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X-ray pulsar-based navigation(XPNAV) is an attractive method for autonomous deepspace navigation in the future.The pulse phase estimation is a key task in XPNAV and its accuracy directly determines the navigation accuracy.State-of-the-art pulse phase estimation techniques either suffer from poor estimation accuracy,or involve the maximization of generally nonconvex object function,thus resulting in a large computational cost.In this paper,a fast pulse phase estimation method based on epoch folding is presented.The statistical properties of the observed profile obtained through epoch folding are developed.Based on this,we recognize the joint probability distribution of the observed profile as the likelihood function and utilize a fast Fourier transform-based procedure to estimate the pulse phase.Computational complexity of the proposed estimator is analyzed as well.Experimental results show that the proposed estimator significantly outperforms the currently used cross-correlation(CC) and nonlinear least squares(NLS) estimators,while significantly reduces the computational complexity compared with NLS and maximum likelihood(ML) estimators.
X-ray pulsar-based navigation (XPNAV) is an attractive method for autonomous deep space navigation in the future. The pulse phase estimation is a key task in XPNAV and its accuracy directly determines the accuracy. State-of-the-art pulse phase estimation techniques either suffer from poor estimation accuracy, or involve the maximization of generally nonconvex object function, thereby resulting in a large computational cost.In this paper, a fast pulse phase estimation method based on epoch folding is presented. statistical properties of the observed profile obtained through epoch folding are developed.Based on this, we recognize the joint probability distribution of the observed profile as the likelihood function and utilize a fast Fourier transform-based procedure to estimate the pulse phase. Computational complexity of the proposed estimator is analyzed as well. Experimental results show that the proposed estimator significantly outperforms the currently used cross-correlation (CC) an d nonlinear least squares (NLS) estimators, while significantly reduces the computational complexity compared with NLS and maximum likelihood (ML) estimators.