Deep space exploration, generally referring to the space exploration activities targeting the Moon and more distant extraterrestrial celestial bodies, stands as a critical indicator of a nation’s comprehensive capabilities and technological prowess [...]

The celestial navigation system based on star angle (SA) is a classical autonomous navigation method for the spacecraft, which directly provides the position information of the spacecraft relative to the near celestial body. But due to the relativistic effects, the star direction observed by spacecraft is inconsistent with that acquired from star ephemeris, which reduces navigation accuracy of SA. In addition, SA cannot directly provide the velocity information of the spacecraft. StarNAV is a novel celestial navigation method that utilizes the relativistic effects, which mostly provides the velocity information of the spacecraft. In this paper, the star angle modified with relativistic effects (SAMRE)/StarNAV integrated navigation method is proposed. The measurement model of SAMRE is established by considering relativistic effects in the measurement model of SA. Simulation results indicate that during the Mars approach phase, SAMRE has better navigation accuracy compared with SA, and the navigation accuracy of the SAMRE/StarNAV integrated navigation method is higher than that of SAMRE, StarNAV and SA/StarNAV, respectively. Furthermore, the paper analyses the impact of measurement errors on the navigation accuracy of SAMRE/StarNAV. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This paper proposes a prescribed performance control policy for spacecraft attitude tracking control. The designed controller employs a kind of performance envelope function that can configure the convergence rate arbitrarily. Moreover, the controller design is based on the barrier Lyapunov function and indirect adaptive methods, which can suppress the fluctuation of external disturbances to obtain lower steady-state errors. Compared to prior studies on spacecraft attitude tracking, the proposed policy can arbitrarily configure the system convergence performance to achieve performance trade-offs in different scenarios. Moreover, the barrier Lyapunov function design and adaptive methods can achieve low steady-state errors with low computational resource consumption. Simulations that verify its effectiveness and superiority are provided herein.

To accelerate the convergence rate of high-dimensional optimization problems, inspired by the cooperative hunting process of spider colonies named Anelosimus eximius, a new A. eximius colony algorithm (AECA) was proposed to solve combinatorial optimization problems. In the AECA, a certain direction component of the problem solution is represented as a certain direction in which a spider travels, so that a high-dimensional optimization problem can be transformed into multiple low-dimensional optimization problems. The AECA includes two intelligent behaviors: the random walk of spiders and the summoning of the initiator. The random walk of spiders ensures the diversity of spider colonies, whereas the summoning of the initiator can accelerate the convergence rate. We theoretically proved that the AECA is globally convergent. The inversion method of asteroid spectrum reflectance template can be used to solve the problem that the measured planetary spectrum is affected by the asteroid absorption effect and improves the accuracy of celestial Doppler difference velocimetry, which uses celestial spectrum to provide the information of velocity measurement for navigation. The essence of this method is the optimal combination problem of intrinsic mode functions (IMFs). We applied the AECA to the inversion of the planetary spectrum reflectance template. Experimental results show that, compared with genetic algorithms (GAs), the AECA can obtain the optimal combination of spectrum reflectance templates faster. In addition, to verify the universality of the AECA, for the classical knapsack problem, the AECA has a better optimization effect, faster convergence rate, and higher stability than other swarm intelligence algorithms such as GA, discrete particle swarm optimization, and the quantum genetic algorithm. (c) 2024 American Society of Civil Engineers.

The X-ray pulsar-based navigation (XNAV) is a feasible navigation method, which provides position information of the spacecraft. However, according to the theory of relativity, the time dilation causes the spacecraft-borne atomic clock frequency change and affects the navigation accuracy. StarNAV is a novel navigation method using relativistic starlight perturbation, which mainly provides velocity information of the spacecraft. In this article, the time dilation modified-TDTOA/StarNAV (TDM-TDTOA/StarNAV) integrated navigation method is proposed. The effect of time dilation on navigation accuracy is analyzed and compensated in the measurement model of time differential time of arrival (TDTOA). In the integrated navigation method, TDM-TDTOA provides the spacecraft's position information, and StarNAV mainly provides the spacecraft's velocity information. Simulation results show that after time dilation modification, the navigation accuracy of TDTOA is increased. The navigation accuracy of TDM-TDTOA/StarNAV integrated navigation method is higher than that of StarNAV and TDM-TDTOA. In addition, for TDM-TDTOA/StarNAV, the more the number of pulsars and interstar angles are observed, the higher the navigation accuracy is. The impact on navigation accuracy caused by the change in the number of interstar angles is greater than that caused by the change in the number of pulsars.

Solar disk velocity difference is an emerging celestial navigation measurement acquired through four spectrometers positioned on the four corners of the quadrangular pyramid. The alignment of the pyramid’s axis with the direction from the sun to the spacecraft is crucial. However, the sun sensor measurement error inevitably leads to the sun direction error, which both significantly affect navigation accuracy. To address this issue, this article proposes an augmented state sun direction/solar disk velocity difference integrated navigation method. By analyzing the impact of the sun direction error on sun direction and solar disk velocity difference measurements, the errors of the solar elevation and azimuth angle are extended to the state vector. The navigation method establishes state and measurement models that consider these errors. Simulation results show that the position error and velocity error of the proposed method are reduced by 97.51% and 96.91% compared with those of the integrated navigation with the sun direction error, respectively. The result demonstrates that the proposed method effectively mitigates the impact of sun direction error on navigation performance. In addition, the proposed method can maintain a satisfactory error suppression effect under different sun direction error values.

Pulsar navigation is a promising autonomous navigation system for spacecraft, which is applicable to the entire solar system. However, the pulsar's directional error and the onboard clock error are two types of systematic errors that seriously reduce navigation accuracy. To solve this problem, a star angle/double-differenced pulse time of arrival(SA/DDTOA) integrated navigation method is proposed. Since measurements obtained by observing different pulsars contain the same clock errors, the measurements can be differed to eliminate the common clock error. Then, the pulsar-differenced measurements at neighbor filtering time can be differed to suppress the effect of the pulsar's directional error on navigation precision. Star angle is used to obtain absolute navigation information, which denotes the angles between the light of sight of Jupiter and that of its background stars. Simulation results demonstrate that the proposed method can eliminate the influence of the onboard clock error and greatly weaken the effects of the pulsar's directional error. The navigation accuracy is better than the traditional star angle/pulse time of arrival integrated navigation method and star angle/pulse time difference of arrival integrated navigation method. In addition, the navigation accuracy of the SA/DDTOA integrated navigation method is less affected by Jupiter's ephemeris error. This work greatly reduces the influence of common systematic errors in pulsar navigation on navigation accuracy.

AbstractTrajectory optimization, an optimal control problem (OCP) in essence, is an important issue in many engineering applications including space missions, such as orbit insertion of launchers, orbit rescue, formation flying, etc. There exist two kinds of solving methods for OCP, i.e., indirect and direct methods. For some simple OCPs, using the indirect methods can result in analytic solutions, which are not easy to be obtained for complicated systems. Direct methods transcribe an OCPs into a finite-dimensional nonlinear programming (NLP) problem via discretizing the states and the controls at a set of mesh points, which should be carefully designed via compromising the computational burden and the solution accuracy. In general, the larger number of mesh points, the more accurate solution as well as the larger computational cost including CPU time and memory [1]. There are many numerical methods have been developed for the transcription of OCPs, and the most common method is by using Pseudospectral (PS) collocation scheme [2], which is an optimal choice of mesh points in the reason of well-established rules of approximation theory [3]. Actually, there have several mature optimal control toolkits based PS methods, such as DIDO [4], GPOPS [5]. The resulting NLP problem can be solved by the well-known algorithm packages, such as IPOPT [6] or SNOPT [7]. However, these algorithms cannot obtain a solution in polynomial-time, and the resulting solution is locally optimal. Moreover, a good initial guess solution should be provided for complicated problems.

Martian river valleys are inextricably linked to Martian researches, including the development of the Martian climate, geological development and shallow water ice distribution. The segmentation of Martian river valleys provides materials for scientific research. Due to the absence of water, however, the features of Martian rivers are not obvious, resulting in inefficient segmentation. In order to realize high-accuracy segmentation, we propose an end-to-end segmentation method of Martian river valleys based on deep learning. We put forward the MDR (multi-scale double residual) convolution module and the TA (triple attention) module to improve Unet, and thus, MDR-Unet-TA. In this network, we replace two 3 x 3 convolution layers in Unet with an MDR convolution module, which uses multi-scale convolution to extract features of multiple sizes, and uses the residual connection to avoid gradient disappearance. In addi-tion, we introduce a TA module into the skip connection, which reduces the feature map difference between the encoder and the decoder, and obtains detailed information during decoding. Experimental results demonstrate that compared with current semantic segmentation networks, MDR-Unet-TA obtains higher accuracy, F1 and IOU scores of 98.78%, 95.77% and 95.12% on the simple test set and 97.50%, 95.12% and 93.50% on the complex test set, respectively. Compared with MultiRes Unet, the improvement by MDR-Unet-Tri1 in accu-racy, F1, and IOU is 0.92%, 2.40% and 2.70% respectively on the simple test set, and 1.98%, 1.84% and 3.58% respectively on the com-plex test set. MDR-Unet-TA improves the segmentation performance significantly compared with the original network, and realizes the end-to-end segmentation of Martian river valleys.& COPY; 2023 COSPAR. Published by Elsevier B.V. All rights reserved.

Background: Current research on the prediction of movement complications associated with levodopa therapy in Parkinson's disease (PD) is limited. levodopa-induced dyskinesia (LID) is a movement complication that seriously affects the life quality of PD patients. One-third of PD patients develop LID within 1 to 6 years of levodopa treatment. This study aimed to construct models based on radiomics and machine learning to predict early LID in PD. Methods: We extracted radiomics features from the T1-weighted MRI obtained in the baseline of 49 PD control and 54 PD with LID in the first 6 years of levodopa therapy. Six brain regions related to the onset of PD were segmented as regions of interest (ROIs). The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Using the machine learning methods of support vector machine (SVM), random forest (RF), and AdaBoost, we constructed radiomics models and hybrid models. The hybrid models combined the radiomics features and the Unified Parkinson's Disease Rating Scale part III (UPDRS III) total score. The five-fold cross-validation was performed and repeated 20 times to validate the stability of the classifiers. We used sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curves, and area under the ROC curve (AUC) for model validation. Results: We selected 33 out of 6138 radiomics features. In the testing set of the radiomics model, the AUC values of the SVM, RF, and AdaBoost classifiers were 0.905, 0.808, and 0.778, respectively, and the accuracies were 0.839, 0.742, and 0.710. The hybrid models had better prediction performance. In the testing set, the AUC values of SVM, RF, and AdaBoost classifiers were 0.958, 0.861, and 0.832, respectively, and the accuracies were 0.903, 0.806, and 0.774. Conclusions: Our results indicate that T1-weighted MRI is valuable in predicting early LID in PD. This work demonstrates that the combination of radiomics features and clinical features has good potential and value for identifying early LID in PD.

Solar disk velocity difference is a novel celestial navigation measurement, which can be obtained by four spectrometers installed on the four corners of the quadrangular pyramid. However, the characteristic of the spacecraft's high dynamic results in the spectrometer installation error. Spectrometer installation error has an obvious influence on navigation accuracy. To solve this problem, an augmented state Sun direction/solar disk velocity difference integrated navigation is proposed. After analyzing the influence of spectrometer installation error on the solar disk velocity difference measurement, the spectrometer installation error is augmented into the state vector. The state model and measurement model of the augmented state Sun direction/solar disk velocity difference integrated navigation considering spectrometer installation error are established. Unscented Kalman filter (UKF) is used to estimate and correct spectrometer installation error. Simulation results show that the proposed method greatly suppresses the effect of spectrometer installation error on navigation accuracy. Furthermore, the effects of the Sun sensor accuracy, the sample time, the number of spectrometers, and the spectrometer installation error value on the navigation performance are analyzed.

Sunlight arrival time delay is an innovative measurement for celestial navigation, which provides the distance information between the spacecraft and the nearby celestial body. For deep space detectors, the time delay measurement noise is time-varying, and its statistics cannot be accurately determined. The too-large deviation between the measured noise covariance matrix (R) and the noise statistics seriously reduces navigation accuracy. A variational Bayesian implicit unscented Kalman filter (VBIUKF) method is proposed and applied to celestial navigation using time delay measurement in this paper. VBIUKF adaptively estimates R using the innovation sequence and the predicted measurement covariance matrix to make R closest to the measurement noise statistics. Simulation results indicate that VBIUKF can obtain high accuracy navigation results under time-varying measurement noise.

Celestial navigation using time delay measurement is an innovative autonomous navigation method. To calculate the equivalent measurement, the numerical method needs to be applied, which is time-consuming. The event-triggered mechanism intermittently and aperiodically processes measurements by judging if the update error has changed drastically. However, its performance is greatly affected by the constant threshold. To solve this problem, a parameter-independent event-triggered implicit unscented Kalman filter (UKF) is proposed and applied to the celestial navigation using time delay measurement. The innovation at the current moment and the updated estimate covariance at the last moment are compared with the previous value instead of the constant threshold. The event is automatically triggered when the accuracy of the state estimate is low. Simulation results indicate that the proposed parameter-independent event-triggered implicit UKF can reduce the running time by reducing unnecessary measurement updates, whose performance will not be affected by any parameter or window size. In a word, the proposed method substitutes the dynamic threshold for the constant threshold, ensuring that its performance will not be affected by any parameter or window size.

Pulsar navigation is a promising deep space autonomous navigation technology, which usually uses the time of arrival (time of arrival, TOA) as the measurement information. However, system errors such as pulsar ephemeris errors and space-borne atomic clock errors have a significant impact on navigation performance. To solve the above problems, an error suppression method for augmented state pulsar integrated navigation based on TOA and time difference TOA (TDTOA) is proposed. By adding the ephemeris error and clock error of each pulsar to the state vector, the TOA and TDTOA measurements are used to estimate and correct them. Simulation results show that this method improves the observability of the pulsar ephemeris and clock errors, eliminates the effect of these systematic errors, and improves navigation accuracy by 29% compared to traditional pulsar navigation.

Time delay is a novel celestial navigation measurement that can provide the distance information of the spacecraft relative to the nearby celestial body. Combining time delay measurement with traditional star angle measurement can greatly improve navigation performance. However, the navigation accuracy will be affected by the ephemeris error of the nearby celestial body. To solve this problem, this article adds the position and velocity of the nearby celestial body to the state vector and estimates them online. The estimated values are used to replace the ephemeris data of the nearby celestial body in the measurement model. Besides, an event-triggered implicit unscented Kalman filter (IUKF) is proposed to reduce unnecessary calculation and shorten the running time. Simulation results indicate that the position error and velocity error of the proposed method are reduced by about 61% and 67% compared with that of the traditional time delay/star angle integrated navigation method (TDSA), respectively. The running time of the proposed method is reduced by about 88% compared with that of the TDSA with the augmented state (AS). In a word, the proposed method can greatly reduce the running time while maintaining high navigation accuracy.

Subjected to the current observation technology, the planetary ephemerides which are used to predict the positions of planets are not accurately known. For .Jupiter exploration, ephemeris error of Jupiter will greatly degrade the performance of navigation system. To improve the autonomous navigation performance, a novel celestial aided time-differenced pulsar navigation method against ephemeris error is proposed. Time-differenced time-of-arrival (TDTOA) is adopted as measurement to eliminate the system bias caused by the ephemeris error of Jupiter. Because TDTOA cannot work individually, the celestial measurement adopting Jupiter as near celestial body is added to provide the position information of the spacecraft with respect to Jupiter. Unscented kinematic and static filter is used as the navigation filter. Simulations demonstrate that the proposed method can acquire similar accuracy compared with the ideal scenario when there is no ephemeris error of Jupiter, which indicates that the impact of ephemeris error of Jupiter can be eliminated effectively by the proposed method. Moreover, the proposed method can acquire better navigation performance than using celestial measurements or TDTOA measurements individually.

To improve the accuracy of estimating the time delay of X-ray pulsar-based navigation (XNAV), this paper proposes a new generalized cross-correlation (GCC) algorithm based on fractional Fourier transform (FrFT). Given that the effect of noise on signal can be suppressed or eliminated in a highly effective manner in some FrFT domain, we identify the optimum FrFT domain before computing the self- and cross-power spectrums of the standard and observed pulsar integrated pulse profiles. We also apply the FrFT of the standard and observed pulsar integrated pulse profiles in the optimum FrFT domain. By using the FrFT-based GCC, we obtain highly accurate estimations of time delay. The experimental results reveal that the proposed algorithm outperforms the conventional GCC in terms of accuracy.

Solar oscillation，causing a dramatic variation of the sunlight spectral central wavelengths and intensity during a short time，has been studied in detail over the years，both observationally and theoretically. Through detecting the sunlight spectral central wavelengths and intensity and recording the moment when solar oscillation occurs，the time delay between the sunlight coming from the Sun directly and the sunlight reflected by a celestial body such as the satellite of planet or asteroid can be obtained. Because the solar oscillation time delay is determined by the relative positions of the spacecraft，reflective celestial body and the Sun，it can be adopted as the navigation measurement to provide the spacecraft's position information. In this paper，a novel celestial navigation method using solar oscillation time delay measurement is proposed. The implicit measurement model of time delay is built，and the Implicit Unscented Kalman Filter （IUKF） is applied. Simulation results indicate that the position error and velocity error of the proposed method for the transfer orbit are about 3.55 km and 0.077 m/s respectively，and for the surrounding orbit are about 1.76 km and 1.57 m/s respectively. The impact of the three factors on the navigation performance is also investigated.

The strap-down inertial navigation system/celestial navigation system (SINS/CNS)-integrated system can be divided into the loosely coupled integrated mode and the tightly coupled integrated mode. Because the loosely coupled integrated mode requires the star sensor to observe at least three stars at the same time to obtain the attitude matrix, the tightly coupled integrated mode, which can still work with only one star, is more practical in reality, especially for the aircraft that need to work in the daytime. The star sensor is a vital part of the CNS, while its installation error impacts the navigation accuracy seriously in both the modes. Therefore, the star sensor installation error of the SINS/CNS-integrated navigation system must be corrected. The installation error calibration has been settled for the loosely coupled SINS/CNS-integrated navigation system when the attitude matrix is available. However, it is still a problem for the tightly coupled mode. This paper proposes a fast calibration method of the star sensor installation error for the tightly coupled SINS/CNS-integrated navigation system based on maneuvers and observability analysis. Simulations indicate that the proposed method is feasible and effective when only one star is visible at a time. The mean position error of the tightly coupled SINS/CNS-integrated navigation decreases by about 63.23% after compensation for the star sensor installation error.

In order to improve the spacecraft capability of autonomous celestial navigation，a celestial Doppler difference/pulsar for formation flying and its integrated navigation method is proposed. The Sun light is strong，and the accuracy of the Sun Doppler difference navigation is high，but it is difficult to provide multi-directional velocity information. Star light is weak，and the accuracy of star Doppler difference navigation is low，but it can provide multi-directional velocity information. The Sun Doppler difference navigation and the star Doppler difference navigation are complementary，but which cannot be fully observable. Using three or more pulsar navigation is completely observable，but the filtering period is longer，and it is difficult to obtain continuous navigation information. The three navigation methods are complementary and can be used for integrated navigation. The extended Kalman filter is used as a navigation filter to fuse the difference and arrival time of the astronomical Doppler，and can provide absolute and relative navigation information for formation flying. Simulation results show that the integrated navigation method for formation flight can provide absolute and relative highly-accurate navigation information.