on UAV applications also has led to security concerns particularly in the context of networked UAVs. Networked UAVs are vulnerable to malicious attacks over open-air radio space and accordingly, intrusion detection systems (IDSs) have been naturally derived to deal with the vulnerabilities and/or attacks. In this paper, we briefly survey the state-of-the-art IDS mechanisms that deal with vulnerabilities and attacks under networked UAV environments. In particular, we classify the existing IDS mechanisms according to information gathering sources, deployment strategies, detection methods, detection states, IDS acknowledgment, and intrusion types. We conclude this paper with research challenges, insights, and future research directions to propose a networked UAV-IDS system which meets required standards of effectiveness and efficiency in terms of the goals of both security and performance.
We use spatio-temporal cross-correlations of slopes from five Shack-Hartmann wavefront sensors to analyse the temporal evolution of the atmospheric turbulence layers at different altitudes. The focus is on the verification of the frozen flow assumption. The data is coming from the Gemini South Multi-Conjugate Adaptive Optics System (GeMS). First, the Cn2 and wind profiling technique is presented. This method provides useful information for the AO system operation such as the number of existing turbulence layers, their associated velocities, altitudes and strengths and also a mechanism to estimate the dome seeing contribution to the total turbulence. Next, by identifying the turbulence layers we show that it is possible to estimate the rate of decay in time of the correlation among turbulence measurements. We reduce on-sky data obtained during 2011, 2012 and 2013 campaigns and the first results suggest that the rate of temporal de-correlation can be expressed in terms of a single parameter that is independent of the layer altitude and turbulence strength. Finally, we show that the decay rate of the frozen-flow contribution increases linearly with the layer speed. The observed evolution of the decay rate confirms the potential interest of the predictive control for wide-field AO systems.
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