Research On The RCS Of Sea-skimming Drones Based On Sea Conditions

With the development of ultra-low-altitude penetration technology,the sea-skimming Unmanned Aerial Vehicles(UAV)have become reliable attack weapons in modern marine warfare and can successfully cover their own Radar Cross Section(RCS)signals by using sea background clutter.However,it is difficult to accurately obtain the RCS signals that reflect the scattering characteristics of sea-skimming UAV,because the scattered signals of sea surface under different sea conditions seriously affect the validity and accuracy of radar identification.In order to solve the above problems,complex RCS models of two-dimensional rough sea and sea-skimming UAV under different sea conditions are established.The Physical Optics with Improving Multilevel Fast Multipole Algorithm(PO-IMLFMA)hybrid algorithm based on Four-Path Model(FPM)is proposed,to analyse the influence of different sea conditions on double-station RCS and single-station RCS of sea-skimming UAV.Finally,the optimal compensation scheme based on Back Propagation(BP)neural network is proposed to effectively compensate the RCS of sea-skimming UAV under the influence of sea conditions.It provides a basis for detecting radar's effective identification technology for sea-skimming targets.The main contents are outlined as follows:(1)The RCS numerical calculation method of UAV is studied and the RCS calculation model of a certain type of sea-skimming UAV is established.The Improving Multilevel Fast Multipole Algorithm(IMLFMA),based on Physical Optics(PO)current distribution,is proposed for the model.UAV electromagnetic scattering characteristics are analyzed from three aspects of angular response,shape structure and body material.(2)Two-dimensional rough sea model under different sea conditions are established by using PM spectrum and Monte Carlo method.A fast RCS algorithm based on PO is proposed for this model,and the electromagnetic scattering characteristics of sea surface under different sea conditions are analyzed.(3)The RCS composite models of two-dimensional rough sea and sea-skimming UAV under different sea conditions are established.A new PO-IMLFMA hybrid algorithm based on FPM is proposed,to study the influence of different sea conditions on double-station RCS and single-station RCS of sea-skimming UAV.(4)The RCS compensation scheme based on BP neural network is proposed for the sea-skimming UAV.The RCS detection results of the sea-skimming UAV under different sea conditions are compensated by taking the airborne radar as a platform.The compensation results show that the compensation error under Grade 1 sea condition is within 0.95 dBsm.The compensation error within Grade 3 sea condition is within 0.41 dBsm.The compensation error under Grade 5 sea condition is within 1.94 dBsm.In most cases,the compensation result and the expected value curve can be well fitted.This compensation scheme is verified to have high applicability and can effectively reduce the impact of sea conditions on RCS exploration of sea-skimming UAV.