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CLC number: TP273; V11

On-line Access: 2014-06-06

Received: 2013-12-05

Revision Accepted: 2014-04-22

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Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.6 P.458-469


A new maximum-likelihood phase estimation method for X-ray pulsar signals

Author(s):  Hua Zhang, Lu-ping Xu, Yang-he Shen, Rong Jiao, Jing-rong Sun

Affiliation(s):  School of Aerospace Science and Technology, Xidian University, Xi’an 710126, China

Corresponding email(s):   zhanghua@mail.xidian.edu.cn, lpxu@mail.xidian.edu.cn, yanghe_shen@163.com, jrong_06@163.com, jrsun@xidian.edu.cn

Key Words:  X-ray pulsar, Poisson model, Phase estimation, Maximum likelihood

Hua Zhang, Lu-ping Xu, Yang-he Shen, Rong Jiao, Jing-rong Sun. A new maximum-likelihood phase estimation method for X-ray pulsar signals[J]. Journal of Zhejiang University Science C, 2014, 15(6): 458-469.

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%T A new maximum-likelihood phase estimation method for X-ray pulsar signals
%A Hua Zhang
%A Lu-ping Xu
%A Yang-he Shen
%A Rong Jiao
%A Jing-rong Sun
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 6
%P 458-469
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%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300347

T1 - A new maximum-likelihood phase estimation method for X-ray pulsar signals
A1 - Hua Zhang
A1 - Lu-ping Xu
A1 - Yang-he Shen
A1 - Rong Jiao
A1 - Jing-rong Sun
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 6
SP - 458
EP - 469
%@ 1869-1951
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PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300347

x-ray pulsar navigation (XPNAV) is an attractive method for autonomous navigation of deep space in the future. Currently, techniques for estimating the phase of x-ray pulsar radiation involve the maximization of the general non-convex object functions based on the average profile from the epoch folding method. This results in the suppression of useful information and highly complex computation. In this paper, a new maximum likelihood (ML) phase estimation method that directly utilizes the measured time of arrivals (TOAs) is presented. The x-ray pulsar radiation will be treated as a cyclo-stationary process and the TOAs of the photons in a period will be redefined as a new process, whose probability distribution function is the normalized standard profile of the pulsar. We demonstrate that the new process is equivalent to the generally used poisson model. Then, the phase estimation problem is recast as a cyclic shift parameter estimation under the ML estimation, and we also put forward a parallel ML estimation method to improve the ML solution. Numerical simulation results show that the estimator described here presents a higher precision and reduces the computational complexity compared with currently used estimators.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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