Model Order Selection For Multidimensional Innovations Based Detection In Airborne Radar

This paper investigates the model order selection problem for use with the multidimensional autoregressive (MAR) process in airborne radar detection processing which uses an Innovations Based Detection Algorithm (IBDA). Results indicate that a low order model should be used to accurately portray the return signal spectrum. Specifically, this paper investigates the use of the Akaike Information Criterion for model order selection. Examples are included for physically modeled data sets as well as actual radar data sets. 1 INTRODUCTION Parametric modeling using autoregressive processes has been used extensively in the literature for various applications in the univariate case as well as in the multivariate case (Kay, 1988) (Jones, 1974) (Nuttall, 1976). However, literature discussing the application of multidimensional autoregressive (MAR) processes is limited for the airborne RADAR target detection application (Michels, 1991). Noticeably absent is guidance and recommendations for..