Spacecraft Autonomous Navigation Research Articles

Research on Intelligent Navigation System Based on Aerospace Big Data Traffic System

This paper studies the navigation technology of the On-Orbit Servicing Spacecraft (OSS) and proposes an overall solution for the OSS navigation system. The composition of the OSS satellite navigation simulation system and simulation supporting environment are studied. Then a new SSUKF navigation filter is proposed based on the hyper spherical distributed feature sampling point transform algorithm (SSUT) and the non-scanning Kalman filter (UKF). The numerical simulation of the OSS system is studied based on MATLAB/RTW. Finally, the SSUKF algorithm and the conventional UKF algorithm are simulated digitally. The effectiveness and advanced nature of the hybrid Kalman filter applied to spacecraft autonomous navigation are verified.

Intelligent Navigation System based on Big Data Traffic System

This paper studies the navigation technology of the On-Orbit Servicing Spacecraft (OSS) and proposes an overall solution for the OSS navigation system. The composition of the OSS satellite navigation simulation system and simulation supporting environment are studied. Then a new SSUKF navigation filter is proposed based on the hyper spherical distributed feature sampling point transform algorithm (SSUT) and the non-scanning Kalman filter (UKF). The numerical simulation of the OSS system is studied based on MATLAB/RTW. Finally, the SSUKF algorithm and the conventional UKF algorithm are simulated digitally. The effectiveness and advanced nature of the hybrid Kalman filter applied to spacecraft autonomous navigation are verified.

Spacecraft autonomous navigation using line-of-sight directions of non-cooperative targets by improved Q-learning based extended Kalman filter

Autonomous optical navigation is one of the most promising techniques to estimate the position and velocity of a spacecraft in Earth orbit without the supports of Earth-based tracking stations. To improve the navigation performance, this paper presents a novel autonomous optical navigation method, where a star camera on the spacecraft is utilized to measure the line-of-sight (LOS) directions of a number of non-cooperative space targets, whose position vectors are supposed to be not precisely known in advance, and an improved Q-learning based extended Kalman filter (IQEKF) is developed to obtain the accurate motion state estimate of both the spacecraft and the space targets based on the LOS direction measurements. The main advantage of the presented method is that the LOS directions of the space targets can be acquired with high-accuracy by using the state-of-the-art star camera, such that the superior navigation accuracy is achievable. In addition, the whole motion state of the spacecraft, such as position, velocity, and attitude, can be obtained with the star camera, in the case that the space targets and the stars are observed simultaneously. The high performance of the presented autonomous navigation method is illustrated through a representative simulation of a medium Earth orbit (MEO) satellite. Furthermore, the simulation results indicate that the IQEKF yields more accurate solutions than the traditional navigation filtering algorithms.

Multi-Normal-Inverse Wishart mixture distribution based nonlinear filter with applications

In Kalman filter-based spacecraft autonomous celestial navigation, Inertial/Celestial integrated navigation system and asynchronous multi-sensor data fusion, complex environments and sensor alignment errors are likely to lead to inaccurate statistical priors for the noise covariance matrices and the non-zero measurement noise mean vector (MNMV). To address this issue, this paper firstly proposes a Multi-Normal-Inverse Wishart (MNIW) mixture distribution modelling the joint probability density function (PDF) for one-step prediction and measure likelihood, then the MNIW mixture distribution is decomposed into a Gaussian hierarchy, and finally the posterior estimates of the state and variables are obtained using variational Bayesian technique. In this paper, a Multi-Normal-Inverse Wishart mixture distribution-based variational Bayesian extended Kalman filter (VB-EKF) is proposed, in which a first-order Taylor expansion is used to address the non-linear problem. The proposed filter can be used to address non-linear filtering problem with inaccurate noise covariance matrices and measurement bias. Simulations of spacecraft autonomous celestial navigation validate the effectiveness and superiority of the proposed filter.

Symmetry between Some Methods of Autonomous Spacecraft Navigation and Methods of Determining Their Position from Earth

Symmetry between Some Methods of Autonomous Spacecraft Navigation and Methods of Determining Their Position from Earth

Robust interacting multiple model cubature Kalman filter for nonlinear filtering with unknown non-Gaussian noise

Estimating noise covariance matrices and suppressing outliers simultaneously is a huge challenge because they are coupled. In order to solve the nonlinear filtering problem with unknown non-Gaussian noise, the maximum correntropy criterion (MCC) is used to suppress the outliers, while the variational Bayesian (VB) technique is used to estimate the one-step prediction error covariance matrix (PECM) and the measurement noise covariance matrix (MNCM), and the nonlinear problem is solved by the third-order spherical radial cubature rule. In this paper, nominal measurement is constructed to eliminate the influence of outliers on the recursive estimation of MNCM. Through these operations, the variational Bayesian and maximum correntropy based cubature Kalman filter (VBMCCKF) and the maximum correntropy cubature Kalman filter (MCCKF) are derived. The interacting multiple model (IMM) fusion framework is used to promote the collaborative work of VBMCCKF and MCCKF, thus a novel filter is proposed in this paper. The simulation verifies the validity and universal applicability of the proposed filter. The estimation error of the proposed filter in spacecraft autonomous celestial navigation is about 30% less than that of the existing filter with the best performance.

Analytical Nonlinear Observability Analysis for Spacecraft Autonomous Relative Navigation

Spacecraft autonomous relative navigation is widely required in the field of on-orbit services. In previous works, it has been shown that the monocular sequential images (MSIs) is a cost-effective autonomous relative navigation method. However, given the lack of size and initial orbit of noncooperative targets, the relative navigation system (RNS) with MSIs cannot ensure to estimate the entire relative motion states during the whole observation process. It is critical to perform an online observability analysis for RNS with MSIs to ensure autonomous relative motion estimation validity and accuracy. An analytical nonlinear observability analysis method is proposed in this article to improve the computational efficiency. We study the attributes of the relative orbit dynamics model and Lie algebra operations, and find that the observability matrix is only related to the limited states. The relationship among those states, rank, and the Frobenius norm of observability matrix is demonstrated, which simplify the observability criteria conditions and the degree measurements in analytical forms. In addition, based on the proposed method, we analyze the influence of the orbital manifolds on the observability degree of RNS with MSIs. Several suggestions are proposed for orbital manifolds detecting. The conclusion is a theoretical supplement to the past numerical research results. Finally, we implement the numerical simulations, and the current study’s findings verified the effectiveness of the proposed observability analysis method.

A Novel Visual-Based Terrain Relative Navigation System for Planetary Applications Based on Mask R-CNN and Projective Invariants

In the framework of autonomous spacecraft navigation, this manuscript proposes a novel vision-based terrain relative navigation (TRN) system called FederNet. The developed system exploits a pattern of observed craters to perform an absolute position measurement. The obtained measurements are thus integrated into a navigation filter to estimate the spacecraft state in terms of position and velocity. Recovering crater locations from elevation imagery is not an easy task since sensors can generate images with vastly different appearances and qualities. Hence, several problems have been faced. First, the crater detection problem from elevation images, second, the crater matching problem with known craters, the spacecraft position estimation problem from retrieved matches, and its integration with a navigation filter. The first problem was countered with the robust approach of deep learning. Then, a crater matching algorithm based on geometric descriptors was developed to solve the pattern recognition problem. Finally, a position estimation algorithm was integrated with an Extended Kalman Filter, built with a Keplerian propagator. This key choice highlights the performance achieved by the developed system that could benefit from more accurate propagators. FederNet system has been validated with an experimental analysis on real elevation images. Results showed that FederNet is capable to cruise with a navigation accuracy below 400 meters when a sufficient number of well-distributed craters is available for matching. FederNet capabilities can be further improved with higher resolution data and a data fusion integration with other sensor measurements, such as the lunar GPS, nowadays under investigation by many researchers.

3D Component Segmentation Network and Dataset for Non-Cooperative Spacecraft

Spacecraft component segmentation is one of the key technologies which enables autonomous navigation and manipulation for non-cooperative spacecraft in OOS (On-Orbit Service). While most of the studies on spacecraft component segmentation are based on 2D image segmentation, this paper proposes spacecraft component segmentation methods based on 3D point clouds. Firstly, we propose a multi-source 3D spacecraft component segmentation dataset, including point clouds from lidar and VisualSFM (Visual Structure From Motion). Then, an improved PointNet++ based 3D component segmentation network named 3DSatNet is proposed with a new geometrical-aware FE (Feature Extraction) layers and a new loss function to tackle the data imbalance problem which means the points number of different components differ greatly, and the density distribution of point cloud is not uniform. Moreover, when the partial prior point clouds of the target spacecraft are known, we propose a 3DSatNet-Reg network by adding a Teaser-based 3D point clouds registration module to 3DSatNet to obtain higher component segmentation accuracy. Experiments carried out on our proposed dataset demonstrate that the proposed 3DSatNet achieves 1.9% higher instance mIoU than PointNet++_SSG, and the highest IoU for antenna in both lidar point clouds and visual point clouds compared with the popular networks. Furthermore, our algorithm has been deployed on an embedded AI computing device Nvidia Jetson TX2 which has the potential to be used on orbit with a processing speed of 0.228 s per point cloud with 20,000 points.

Evaluation of earth rotation parameters from modernized GNSS navigation messages

Modernized navigation messages of global navigation satellite systems like GPS CNAV include earth rotation parameters (ERPs), namely the pole coordinates and UT1-UTC (∆UT1) as well as their rates. Broadcast ERPs are primarily needed for space-borne GNSS applications that require transformations between earth-fixed and inertial reference frames like navigation in earth orbit as well as to the moon. Based on a global tracking network of 23 stations, broadcast ERP values are obtained for the global systems GPS and BeiDou as well as the regional QZSS and IRNSS. Subsequent data sets at daily intervals show polar motion discontinuities of 0.4 to 0.7 mas for GPS, QZSS, and IRNSS, whereas BDS is worse by a factor of about two. Discontinuities in ∆UT1 range from 0.17 to 0.45 ms. External comparison with the C04 series of the International Earth Rotation and Reference Systems Service results in polar motion RMS differences of 0.3 to 1.0 mas and ∆UT1 differences of about 0.13 ms for GPS, QZSS, and IRNSS. Due to less frequent update intervals, BDS performs worse by a factor of 2 – 4. In view of the current GNSS-based positioning errors at geostationary or even lunar distances, the accuracy of GPS, QZSS, and IRNSS broadcast ERPs is sufficient to support autonomous spacecraft navigation without the need for external data.

Autonomous Navigation of Relativistic Spacecraft in Interstellar Space

This paper introduces a general autonomous navigation method for any spacecraft for which relativistic effects are significant. It builds on a relativistic observation model and represents the foundational step in the development of interstellar navigation. By comparing astrometric and spectrometric measurements obtained in two different reference frames, the method estimates instantaneous spacecraft position and velocity, and distances to observed stars. The paper first derives relativistic autonomous observation equations, which are more general than those provided in the existing literature. It then presents an autonomous navigation algorithm that compares favorably to a nonrelativistic approach. A case study investigates the method in the context of technological details of a mission, including certain sources of noise and disturbance in the interstellar medium. Specifically, a state-of-the-art spacecraft traveling to Proxima Centauri at can estimate its position and velocity with less than 0.001% error, and distances to stars it observes with less than 1% error. Measurement noise is the dominant source of this error; thus, the algorithm is highly sensitive to sensor performance. A simple running average reduces these errors by more than an order of magnitude, which suggests that this algorithm is suitable as a first step in a realistic, recursive navigation algorithm.

Q-Learning-Based Target Selection for Bearings-Only Autonomous Navigation

This paper presents a Q-learning-based target selection algorithm for spacecraft autonomous navigation using bearing observations of known visible targets. For the considered navigation system, the position and velocity of the spacecraft are estimated using an extended Kalman filter (EKF) with the measurements of inter-satellite line-of-sight (LOS) vectors obtained via an onboard star camera. This paper focuses on the selection of the appropriate target at each observation period for the star camera adaptively, such that the performance of the EKF is enhanced. To derive an effective algorithm, a Q-function is designed to select a proper observation region, while a U-function is introduced to rank the targets in the selected region. Both the Q-function and the U-function are constructed based on the sequence of innovations obtained from the EKF. The efficiency of the Q-learning-based target selection algorithm is illustrated via numerical simulations, which show that the presented algorithm outperforms the traditional target selection strategy based on a Cramer-Rao bound (CRB) in the case that the prior knowledge about the target location is inaccurate.

Spacecraft Autonomous Navigation Using the Doppler Velocity Differences of Different Points on the Solar Disk

The solar differential rotation results in different velocities on the solar disk. The Doppler velocities of different points on the solar disk observed by the spacecraft are also different, which are functions of the current position of the spacecraft. Thus, using the velocities’ information or their differences, the position of the spacecraft can be estimated. The advantage of velocity difference measurement is that the influence of some systematic error, such as the spectrometer error and the periodic variation of the sun's velocity on the navigation performance, can be eliminated. In this article, a new autonomous navigation method using the Doppler velocity differences of different points on the solar disk is proposed. Taking solar explorer as an example, simulations show that the proposed method can effectively restrain systematic errors compared with the traditional Doppler velocity method.

Closed-Loop Software Architecture for Spacecraft Optical Navigation and Control Development

A software architecture is discussed to develop, run, and test novel autonomous visual spacecraft navigation and control methods in a realistic simulation. This architecture harnesses two main components: a high-fidelity, faster-than-real-time, astrodynamics simulation framework; and a sister software package to dynamically visualize the simulation environment. Maneuvers such as fly-bys and orbit insertions occur over short periods of time and must occur autonomously. Yet, there are no open-source software packages that provide fully coupled spacecraft environments and Flight Software (FSW) enabling Optical Navigation (OpNav) mission scenarios. The presented tool consists of the Basilisk∗ astrodynamics framework interfacing with a Unity-based visualization Vizard that provides a synthetic image stream of a camera sensor. This modular and extensible setup allows optical guidance, navigation and control (GNC) algorithms to be run in a closed-loop format purely in software. The optical measurements are generated in the visualization and passed to the simulation, allowing for real-time control and decision making. This Vizard software has the ability to import shape-models, planet maps, and move into an instrument point-of-view. Paired with open-source image processing libraries, these combined components provide all the necessary pieces to fully simulate autonomous, closed-loop, OpNav scenarios in a faster-than-real-time configuration. This allows for progress in the autonomy sector, as full-fledged FSW can be tested in a real flight environment. Furthermore, this enables more realistic and extensive testing of the software, which in turn increases reliability of the GNC methods as they are refined. This paper presents the Basilisk and Vizard interface architecture, its performance, and develops a example scenario. The image processing methods are displayed and the visualization scenes are validated for pointing purposes, which in turns allows to develop an autonomous pointing algorithm developed in this software environment.

Q‐learning for noise covariance adaptation in extended KALMAN filter

AbstractThe extended Kalman filter (EKF) is a widely used method in navigation applications. The EKF suffers from noise covariance uncertainty, potentially causing it to perform poorly in practice. This paper attempts to suppress the unfavorable effect of the noise covariance uncertainty to the EKF in the framework of reinforcement learning. The proposed state estimation algorithm combines the EKF and a Q‐learning method, where a covariance adaptation strategy is designed based on the Q‐values, leading to a gradual improvement in the estimation performance. The resultant algorithm is called the Q‐learning extended Kalman filter (QLEKF), which is less sensitive to the noise covariance uncertainty. To evaluate the estimation error behavior in nonlinear uncertain systems, the stability of the filtering algorithm is investigated. A numerical simulation for spacecraft autonomous navigation is implemented to demonstrate the efficiency of the QLEKF. It is shown that the presented algorithm outperforms a traditional EKF and an adaptive extended Kalman filter (AEKF) in estimation accuracy.

Отработка технологии автономной навигации космических аппаратов дальнего космоса на Международной космической станции

Отработка технологии автономной навигации космических аппаратов дальнего космоса на Международной космической станции

Comparative estimation of the accuracy of methods of autonomous navigation of small spacecraft in formation flying

The results of comparative estimation of the accuracy of autonomous navigation of small spacecraft in formation flying are presented. To carry out the research, the zenith method and the method of navigation by orbital references were chosen. These methods are based on measurements of the angular position of the Earth and an orbital reference point relative to navigational stars. Assumptions concerning the central terrestrial gravitational field and the normality of errors of the on-board navigation measurements with known constant variability were introduced in the studies. The studies were carried out using the theory of analytical estimation of the accuracy of spacecraft autonomous navigation methods. The use of this theory makes it possible to obtain the covariance error matrix of the required vector of navigation parameters and to estimate the potential (maximum achievable) characteristics of the accuracy of the navigation methods used. A dimensionless navigation error coefficient was chosen as an indicator of the accuracy of small spacecraft navigation method. The coefficient is associated with the elements of the main diagonal of the covariance matrix, it characterizes the precision properties of the method, is integrated by nature and does not depend on the volume and accuracy of the results of navigation measurements. The criterion of expediency of applying the method of determining the parameters of motion of the spacecraft center of mass is based on the comparison of navigation error rates. The presented results allow us to make reasonable choice of the method of autonomous navigation and of the composition of the onboard control of small spacecraft in formation flying.

Detection and Localization of Interference and Useful Signal Extreme Points in Closely Coupled Multiconductor Transmission Line Networks

This study highlights the importance of detecting and localizing useful and interference signal extreme points in multiconductor transmission lines (MCTL) by developing a new approach for detecting and localizing signal extreme points in MCTL networks of arbitrary complexity. A radio-electronic component is presented as a network consisting of a number of connected MCTL sections. Each MCTL section is divided into segments and the number of segments is set by the user. The approach is based on a quasi-static calculation of signal waveforms at any point (segment) along each conductor of an MCTL. The block diagrams of the developed algorithms are presented. Using the approach, a number of investigations have been done which include the following: the signal maximum detection and localization in the meander lines with one and two turns, the influence of ultrashort pulse duration on localization of its extreme points in the printed circuit board (PCB) bus of a spacecraft autonomous navigation system, the influence of ultrashort pulse duration on localization of crosstalk extreme points in the PCB bus, and the simulation of electrostatic discharge effects on the PCB bus. There are also some investigations with optimization methods presented. A genetic algorithm (GA) was used to optimize the influence of ultrashort pulse duration on localization of the pulse and crosstalk extreme points in the PCB bus. Furthermore, the GA was used to optimize the PCB bus loads by criteria of the peak voltage minimization. A similar investigation was carried out with the evolution strategy. The obtained results help us to argue that the signal extreme points can be detected both in structures with different configurations and applying different excitations.

Crater Detection Using Unsupervised Algorithms and Convolutional Neural Networks

Craters are among the most abundant features on the surface of many planets with great importance for planetary scientists. They reveal chronology information about planets and may be used for autonomous spacecraft navigation and landing. Although numerous research efforts have been carried out in the field of crater detection, existing crater detection algorithms (CDAs) are only helpful in a limited number of applications. A promising crater detection approach involves two main steps: 1) hypothesis generation (HG) and 2) hypothesis verification (HV). During HG, potential crater locations are detected. The validity of the hypothesized crater locations is then tested in a HV step. In this context, we discuss some commonly used algorithms for HG such as highlight-shadow region detection and Hough transform as well as our novel and enhanced algorithms based on interest point detection and convex grouping. A key objective of this paper is to analyze their performance while paying special attention to how they affect the accuracy of the verification step. To deal with different size craters, we focus on multiscale HG. For HV, we have chosen convolutional neural networks which have recently achieved state-of-the-art performance in many computer vision applications. Due to the variation of test sets in the literature, it is often challenging to compare the performance of different CDAs in a fair way. In this paper, we present a comprehensive performance evaluation and comparison of CDAs. Each algorithm has been trained/tested using common data sets generated by a systematic approach.

Diagnostics of the printed circuit board bus of a spacecraft autonomous navigation system with using the N-norms

The paper presents the study of the printed circuit board bus of the autonomous navigation system of a spacecraft using the amplitude criteria based on N-norms. During the study, the authors revealed the most vulnerable places on the bus by calculating the five N-norm profiles along the active conductor of the bus (in 280 segments). The results showed that the peak voltage was reduced by 6.2 % to the end of the active conductor, the signal rate changed sharply but insignificantly (within ±9.3 %) along the active conductor and reduced (by 20 %) towards its end. However, the energy characteristics of the excitation along the active conductor changed insignificantly.