Identification and micro-motion parameter estimation of non-cooperative UAV targets

Abstract

With the wide application of unmanned aerial vehicles (UAV) in industrial production, transportation, and entertainment, it is urgent to identify UAVs in time. Traditional UAV recognition mainly depends on wireless communication, which puts forward high requirements for a communication environment and has no way to deal with non-cooperative targets. Therefore, it is urgent to explore a UAV target recognition scheme based on perception. In this paper, aiming at the time series preprocessing method, a coding-based sequence preprocessing method is proposed. This method effectively improves the effect of the Deep Learning method in the identification task. In order to verify the ability of Deep Learning in radar time series data processing and the effectiveness of the proposed method, the Deep Learning method is used to analyze the radar signal time series of the target to realize the target recognition. Finally,considering the influence of micro-motion factors on UAV targets, the neural network is used to estimate UAV’s micro-motion parameters to enhance the ability of target recognition with the help of micro-motion information.