DocumentCode :
3393711
Title :
A modified MCMC approach for classifying target and decoy
Author :
Zhe Liu ; Mai Xu ; Zulin Wang
Author_Institution :
Sch. of Electron. & Inf. Eng., Beihang Univ., Beijing, China
fYear :
2013
fDate :
19-21 Oct. 2013
Firstpage :
318
Lastpage :
323
Abstract :
In towed radar active decoy (TRAD) scenario, the target and decoy, locating in same radar half-power beam, make object tracking more challenging in today´s electronic warfare. Since the DOAs (direction-of-arrival) of target and decoy are the parameters of the likelihood of the observation data, the categorization of their becomes a sampling problem of machine learning field. Therefore, we, in this paper, propose a modified Markov Chain Monte Carlo (M-MCMC) approach towards classifying the target and decoy. First, we construct the observation signal model. Then, we find out that the parameters of the localization of target and decoy can be achieved by computing the covariance matrix of the observation vector. Moreover, rather than conventional numerical computation, our approach, intrinsically, combines the advantages of random walk and simulation annealing. The simulational results demonstrate the effectiveness of our approach.
Keywords :
Markov processes; Monte Carlo methods; covariance matrices; direction-of-arrival estimation; electronic warfare; radar signal processing; simulated annealing; DOA; MCMC approach; TRAD; covariance matrix; decoy classification; direction-of-arrival; electronic warfare; modified Markov chain Monte Carlo approach; observation signal model; observation vector; radar half-power beam; random walk; simulation annealing; target classification; towed radar active decoy; Azimuth; Jamming;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computational Intelligence (ICACI), 2013 Sixth International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-6341-9
Type :
conf
DOI :
10.1109/ICACI.2013.6748523
Filename :
6748523
Link To Document :
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