RCS based target recognition with real FMCW radar implementation

Abstract

In this article, we investigate the methods that can realize automatic target recognition and tracking by exploiting signal distribution of radar cross section (RCS) with frequency modulated continuous wave (FMCW) radar. In doing this we use the real RCS data measured from the short‐range FMCW vehicle radar. We estimate the continuous valued degree of freedom and the mean of RCS distribution using maximum likelihood estimation (MLE) assuming that RCS follows gamma distribution. The experiments with real radar verify that parameterized gamma distributions for three targets of man, vehicle, and drone closely follow the empirical distributions. Then, we apply maximum a posteriori criterion (MAP) for target recognition. The average recognition probabilities for man, vehicle, and drone using MAP are 85%, 100%, and 92%, respectively. Since the vehicle has distinct RCS and thus perfectly recognizable, we apply a support vector machine (SVM) hoping to better classify the man and the drone. The man is recognized with similar accuracy, but the drone is not due to the lack of training samples, of which constraint is imposed by real implementation and experiment. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:1745–1750, 2016

Implementation of antenna space and beam diversities in a K band FMCW radar system for the short‐range vehicular detections

“…Figure shows the simulated electromagnetic (EM) backscattering fields when these two beams of 7 and 18° in beam width illuminate a sedan, where the commercial software FEKO [ FEKO Suite 6.3 , ] was used to compute the scattering fields at the same location of antenna [ Lee et al, ]. The dimensions of car are 4.6 m, 1.8 m, and 1.4 m in length, width, and height, respectively.…”

Radar cross section statistical characteristics of stealth aircraft using the Weibull‐generalized gamma mixture model

“…Due to the complicated electromagnetic scattering properties, RCS of stealth aircraft is usually described using a statistical variable for the radar detection application 3,4 . The typical conventional RCS statistical models include Swerling‐chi model, Weibull model, Rice model, Lognormal model and $$\alpha -\mu $$ model, and so on 5,6 . As applications, the RCS statistical characteristics of a scaled stealth aircraft model are described using the $${\chi }^{2}$$ distribution and lognormal distribution in Shi et al 7 Weibull distribution is selected to analyze the RCS statistical characteristics of an aircraft model in Shi et al 8 …”

Statistical modeling for the monostatic radar cross‐section characteristics of stealth aircraft using the Swerling‐chi/inverse generalized gamma composite distribution model

“…Radar cross‐section (RCS) is an important parameter of the electromagnetic scattering characteristics of stealth aircrafts 1–4 . As the stealth target RCS fluctuates rapidly with the variation of aspect angle, it is usually described using statistical distributions 5–9 . For example, distribution, lognormal distribution, and Weibull distribution have been used for modeling RCS statistical characteristics of stealth aircrafts 8,9 .…”

“…[1][2][3][4] As the stealth target RCS fluctuates rapidly with the variation of aspect angle, it is usually described using statistical distributions. [5][6][7][8][9] For example, χ 2 distribution, lognormal distribution, and Weibull distribution have been used for modeling RCS statistical characteristics of stealth aircrafts. 8,9 However, as RCS values of stealth aircrafts are usually very small and appear sharply fluctuating characteristics, 10 the conventional RCS statistical models may be not suitable for fitting RCS statistical characteristics of stealth aircrafts.…”