Long baseline stereovision for automatic detection and ranging of moving objects in the night sky

SST Anywhere—A Portable Solution for Wide Field Low Earth Orbit Surveillance

The low-Earth orbit (LEO) is filled with active satellites, but also with space debris, which need constant observation. The orbiting objects may be affected by collisions or by atmospheric drag, and therefore they can change their orbit or even fall to the ground, a process known as reentry. The low altitude of these objects (below 2000 km, usually even below 1000 km) means that at given time they can be observed from a limited range of locations on the Earths’ surface, and therefore having multiple, easy to set up observation stations can be extremely useful. This paper presents a portable hardware solution for on-demand wide-field surveillance of the LEO region, the image processing algorithms for detecting the satellite streaks and for joining these streaks into tracklets, and the solution for astrometrical reduction and generating the result file for each tracklet. An automatic validation solution that is able to automatically identify the detected satellites and compute the measurement angular errors is also presented. The acquisition and processing system is built with commercially available items of low and moderate costs and is capable of on-site acquisition and real-time processing of images. The acquired images are processed by background subtraction, analysis of the difference between frames, extraction of elongated objects corresponding to the satellite streaks, and forming trajectories (tracklets) from consecutive detections. The pixel coordinates of the tracklets are converted to angular coordinates using the tools from Astrometry.net, subsequently filtered for improving the accuracy. The results are validated by using daily updated orbital parameters (TLEs), which are used to predict the angular positions that are subsequently matched with the detection results.

Real-Time Multi-Target Localization from Unmanned Aerial Vehicles

In order to improve the reconnaissance efficiency of unmanned aerial vehicle (UAV) electro-optical stabilized imaging systems, a real-time multi-target localization scheme based on an UAV electro-optical stabilized imaging system is proposed. First, a target location model is studied. Then, the geodetic coordinates of multi-targets are calculated using the homogeneous coordinate transformation. On the basis of this, two methods which can improve the accuracy of the multi-target localization are proposed: (1) the real-time zoom lens distortion correction method; (2) a recursive least squares (RLS) filtering method based on UAV dead reckoning. The multi-target localization error model is established using Monte Carlo theory. In an actual flight, the UAV flight altitude is 1140 m. The multi-target localization results are within the range of allowable error. After we use a lens distortion correction method in a single image, the circular error probability (CEP) of the multi-target localization is reduced by 7%, and 50 targets can be located at the same time. The RLS algorithm can adaptively estimate the location data based on multiple images. Compared with multi-target localization based on a single image, CEP of the multi-target localization using RLS is reduced by 25%. The proposed method can be implemented on a small circuit board to operate in real time. This research is expected to significantly benefit small UAVs which need multi-target geo-location functions.

Detecting Curvilinear Features Using Structure Tensors

Few published articles on curvilinear structures exist compared with works on detecting lines or corners with high accuracy. In medical ultrasound imaging, the structures that need to be detected appear as a collection of microstructures correlated along a path. In this paper, we investigated techniques that extract meaningful low-level information for curvilinear structures, using techniques based on structure tensor. We proposed a novel structure tensor enhancement inspired by bilateral filtering. We compared the proposed approach with five state-of-the-art curvilinear structure detectors. We tested the algorithms against simulated images with known ground truth and real images from three different domains (medical ultrasound, scanning electron microscope, and astronomy). For the real images, we employed experts to delineate the ground truth for each domain. Techniques borrowed from machine learning robustly assessed the performance of the methods (area under curve and cross validation). As a practical application, we used the proposed method to label a set of 5000 ultrasound images. We conclude that the proposed tensor-based approach outperforms the state-of-the-art methods in providing magnitude and orientation information for curvilinear structures. The evaluation methodology ensures that the employed feature-detection method will yield reproducible performance on new, unseen images. We published all the implemented methods as open-source software.

A low cost automatic detection and ranging system for space surveillance in the medium Earth orbit region and beyond

The space around the Earth is filled with man-made objects, which orbit the planet at altitudes ranging from hundreds to tens of thousands of kilometers. Keeping an eye on all objects in Earth's orbit, useful and not useful, operational or not, is known as Space Surveillance. Due to cost considerations, the space surveillance solutions beyond the Low Earth Orbit region are mainly based on optical instruments. This paper presents a solution for real-time automatic detection and ranging of space objects of altitudes ranging from below the Medium Earth Orbit up to 40,000 km, based on two low cost observation systems built using commercial cameras and marginally professional telescopes, placed 37 km apart, operating as a large baseline stereovision system. The telescopes are pointed towards any visible region of the sky, and the system is able to automatically calibrate the orientation parameters using automatic matching of reference stars from an online catalog, with a very high tolerance for the initial guess of the sky region and camera orientation. The difference between the left and right image of a synchronized stereo pair is used for automatic detection of the satellite pixels, using an original difference computation algorithm that is capable of high sensitivity and a low false positive rate. The use of stereovision provides a strong means of removing false positives, and avoids the need for prior knowledge of the orbits observed, the system being able to detect at the same time all types of objects that fall within the measurement range and are visible on the image.