Radar Recognition through Statistical Classification of Cellular Emission in the Moment Space

Abstract

Abstract.A method is presented for radar target rescognition, using the moments of the parameters of the scattered or emitted signals for classification. Large size samples of signal parameters, formed by signals emitted by each of the resolution cells of the searching region, are used to obtain a determined number of normal distributed moments which represent statistical features of the cellular emissions. Taking these moments as components a classification vector is obtained in the moment space. Classification process is carried out assigning every cell of the searching region to one of the previously determined classes during an adaptive process, where the border limiting each class is determined accordingly Neyman -Pearson criterion. The larger size samples, the lower the classification error, which makes possible the distinction of processes very similarly to each other.

Two-dimensional Determination of the Decision Boundary for a Radar Detection Method in the Moment Space

“…To guarantee the P FA , DRACEC requires to estimate the detection threshold using the SDS Probability Density Function (PDF) (Chávez and Guillén 2018). Although this threshold has a direct representation in the SDS domain, it is useful to analyze its equivalent in the moments space: the decision boundary (Chávez and González 2008). It is possible to establish different ways to calculate the boundary knowing the relationship between both domains, which could be convenient depending on the application scenario.…”

Selection of Sampling Interval and Size of Random Sample for Radar Detection in the Moments Space

“…Figs. [10][11][12] show that the assumption of a multivariate Gaussian JPDF for the moments is satisfactory for the chosen M and T m . The elliptical shape of the clusters clearly shows that the JPDF of the moments is multivariate Gaussian.…”

“…In the last two decades progress has been made in a method with the dual purpose of reducing a priori uncertainty about the statistical model and providing features to increase the detection quality. Known by its authors as DRACEC, a Spanish acronym for Radar Detection through the Analysis and Classification of Cellular Emission, it is grounded on [9] and was first presented in [10].…”