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The radar detection with decision making in the moments space (DRACEC method) is based on a statistical analysis to determine the boundary between the background (absent target) and the anomaly (present target) classes. In this article, the boundary is taken as an ellipse and is calculated for two dimensions, emphasizing its geometric interpretation. The procedures to establish the shape, location, and size of the ellipse are highlighted, guaranteeing the probability of false alarm by applying the Neyman-Pearson criterion. The proposal establishes a methodology for calculation of the boundary when it is required to use the moments directly as a sufficient decision statistic.

Recently a novel method for radar detection was conceived to process the scattered signal parameters and detect through its statistical moments. Among the advantages of detection in the moments space stands the opportunity of considering the moments like Gaussian random variables, decreasing the uncertainty about the distribution of the variables used by traditional methods. Therefore, it is very important to study the conditions for assuming the above within certain level of confidence. This work uses real radar signals in order to study the influence of two essential variables for detection in the moments space: the sampling interval and the size of the random sample. Average correlation coefficient, hypothesis testing and numerical goodness-of-fit coefficients are used to estimate the values of the previous variables that allow to take the joint distribution of moments as close to the multivariate Gaussian. The guidelines presented should be taken into account for the proper configuration of detectors in the moments space.

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