A Classification and Recognition Method for UAV Targets Based on Micro-Doppler and Machine Learning
The drone has been a key threat as its infiltration events are incredibly increasing,due to the rapid de-velopment of the drone industry,which makes it a critical demand to develop drone prevention systems.Birds and drones are both classic objects that have low attitude,slow speed and small scale,leading to similar echo characteristics and movement patterns.To ensure the safety of airlines,it has been an important issue to observe,separate and track them two.To solve the problem,firstly,the mathematical and physical model of the drone and the bird are established by simulation,and the micro-motion characteristics of both are extracted.Then,the two kinds of objects are distinguished using convolutional neural network and ensemble learning methods.Based on which,the real field data are processed,and the result has proved the validity of the method.
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