Title :
Model order selection for multidimensional innovations based detection in airborne radar
Author :
Castro, Jose ; LeBlanc, James P.
Author_Institution :
Klipsch Sch. of Electr. & Comput. Eng., New Mexico State Univ., Las Cruces, NM, USA
Abstract :
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
Keywords :
airborne radar; autoregressive processes; information theory; radar detection; radar signal processing; search radar; spectral analysis; Akaike information criterion; actual radar data sets; airborne radar detection processing; airborne surveillance radar; innovations based detection algorithm; low order model; model order selection; multidimensional autoregressive process; multidimensional innovations based detection; physically modeled data sets; return signal spectrum; Airborne radar; Autoregressive processes; Density estimation robust algorithm; Multidimensional systems; Object detection; Radar applications; Radar detection; Reflection; Surveillance; Technological innovation;
Conference_Titel :
Radar Conference, 1998. RADARCON 98. Proceedings of the 1998 IEEE
Conference_Location :
Dallas, TX
Print_ISBN :
0-7803-4492-8
DOI :
10.1109/NRC.1998.677991
