Flying birds and Unmanned Aerial Vehicles (UAVs) are typical “low, slow, and small” targets with low observability. The need for effective monitoring and identification of these two targets has become urgent and must be solved to ensure the safety of air routes and urban areas. There are many types of flying birds and UAVs that are characterized by low flying heights, strong maneuverability, small radar cross-sectional areas, and complicated detection environments, which are posing great challenges in target detection worldwide. “Visible (high detection ability) and clear-cut (high recognition probability)” methods and technologies must be developed that can finely describe and recognize UAVs, flying birds, and “low-slow-small” targets. This paper reviews the recent progress in research on detection and recognition technologies for rotor UAVs and flying birds in complex scenes and discusses effective detection and recognition methods for the detection of birds and drones, including echo modeling and recognition of fretting characteristics, the enhancement and extraction of maneuvering features in ubiquitous observation mode, distributed multi-view features fusion, differences in motion trajectories, and intelligent classification via deep learning. Lastly, the problems of existing research approaches are summarized, and we consider the future development prospects of target detection and recognition technologies for flying birds and UAVs in complex scenarios.
Two existing airport-based avian radar systems,Merlin and Accipiter,are introduced.Construction and major algorithms ofBeihang avian radar surveillance systemare analyzed.Some key avian radar system requirements are presented, which is followed by a system design based on network that satisfies the requirements.Finally,some suggestions are provided for two future research on two critical technologies in avian radar systems,3D information acquirement and target tracking algorithm.
Air cushion track is a kind of precise device used usually in general physical experiment,
which may be applicable to many experiments such as verification of Newton's motion law and momentum
--conservation law, measurement of gravity--acceleration g and simple harmonic motion of spring vibrator
etc. With photoelectric timing and air cushion floating, it may exactly measure time and lower the error
caused by motion friction to a minimum. The article only makes a study of the experiment in measuring
gravity--acceleration g with air cushion track and focuses on lowering experimental error and reinforcing
accuracy of measurement values by improving experimental method and perfecting experimental condi--
tions.
A sequence of plane position indicator (PPI) images containing a small moving target is collected using an experimental avian radar surveillance system, which is constructed by modifying a standard marine radar. Smoothing trajectory of a small moving target is separated from the image sequence after background subtraction, clutter suppression, measurements extraction and tracking. The background image is generated by Fast Independent Component Analysis (FastICA). Low segmentation value is set in clutter suppression to improve detecting rate at the cost of introducing a great deal of clutters. Therefore, false alarm rate need to be reduced by tracking. Meanwhile, a modified Hough transform method is applied for track initiation. Monte Carlo data association is proposed for track maintenance and Kalman filtering is adopted for target state prediction and update. Finally, the trajectory is smoothed and then fused with a satellite map for further observation.
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