Satellite Autonomous Navigation Research Articles

X-ray-to-visible light-field detection through pixelated colour conversion

Light-field detection measures both the intensity of light rays and their precise direction in free space. However, current light-field detection techniques either require complex microlens arrays or are limited to the ultraviolet–visible light wavelength ranges1–4. Here we present a robust, scalable method based on lithographically patterned perovskite nanocrystal arrays that can be used to determine radiation vectors from X-rays to visible light (0.002–550 nm). With these multicolour nanocrystal arrays, light rays from specific directions can be converted into pixelated colour outputs with an angular resolution of 0.0018°. We find that three-dimensional light-field detection and spatial positioning of light sources are possible by modifying nanocrystal arrays with specific orientations. We also demonstrate three-dimensional object imaging and visible light and X-ray phase-contrast imaging by combining pixelated nanocrystal arrays with a colour charge-coupled device. The ability to detect light direction beyond optical wavelengths through colour-contrast encoding could enable new applications, for example, in three-dimensional phase-contrast imaging, robotics, virtual reality, tomographic biological imaging and satellite autonomous navigation.

Nonlinear Autoregressive Model With Exogenous Input Recurrent Neural Network to Predict Satellites’ Clock Bias

The prediction of Satellites’ Clock Bias (SCB) plays an important role in optimizing the clock bias parameters in navigation messages, meeting the requirements of real-time dynamic precise point positioning and providing the prior information required for satellite autonomous navigation. Satellite-borne atomic clocks are often affected by many factors in space, which makes it difficult to describe the clocks’ bias and behavior with fixed model to achieve reliable high-precision prediction. The composition and characteristics of clock bias for satellite-borne atomic clock are described and analyzed, a clock bias prediction algorithm based on Nonlinear autoregressive model with exogenous input (NARX) recurrent neural network is proposed, the advantages of this model in SCB and other time series prediction are introduced in detail. The SCB data from four different clock types are selected for calculation and analysis. The comparative results show that, for both 6h and 24h forecasts, the accuracy and stability of NARX model are significantly better than three commonly used models, especially in the prediction of satellite cesium atomic clock.

Analysis of BDS-3 distributed autonomous navigation based on BeiDou system simulation platform

Satellite autonomous navigation is an important function of the BeiDou-3 navigation System (BDS-3). Satellite autonomous navigation means that the navigation satellite uses long-term forecast ephemeris and Inter-Satellite Link (ISL) measurements to determinate its own spatial position and time reference without the support of the ground Operation and Control System (OCS) for a long time to ensure that the navigation system can normally maintain the time and space reference. This paper aims to analyze the feasibility of distributed autonomous navigation algorithms. For the first time, a ground parallel autonomous navigation test system (GPANTS) is built. The performance of distributed autonomous navigation is then analyzed using the two-way ISL ranging of BDS-3 satellites. First, the BDS simulation platform and the GPANTS are introduced. Then, the basic principles of distributed satellite autonomous orbit determination and time synchronization based on ISL measurements are summarized. Preliminary evaluation of the performance of the BDS-3 constellation autonomous navigation service under ideal conditions through simulation data. Then the performance of autonomous navigation for 22 BeiDou-3 satellites using ISL measurements is evaluated. The results show that when satellites operate autonomously for 50 days without the support of any ground station, the User Range Error (URE) of autonomous orbit determination is better than 3 m, and the time synchronization accuracy is better than 4 ns.

Analysis of Time Scale Algorithm in Satellite Autonomous Navigation Mode

Using satellite clock to establish and maintain constellation reference time datum is the basis of satellite autonomous navigation, reference time Datum algorithm is the core of realization, the paper first elaborated the commonly used time scale algorithm: ALGOS algorithm and Kalman algorithm, ALGOS is the most commonly used reference time Datum algorithm in the world, The Kalman algorithm can assign weights dynamically, but there are some disadvantages of slow convergence speed. Based on the analysis of the computational complexity, traffic requirements and algorithm convergence of the reference time Datum algorithm in the space satellite environment, the experimental results show that it takes 3 minutes for the ALGOS algorithm to complete 1 reference time datum calculation, the communication volume is about 398kb, and the algorithm converges about 6-9 seconds. The Power acquisition strategy is the most core part of the reference time algorithm, the paper finally gives the experimental analysis to the different power acquisition strategy, the results show that, from the stability point of view, the short stability of the 12-hour power calculation is better than the short stability of the calculation of 5 days of right, but its instability is slightly lower than that of 1 days and 5 days.

Parallel model adaptive Kalman filtering for autonomous navigation with line-of-sight measurements

In this paper, a parallel model adaptive Kalman filtering algorithm is presented for multiple sensors estimation fusion when the measurement noise statistics are uncertain. As a typical adaptive filtering algorithm, the multiple model adaptive estimation tries to reduce the dependency of the filter on the noise parameters. It utilizes multiple models with different noise levels to estimate the state and combines the model-dependent estimates with model probability. However, with the increase in the number of active sensors, a large number of models are required to cover the entire range of the possible noise parameter values, which can become computationally infeasible. The main goal of this work is to incorporate the noise statistic estimator in the framework of the multiple model adaptive estimation, such that only two models are required for each sensor, which significantly reduce the complexity of the estimator. The advantage of the presented algorithm to deal with the model uncertainty is studied analytically. The high performance of the parallel model adaptive Kalman filtering for autonomous satellite navigation using inter-satellite line-of-sight measurements is illustrated in comparison with a robust Kalman filter, an intrinsically Bayesian robust Kalman filter, and the traditional multiple model adaptive estimation.

Sigma-Point Kalman Filter Algorithm in the Problem of GNSS Signal Parameters Estimation in Non-Coherent Tracking Mode in Spacecraft Autonomous Navigation Equipment

The paper presents a multiloop system for noncoherent tracking of the radionavigation parameters of global navigation satellite system (GNSS) signals in autonomous satellite navigation system. Comparative analysis of accuracies of traditional tracking system with discriminators and filter in the tracking loop and the proposed system without discriminators is conducted. RMS errors of estimates, pull-in range and the probability of signal acquisition are studied under various SNR values. Experimental tests of derived noncoherent tracking loop are performed.

Autonomous navigation algorithm based on AUKF filter about fusion of geomagnetic and sunlight directions

PurposeThe geomagnetic field vector is a function of the satellite’s position. The position and speed of the satellite can be determined by comparing the geomagnetic field vector measured by on board three-axis magnetometer with the standard value of the international geomagnetic field. The geomagnetic model has the disadvantages of uncertainty, low precision and long-term variability. Therefore, accuracy of autonomous navigation using the magnetometer is low. The purpose of this paper is to use the geomagnetic and sunlight information fusion algorithm to improve the orbit accuracy.Design/methodology/approachIn this paper, an autonomous navigation method for low earth orbit satellite is studied by fusing geomagnetic and solar energy information. The algorithm selects the cosine value of the angle between the solar light vector and the geomagnetic vector, and the geomagnetic field intensity as observation. The Adaptive Unscented Kalman Filter (AUKF) filter is used to estimate the speed and position of the satellite, and the simulation research is carried out. This paper also made the same study using the UKF filter for comparison with the AUKF filter.FindingsThe algorithm of adding the sun direction vector information improves the positioning accuracy compared with the simple geomagnetic navigation, and the convergence and stability of the filter are better. The navigation error does not accumulate with time and has engineering application value. It also can be seen that AUKF filtering accuracy is better than UKF filtering accuracy.Research limitations/implicationsGeomagnetic navigation is greatly affected by the accuracy of magnetometer. This paper does not consider the spacecraft’s environmental interference with magnetic sensors.Practical implicationsMagnetometers and solar sensors are common sensors for micro-satellites. Near-Earth satellite orbit has abundant geomagnetic field resources. Therefore, the algorithm will have higher engineering significance in the practical application of low orbit micro-satellites orbit determination.Originality/valueThis paper introduces a satellite autonomous navigation algorithm. The AUKF geomagnetic filter algorithm using sunlight information can obviously improve the navigation accuracy and meet the basic requirements of low orbit small satellite orbit determination.

Time synchronization of new-generation BDS satellites using inter-satellite link measurements

Autonomous satellite navigation is based on the ability of a Global Navigation Satellite System (GNSS), such as Beidou, to estimate orbits and clock parameters onboard satellites using Inter-Satellite Link (ISL) measurements instead of tracking data from a ground monitoring network. This paper focuses on the time synchronization of new-generation Beidou Navigation Satellite System (BDS) satellites equipped with an ISL payload. Two modes of Ka-band ISL measurements, Time Division Multiple Access (TDMA) mode and the continuous link mode, were used onboard these BDS satellites. Using a mathematical formulation for each measurement mode along with a derivation of the satellite clock offsets, geometric ranges from the dual one-way measurements were introduced. Then, pseudoranges and clock offsets were evaluated for the new-generation BDS satellites. The evaluation shows that the ranging accuracies of TDMA ISL and the continuous link are approximately 4 cm and 1 cm (root mean square, RMS), respectively. Both lead to ISL clock offset residuals of less than 0.3 ns (RMS). For further validation, time synchronization between these satellites to a ground control station keeping the systematic time in BDT was conducted using L-band Two-way Satellite Time Frequency Transfer (TWSTFT). System errors in the ISL measurements were calibrated by comparing the derived clock offsets with the TWSTFT. The standard deviations of the estimated ISL system errors are less than 0.3 ns, and the calibrated ISL clock parameters are consistent with that of the L-band TWSTFT. For the regional BDS network, the addition of ISL measurements for medium orbit (MEO) BDS satellites increased the clock tracking coverage by more than 40% for each orbital revolution. As a result, the clock predicting error for the satellite M1S was improved from 3.59 to 0.86 ns (RMS), and the predicting error of the satellite M2S was improved from 1.94 to 0.57 ns (RMS), which is a significant improvement by a factor of 3–4.

Improving extended Kalman filter algorithm in satellite autonomous navigation

With the goal of reducing dependence on ground tracking systems, satellite autonomous navigation technologies are developed quickly in the recent several decades. However, precise orbit determination at high orbital altitudes is an important and challenging problem. In this paper, the nonlinear real-time orbit determination problem is investigated. Combined with satellite dynamical model, extended Kalman filter is explored to estimate satellite orbit parameters. Further, considering errors occur in linearization processing, two improvements for the extended Kalman filter algorithm, i.e. extended Kalman filter-I and extended Kalman filter-II, are proposed based on Lagrange’s mean value theorem, and respectively focus on choosing better linear expansion point and Jacobian matrix calculation point. Extensive simulations show that extended Kalman filter-I and extended Kalman filter-II significantly enhance orbit accuracy, compared with extended Kalman filter. And the increases in calculation complexity are acceptable. Finally, the robustness of extended Kalman filter-I and extended Kalman filter-II is analyzed by given different initial position errors, and results show that extended Kalman filter-I and extended Kalman filter-II have better robustness than extended Kalman filter.

Autonomous navigation for lunar satellite using X‐ray pulsars with measurement faults

X-ray pulsar-based navigation (XNAV) is a navigation method using celestial X-ray source observations for spacecraft orbit determination. However, if the measurements are not reliable due to any kind of malfunction, the performance of XNAV may result in considerable errors and even divergence. In this study, a new algorithm called robust extended Kalman filter (REKF) is proposed for the lunar satellite autonomous navigation system, which is robust against measurement malfunctions. First, the satellite dynamic model applied perturbations is derived under the J2 perturbation of the Moon. The pulse time-of-arrival (TOA) is used to build the observation model. Second, the performance of XNAV system is discussed by analysing the transformation error of TOA, system observability and controllability. Then, an adaptive measurement noise scale factor (AMNSF) is designed by using the innovation sequence in REKF. Meanwhile, the gain matrix is modified by adding the AMNSF to reduce the influence of malfunction and enhance the robustness of XNAV system. Finally, the simulation results show the proposed navigation scheme is valid and feasible, and it is appropriate for lunar satellite autonomous navigation.

Implicit UKF and its observability analysis of satellite stellar refraction navigation system

Satellite autonomous navigation by stellar refraction is a type of autonomous celestial navigation method that uses high-accuracy star sensors instead of Earth sensors to provide information regarding Earth's horizon. There are two kinds of measurements of satellite stellar refraction navigation. They are the apparent height and the stellar refraction angle. A better navigation performance can be achieved by the stellar refraction angle rather than the refraction apparent height. However, the measurement model of the stellar refraction angle is an implicit function. The traditional UKF and observability analysis methods are not applicable to the system with an implicit measurement model. The implicit UKF (IUKF) and its observability analysis method are proposed in this paper. The equivalent observability matrix is constructed based on the IUKF algorithm. Furthermore, the best scheme for observation of the stellar refraction angle is studied using this new IUKF and its observability analysis. Both the observability analysis and simulation results show that a significant improvement in navigation performance can be achieved by the rotating observation of the stellar refraction angle.

How to Get the Solar Direction from Skylight Polarization for Satellite Autonomous Navigation

The solar direction is an important clue for satellite autonomous navigation. A new method for obtaining the solar direction from skylight polarization for satellite navigation is discussed. The new method is using two E-vector polarization sensors to get solar direction, and the view field of the polarization sensor is discussed.

Long-term Clock Bias Prediction Based on An ARMA Model

The long-term and reliable prediction of satellite clock bias (SCB) is an important prerequisite for realizing the satellite autonomous navigation and orbit determination. Considering the shortcomings of the quadratic polynomial model (PM) and gray system model (GM) in the long-term prediction of SCB, a new prediction method of SCB based on an ARMA (Auto-Regressive Moving Average) model is proposed to represent the variation characteristics of SCB more accurately. In this paper, a careful precision analysis of the 90-day SCB prediction is made to verify the feasibility and validity of this proposed method by using the IGS (International GNSS Service) clock data. According to the variation characteristics of each satellite clock, the pattern recognition, modeling and prediction of SCB are conducted, and the detailed comparison is made with the other three models at the same time. The results show that adopting the ARMA model can effectively improve the accuracy of long-term SCB prediction.

A Universe Light House — Candidate Architectures of the Libration Point Satellite Navigation System

In view of the shortcomings of existing satellite navigation systems in deep-space performance, candidate architectures which utilise libration point orbits in the Earth-Moon system are proposed to create an autonomous satellite navigation system for lunar missions. Three candidate constellations are systematically studied in order to achieve continuous global coverage for lunar orbits: the Earth-Moon L1,2 two-satellite constellation, the Earth-Moon L2,4,5 three-satellite constellation and the Earth-Moon L1,2,4,5 four-satellite constellation. After a thorough search for possible configurations, the latter two constellations are found to be the simplest feasible architectures for lunar navigation. Finally, an autonomous orbit determination simulation is performed to verify the autonomy of the system and two optimal configurations are obtained in a comprehensive consideration of coverage and autonomous orbit determination performance.

A starlight refraction scheme with single star sensor used in autonomous satellite navigation system

For autonomous satellite navigation, the method based on stellar refraction is studied in this paper. In the previous studies, two star sensors have been used for navigation. Actually, only one star sensor is sufficient for navigation. The additional sensor will result in an extra burden for the initial alignment process and design cost. In this paper, an autonomous satellite navigation scheme based on stellar refraction with a single star sensor is presented. The installed angle of star sensor is closely related with the navigation precision of satellite, and the refraction star identification is crucial in stellar refraction method. Hence the determination of installed angle and refraction star identification are also considered. Finally, to verify the feasibility of the proposed scheme, a simulation for low-Earth-orbit (LEO) satellite is carried out and its result indicates that the proposed method is practical with high-precision.

Augmentation of XNAV System to an Ultraviolet Sensor-Based Satellite Navigation System

X-ray pulsar-based navigation (XNAV) using one X-ray detector is investigated as an augmentation to the capability of an ultraviolet (UV) sensor-based satellite autonomous navigation system. The satellite state dynamics are analyzed to establish the dynamical equations of the satellite autonomous navigation system. A time transformation equation that accounts for relativistic effects is presented and the measurement model of the XNAV system is derived using pulse phase information from only one pulsar. The measurement model of the UV sensor-based satellite navigation system is presented using the Earth image information from the UV sensor. In order to integrate the measurements from the X-ray sensor and the UV sensor, a federated filter is developed to provide the optimal simultaneous estimation of position and velocity of the satellite. The concept is demonstrated on a GPS orbit and a geosynchronous orbit and it is found that the performance of the integrated satellite navigation system is improved with respect to that of the UV sensor-based satellite navigation system.

Modified unscented Kalman filtering and its application in autonomous satellite navigation

In this paper, a modified unscented Kalman filter (UKF) for nonlinear stochastic systems is proposed, and it is applied to autonomous orbit determination for Earth satellites. Based on some standard results about the boundedness of stochastic processes and a new formulation of the unscented transformation (UT), it is demonstrated that the design of the noise covariance matrix plays an important role in enhancing the filter stability. Furthermore, a particular design of the noise covariance matrix is proposed as a modification of the UKF. The modified UKF is less sensitive to the initial error than the usual one. High performance of the modified UKF is illustrated in comparison with the usual one by using the real data obtained from an Earth sensor.

Satellite Autonomous Navigation Based on Image Motion Analysis

A novel approach for autonomous navigation of satellites is presented. The proposed navigation concept uses robust image motion tracking for acquisition of navigational data and requires minimum a priori information. The system incorporates a single compact image sensor and a high speed optical image processing unit to provide real-time performances. With the possibility of determination of both position and attitude estimates this system could be a basis for full autonomous operation of a satellite. This can be of particular interest in context with missions to other planets: Mars, Europa, etc.

The Use of Television Signals of Stationary Television Centers for Navigation Determination of Low-Orbit Satellites

For systems of autonomous satellite navigation, it is proposed to use television signals of stationary television centers positioned along the flight path in the band of satellite radio coverage as sources of primary navigation information. Television signal processing allows one to determine the time instants of transit through traverses of the television centers. An algorithm of calculating the parameters of satellite motion by times of transit through traverses is developed.

An adaptive filter with periodic gain, and its application in autonomous satellite navigation

In the paper an Adaptive Filter with PEriodic Gain (AFPEG) is presented, which is applicable to Discrete-time Linear Periodic Systems (DLPS) and nonlinear systems with a periodic nominal trajectory. Its convergence properties subject to the DLPS are proved. Compared to conventional adaptive filtering algorithms, the AFPEG reduces on-line computation with limited storage requirements. The AFPEG is applied to an autonomous satellite navigation system. Simulation results demonstrate its effectiveness in improving the accuracy of navigation.