Parallel Distributed CFAR Detection Optimization Based on Genetic Algorithm with Interval Encoding

作     者：于泽 周荫清 

作者机构：北京航空航天大学电子信息工程学院 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2010年第23卷第3期

页      面：351-358页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 0825[工学-航空宇航科学与技术] 0801[工学-力学（可授工学、理学学位）] 

基　　金：New Century Program for Excellent Talents of Minis-try of Education of China (NECT-06-0166) The Eleventh Five-year Scientific and Technological Development Plan of National Defense Pre-study Foundation (A2120060006) 

主　　题：parallel processing systems synthetic aperture radar detectors genetic algorithms optimization encoding 

摘      要：Aiming at parallel distributed constant false alarm rate （CFAR） detection employing K/N fusion rule,an optimization algorithm based on the genetic algorithm with interval encoding is proposed. N-1 local probabilities of false alarm are selected as optimization variables. And the encoding intervals for local false alarm probabilities are sequentially designed by the person-by-person optimization technique according to the constraints. By turning constrained optimization to unconstrained optimization,the problem of increasing iteration times due to the punishment technique frequently adopted in the genetic algorithm is thus overcome. Then this optimization scheme is applied to spacebased synthetic aperture radar （SAR） multi-angle collaborative detection,in which the nominal factor for each local detector is determined. The scheme is verified with simulations of cases including two,three and four independent SAR systems. Besides,detection performances with varying K and N are compared and analyzed.