A Radar Recognition Method for UAV and Flying Bird Targets Based on Track Characteristics
For modern radar detection systems,unmanned aerial vehicles(UAVs)and birds belong to a typical type of targets with"low,slow,and small"characteristics.In complex combat environments,the functional requirements of radar detection systems are not only limited to achieving stable detection and tracking of the two targets.How to effectively distinguish the two types and complete the recognition is an urgent and important challenge at present.Conventionally,the targets are distinguished from the differences in their micro-motion characteristics.However,it is difficult to extract the target features through time-frequency analysis methods since the amplitudes of the two echoes are very weak.In order to solve this problem,in this paper,a radar recognition method for UAVs and flying bird targets is proposed based on track characteristics.First,the differences in the motion trajectories of the two targets are compared,then a feature analysis is conducted,and a time-dependent description method for heading the oscillation frequency and velocity oscillation frequency feature quantities is proposed.In the offline state,the effective feature quantities of the two targets are extracted from the track data recorded by the actual radar system.Then,the samples are trained by the support vector machine algorithm.After the optimal model parameters are obtained,tests are carried out.The test classification results show that the accuracy of the identification can reach 87％.Finally,flight tests in the online state are conducted.The obtained results not only indicate the correctness of the method,but also reflect its lightweight,practicality,and applicability in the perspective of engineering implementation,which has high value.
国家科技期刊平台
NSTL
万方数据
维普
