Research On Micro-doppler Feature Extraction Of Ballistic Targets In Midcourse

With the wide application of multi-warhead technology and decoy technology,modern ballistic missiles have advanced penetration capability.The radar recognition features of ballistic targets include RCS sequence,Doppler spectrum,high-resolution radar image and so on,but these features have their own limitations,so it is difficult to identify the real warhead in the target group.Micro-motion is caused by the special structure of the target under the specific force,which reflects the unique motion and structural characteristics of the target.Due to the limitation of the payload,the controllability of the mass distribution and motion characteristics of the decoy is greatly limited,so it is very difficult to use the decoy to simulate the micro-motion characteristics of the warhead target.Therefore,in the increasingly fierce situation of ballistic missile attack and defense,micro-Doppler feature can be used as one of the important bases for ballistic target recognition.This paper mainly studies the micro-Doppler feature extraction method of mid-course ballistic targets,including the following aspects:1.The micro-motion model of mid-course ballistic target is established,and its micro-Doppler modulation effect is analyzed theoretically.Firstly,the principle of micro-Doppler effect is introduced,then the general model of fretting target is established and micro-Doppler analysis is carried out.On this basis,the precession model of the midcourse target of the missile is established,and the micro-Doppler modulation characteristics of the model are analyzed.Finally,the radar echo signal model under the condition of macro-motion is derived.2.Two kinds of common methods of micro-Doppler feature extraction are studied to pave the way for the follow-up research.Firstly,the principles of several methods of joint time-frequency analysis are briefly summarized,and the advantages and disadvantages of these methods are analyzed by simulation.Then the principle of Hilbert-Huang transform is introduced,and the micro-motion feature extraction performance of the algorithm is verified.3.Aiming at the shortcomings of EMD in micro-Doppler feature extraction in Hilbert Huang transform,micro-motion feature extraction methods based on EEMD,CEEMDAN,and ICEEMDAN are respectively proposed to improve the micro-motion feature extraction of Hilbert Huang transform.The anti-noise performance of the three algorithms is compared and analyzed.4.The method of micro-Doppler feature extraction in the presence of macro-motion is studied.The periodicity of time-frequency distribution of micro-Doppler signal is analyzed,and it is proved that this periodicity will be transformed into circular periodicity under the influence of macro-motion.Based on this,combined with circular average amplitude difference function(CAMDF)and circular autocorrelation function(CACF),a micro-motion period estimation method based on CACF/CAMDF is proposed.Experiments show that this method can estimate the precession period of the target in the middle of the ballistic trajectory without the need for translational compensation,and has a good anti-noise ability.