Study On Precession Character Extraction In Ballistic Midcourse

Electromagnetic character extraction and recognition of micro-motion is a hotspot of radar target recognition domain. The phenomenon of precession is a special and important member of micro-motion. Researchers have been paying more and more attention to the study of precession. This paper based on the electromagnetic character of the mid-course target,mainly studies the problem of extracting the precession feature in ballistic mid-course, and also studies the farthest distance of finding the phenomenon of precession and the best radar location to observe the precession. The results will be meaning to the character extraction and recognition in mid-course and extracting the micro-doppler.Firstly, this part models the movement of the midcourse target,this model includes the macroscopic movement of the warhead centroid and the precession of the warhead,and can simulate the whole midcourse movement characteristics of the giving situation. Then we will creat the broadband and the narrowband echo based the moving characteristics.Secondly, study the performance of the arithmetic in extracting the precession period based on narrowband echo, and pay more attention to analyse the effect of restraining the frequency division and the frequency multiplication. We make such the conclusions: Auto-Correlation (AUTOC) has evidence effect on restraining the frequency multiplication; and the Average Magnitude Difference Function (AMDF), Cepstrum Method have evidence effect on restraining frequency division. By compounding the two kinds of the methods, we can solve the frequency multiplication and the frequency division problems. In allusion to the observability of the precession phenomenon, we model the attenuation model to and can study the observability of different distance. Then we analyse the best position to observe the precession phenomenon, and give the best position curve for observe the precession.Finally, we study the arithmetic of extracting the precession angle based on HRRP serials. In allusion to the high-workload and low- efficiency problem of the searching–parameters-by-step arithmetic, we advance the reduce-dimension arithmetic, this method can speed the convergence rate and improve the estimation result.