基于压缩感知的脉冲星轮廓构建算法

利用有限的观测时间获得高精度的累积脉冲轮廓,对于X射线脉冲星导航具有重要意义.提出一种基于压缩感知的脉冲星累积脉冲轮廓重构算法.首先,研究脉冲轮廓,获取先验知识,在此基础上构建波形匹配冗余词典,得到脉冲轮廓的稀疏表示;然后,设计观测矩阵,利用原始观测数据得到具有较少点数的非自适应随机测量值;最后,采用匹配追踪方法从随机测量值中恢复脉冲轮廓.利用罗希X射线探测器的实测脉冲星数据进行实验,结果表明：利用相同时段的观测数据,该算法得到的累积脉冲轮廓的信噪比远高于周期叠加方法得到的累积脉冲轮廓.

Pulsar profile construction algorithm based on compressed sensing

It is of great importance for X-ray pulsar based navigation to obtain high-precision integrated pulse profile within limited observation time.This paper proposes a novel pulsar integrated pulse profile reconstruction algorithm based on compressed sensing.Firstly,we study the pulsar integrated pulse profile and get its priori knowledge.On that basis,we construct the waveform-matched redundant dictionary and obtain the sparse representation of pulse profile.Secondly,observing matrix is designed to acquire non-adaptive random measurement values with small number from original data.Finally,the pulse profile is reconstructed from random measurement values by matching pursuit method.Experiment is conducted using the data observed by Rossi X-ray timing explorer,and the results show that the signal-to-noise ratio of pulse profile obtained by this algorithm is much higher than that obtained by folding method.