Adaptive CFAR detection via Bayesian hierarchical model based parameter estimation

Author

Chen, Biao ; Varshney, Pramod K. ; Michels, James H.

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., NY, USA

Volume

2

fYear

2001

fDate

4-7 Nov. 2001

Firstpage

1396

Abstract

Radar CFAR detection is addressed in this paper where the unknown noise/clutter statistics are modeled using a hierarchical structure. Considering the secondary data as a probability mixture due to the complex and heterogeneous background, parameter estimation is achieved using the empirical Bayesian approach. Unlike conventional cell averaging CFAR (and its variations) and order statistics CFAR, the new CFAR detection algorithm is less sensitive to the clutter edge location/duration. Performance evaluation is conducted via numerical simulation.

Keywords

Bayes methods; adaptive estimation; adaptive radar; parameter estimation; probability; radar clutter; radar detection; Bayesian hierarchical model; adaptive CFAR detection; clutter edge location/duration; complex heterogeneous background; numerical simulation; parameter estimation; performance evaluation; probability mixture; radar detection; unknown noise/clutter statistics; Background noise; Bayesian methods; Exponential distribution; Jamming; Parameter estimation; Probability; Radar clutter; Radar detection; Statistics; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2001. Conference Record of the Thirty-Fifth Asilomar Conference on

Conference_Location

Pacific Grove, CA, USA

ISSN

1058-6393

Print_ISBN

0-7803-7147-X

Type

conf

DOI

10.1109/ACSSC.2001.987720

Filename

987720