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Purpose The authors proposed a new method of fast time delay measurement for integrated pulsar pulse profiles in X-ray pulsar-based navigation (XNAV). As a basic observation of exact orientation in XNAV, time of arrival (TOA) can be obtained by time delay measurement of integrated pulsar pulse profiles. Therefore, the main purpose of the paper is to establish a method with fast time delay measurement on the condition of limited spacecraft’s computing resources. Design/methodology/approach Given that the third-order cumulants can suppress the Gaussian noise and reduce calculation to achieve precise and fast positioning in XNAV, the proposed method sets the third-order auto-cumulants of standard pulse profile, the third-order cross-cumulants of the standard and the observed pulse profile as basic variables and uses the cross-correlation function of these two variables to estimate the time delay of integrated pulsar pulse profiles. Findings The proposed method is simple, fast and has high accuracy in time delay measurement for integrated pulsar pulse profiles. The result shows that compared to the bispectrum algorithm, the method improves the precision of the time delay measurement and reduced the computation time significantly as well. Practical implications To improve the performance of time delay estimation in XNAV systems, the authors proposed a novel method for XNAV to achieve precise and fast positioning. Originality/value Compared to the bispectrum algorithm, the proposed method can improve the speed and precision of the TOA’s calculation effectively by using the cross-correlation function of integrated pulsar pulse profile’s third-order cumulants instead of Fourier transform in bispectrum algorithm.

Abstract.To remove the blur of the image from optical navigation camera, a image restoration method based on spectrum characteristics is proposed. In this method, image denoising pretrea tment is first implemented by the NSCT filter. Then, to obtain the minimum amplitude distanc e, the paper optimizes the image spectrum characteristic by using the simulated annealing al gorithm. And then the minimum amplitude distance can be obtained. There the point spread fu nction is established. Finally, blurred image is recovered by the Lucy -Richardson algorithm.T he recovery quality is evaluated by the Laplace gradient and the average gray level gradient. T he simulation results demonstrate that the proposed method in the paper improves the quality of the restored small celestial body image, and the visual effect is also very ideal. 1、IntroductionIn the process of the deep space exploration, the imaging system of the optical optical camera is vulnerable to the flight speed and the cosmic rays [1], which cause the image fuzzy and inconvenience for image information analysis. Thus, how to restore fuzzy image is a difficulty of image information process in the deep space exploration.The image restoration of small celestial body mainly consists of the image denoising pretreatment and the image degradation process estimation. At present, there are many relevant researches about the image denoising pretreatment. For instance, the median filtering algorithm has a very good effect on the salt and pepper noise, but it is not ideal to other interference noises [1]. The bilateral filter algorithm can eliminate the image noise, while it cannot make image detail intact [2]. The degradation function is important to determine the degradation process. The degradation function of the optical camera is the point-spread function (PSF) [3]. For the point-spread function, a lot of researches have been done by domestic and foreign scholars. X. H. Wang et al, considered that the distance between the spectrum center and the adjacent black belt is inversely proportional to the blurred length [4]. But it is only a project of the degradation function estimation, and does not have a specific quantitative formula. N. Norouzi et al., put forward a method of derivative, which can detect image blurred length. However, the error is very serious under the condition of noise [5].For the image blur problem caused by the optical camera, this paper proposes the NSC algorithm, which mainly includes image denoising pretreatment based on the NSCT filter and fuzzy regression model construction based on the spectrum characteristics. 2、NSC algorithm 2.1、NSCT filterThe non -subsampled contourlet (NSCT) filter is composed of the non -subsampled pyramid (NSP) [6] and the non -subsampled direction filter group (NSDFB) [7]. The redundant representation is adopted by the NSCT filter, which can effectively capture image characteristics, and distinguish between noise and image edge [9]. Thus, it has a very good denoising effect.The non-subsampled pyramid (NSP) is used for ...