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 (1971) information criterion for model order selection. Examples are included for physically modeled data sets as well as actual radar data sets.