Parametric and Model Based Adaptive Detection Algorithms for Non- Gaussian Interference Backgrounds

摘要

This report presents model based adaptive signal processing methods for target detection in a background of non-Gaussian interference. Several candidate algorithms are derived and important insights pertaining to their structure are documented in this report. Performance analysis of these algorithms is discussed in some detail. It is seen that the parametric adaptive matched filter (PAMF) offers the potential for significantly improved performance in non-Gaussian interference scenarios, while leading to considerably lower secondary data support requirements compared to classical adaptive processing methods. This is due to the use of a parametric method that employs a low model order to approximate the interference spectral characteristics. Another reduced rank adaptive algorithm considered in this study is the principal component inverse (PCI) method.