Celestial Navigation System Research Articles

Star angle modified with relativistic effects/StarNAV integrated navigation method for Mars exploration

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.

Correcting Measurements of Launch Vehicle’s Angular Motion Parameters of a Strapdown Inertial Navigation System with the Use of a Celestial Navigation System

Correcting Measurements of Launch Vehicle’s Angular Motion Parameters of a Strapdown Inertial Navigation System with the Use of a Celestial Navigation System

Ship SINS/CNS Integrated Navigation Aided by LSTM Attitude Forecast

Under the strong interference of sky background noise, the reliability of celestial navigation system (CNS) measurement will drop sharply, which leads to performance deterioration for ships’ strapdown inertial navigation system (SINS)/CNS integrated navigation. To solve this problem, a long short-term memory (LSTM) model is trained to forecast a ship’s attitude to detect the attitude provided by the CNS, and the LSTM forecasted attitude can also be used as a backup in case of CNS failure. First, the SINS/CNS integrated model is derived based on an attitude solution of the CNS, which provides more favorable feature data for LSTM learning. Then, the key techniques of LSTM modeling such as dataset construction, LSTM coding method, hyperparameter optimization and training strategy are described in detail. Finally, an experiment is conducted to evaluate the actual performance of the investigated methods. The results show that the LSTM model can accurately forecast a ship’s attitude: the horizon reference error is less than 0.5′ and the yaw error is less than 0.6′, which can provide reliable reference attitude for the SINS when the CNS is invalid.

An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area

In this paper, we present an innovative inertial navigation system (INS)/celestial navigation system (CNS)/scene-matching navigation (SMN) adaptive integrated navigation algorithm designed to achieve prolonged and highly precise navigation in sea areas. The algorithm establishes the structure of the INS/CNS/SMN integrated navigation system. To ensure the availability of CNS in the Nanhai Sea (South China Sea) area, a cloud and fog model is meticulously constructed. Three distinct types of sea area landmarks are defined, and an automated classification model for sea area landmarks, employing support vector machines (SVM), is developed. Corresponding matching methods and strategies for these landmarks are also delineated. Concurrently, the observable probability of each landmark is computed to generate a probability cloud, representing the usability of sea area landmarks. The proposed INS/CNS/SMN adaptive integrated navigation algorithm is simulated and validated across varied altitudes and trajectories in the sea area. The results show that CNS and SMN can dynamically assist INS in achieving prolonged and highly precise navigation.

Celestial Navigation as the Emergency GNSS Backup: Enhancing Navigational Reliability

The development of Global Navigational Satellite Systems (GNSS) has revolutionised navigation on ships. GNSS, together with electronic chart display information system (ECDIS), has made navigation more accurate, efficient, and safer for sailors. However GNSS signals can be affected by various factors such as spoofing, jamming, ionospheric disturbances etc., while no backup means of position fixing in the open sea are available on board ships. Over-reliance on GNSS without the development of an alternative backup will jeopardise safe navigation of ships. However, most of the alternatives available are either complicated or expensive. By changing certain regulations and further development celestial navigation, a reliable and cost-effective backup system can be obtained.

Error suppression method of SINS/CNS integrated navigation based on diametrical stargazing

The strap-down inertial navigation system (SINS)/celestial navigation system (CNS) integrated navigation system has been extensively employed in the aerospace field owing to its unique advantages. The internal parameter errors of the star sensor constitute one of the primary factors that affect the navigation accuracy. In this paper, an error suppression method based on diametrical stargazing has been designed to suppress the navigation errors caused by the internal parameter errors of the star sensor. First, a SINS/CNS integrated navigation model based on Kalman filter has been established. Then the influence of the principal point error, image-plane tilt error, and distortions of the optical system on the measurement accuracy of the star sensor have been analyzed. Further, the process of introducing the errors into the measurement equation has been obtained. Finally, for multiple errors, two stargazing methods have been designed to suppress the impact of these errors on the navigation accuracy, whereas the constant errors of the inertial device can be modulated. The simulation results demonstrate that the proposed method can improve the positioning accuracy of the system, and verify the effectiveness of the algorithm.

All-Parameter System-Level Calibration for SINS/CNS Based on Global Observability Analysis

Before using the strap-down inertial navigation system (SINS)/ celestial navigation system (CNS) integrated navigation system, the calibration of its error parameters is a necessary process to improve the system accuracy. In this paper, an all-parameter system-level calibration method has been proposed, which utilizes Kalman filtering for simultaneously estimating the bias, scale factor, misalignments of inertial measurement unit (IMU), and installation errors of the star sensor. The observation has been constructed using the inertial navigation output information and the starlight vector measured by the star sensor. The observability of the error parameters has been analyzed theoretically, and the optimal excitation conditions for each error parameter are obtained. The effects of the angle between the starlight vector and the optical axis of the star sensor, and the attitude of the IMU, on the estimation accuracy of the installation error of the star sensor are also given. Finally, a 10-order rotation scheme has been designed and all the error parameters can be optimally excited. The simulation and experimental results demonstrate that all the error parameters have better stability and repeatability, when compared with the traditional method, and the initial alignment error of 3.4" is better than 13.1" of the traditional method. Moreover, the installation error of the star sensor can be estimated with a high precision only when the angle between the starlight vector and the optical axis of the star sensor exceeds 1°. It is shown that the proposed method can effectively improve the navigation performance of the SINS/CNS integrated navigation system.

A Novel Tightly-Coupled SINS/RCNS Integrated Navigation Method Considering Atmospheric Density Error

With the increasing demands for high-accuracy autonomous navigation, the integrated strapdown inertial navigation system and refractive celestial navigation system (SINS/RCNS) has received close attention. Aided by stellar refraction information, it can correct position and velocity errors. However, atmospheric density error is a critical element that restricts the accuracy of SINS/RCNS. To solve this problem, this paper proposes a novel tightly-coupled method. In this scheme, the atmospheric density error is estimated and compensated to accommodate for the unknown model perturbation. Additionally, a tight integration model based on star pixel coordinates is derived to handle the time-varying measurement noise. The proposed method is evaluated by representative suborbital flight vehicle navigation simulations, and the results indicate that the introduction of atmospheric density error compensation can improve the navigation accuracy without adding extra sensors and enhance the performance.

Moonlit polarized skylight-aided INS/CNS: An enhanced attitude determination method

Autonomous navigation in complex environments (e.g., global navigation satellite system denied and unknown environments) is paramountly important for unmanned systems. Inspired by nocturnal insects’ navigation mechanism, an integrated navigation method combining the information of polarization navigation system (PNS), inertial navigation system (INS), and celestial navigation system (CNS) is presented for attitude determination at night. In order to improve the robustness of attitude estimation, a two-mode attitude determination system is designed, which includes the PNS/INS/CNS (PIC) mode and PNS/INS (PI) mode. With respect to the PIC mode, the bias in moon azimuth calculated by polarized skylight is modeled as a system state and subsequently estimated according to the precise starlight information. By detecting the CNS fault, the PIC mode is switched out. Focusing on PI mode, the estimated moon azimuth bias is applicable to improve the accuracy of the polarization information. A series of simulations and field tests are conducted to evaluate the performance of the presented method. The experimental data indicates that the presented method is able to achieve accurate and stable attitude estimation, which may offer valuable insight into the navigation of complex night environments.

Challenges to overcome limitations of human centric practices in celestial navigation at sea

Celestial navigation for position-fixing ships at sea has been practised for over two centuries, and the fundamental principles are the same even today. However, the mariner's interest has gradually eroded in practising celestial navigation at sea with the proliferation of satellite-based systems with better positional accuracy. The primary cause for neglecting celestial navigation at sea is limited dependence on manual observation of heavenly bodies. Computational celestial navigation methods have achieved near real-time calculation with advanced software. However, the accuracy of the computed position depends on the feed of manually observed altitude of celestial bodies. To revive the time-tested and proven celestial navigation at sea, this paper analyses the challenges of overcoming this human-centric observation. The paper explores ways and means to improve positional accuracy by advanced technology for auto-observation of celestial bodies to wean off these observational errors. Further, the article thrives to achieve an autonomous celestial navigation system enabling it as an alternate tool for position fixing at sea by focusing on the sole integration of automated observation devices with advanced algorithms-based software processing to augment positional accuracy apart from achieving instant determination of a ship's position at sea.

Autonomous Navigation of Unmanned Aircraft Using Space Target LOS Measurements and QLEKF.

An autonomous navigation method based on the fusion of INS (inertial navigation system) measurements with the line-of-sight (LOS) observations of space targets is presented for unmanned aircrafts. INS/GNSS (global navigation satellite system) integration is the conventional approach to achieving the long-term and high-precision navigation of unmanned aircrafts. However, the performance of INS/GNSS integrated navigation may be degraded gradually in a GNSS-denied environment. INS/CNS (celestial navigation system) integrated navigation has been developed as a supplement to the GNSS. A limitation of traditional INS/CNS integrated navigation is that the CNS is not efficient in suppressing the position error of the INS. To solve the abovementioned problems, we studied a novel integrated navigation method, where the position, velocity and attitude errors of the INS were corrected using a star camera mounted on the aircraft in order to observe the space targets whose absolute positions were available. Additionally, a QLEKF (Q-learning extended Kalman filter) is designed for the performance enhancement of the integrated navigation system. The effectiveness of the presented autonomous navigation method based on the star camera and the IMU (inertial measurement unit) is demonstrated via CRLB (Cramer–Rao lower bounds) analysis and numerical simulations.

One-Step Initial Alignment Algorithm for SINS in the ECI Frame Based on the Inertial Attitude Measurement of the CNS.

To solve the problem of high-precision and fast initial alignment for the Strapdown Inertial Navigation System (SINS) under both dynamic and static conditions, the high-precision attitude measured by the celestial navigation system (CNS) is used as the reference information for the initial alignment. The alignment algorithm is derived in the Earth-centered inertial (ECI) frame. Compared with the alignment algorithm in the navigation frame, it is independent of position parameters and avoids the influence of the approximate error caused by the dynamic deflection angle. In addition, hull deformation is considered in attitude optimal estimation, which can realize initial the alignment of the SINS installed in various parts of the carrier. On this basis, the velocity measurement information is added to the alignment process, which further improves the accuracy and speed of the initial alignment under static conditions. The experimental results show that the algorithms proposed in this paper have better performance in alignment accuracy, speed, and stability. The attitude and velocity matching algorithm in the ECI frame can achieve alignment accuracy better than 0.6′. The attitude matching algorithm in the ECI frame has better robustness and can be used for both dynamic and static conditions, which can achieve alignment accuracy better than 1.3′.

INS/CNS/DNS/XNAV deep integrated navigation in a highly dynamic environment

PurposeThis study aims to address the problem of the divergence of traditional inertial navigation system (INS)/celestial navigation system (CNS)-integrated navigation for ballistic missiles. The authors introduce Doppler navigation system (DNS) and X-ray pulsar navigation (XNAV) to the traditional INS/CNS-integrated navigation system and then propose an INS/CNS/DNS/XNAV deep integrated navigation system.Design/methodology/approachDNS and XNAV can provide velocity and position information, respectively. In addition to providing velocity information directly, DNS suppresses the impact of the Doppler effect on pulsar time of arrival (TOA). A pulsar TOA with drift bias is observed during the short navigation process. To solve this problem, the pulsar TOA drift bias model is established. And the parameters of the navigation filter are optimised based on this model.FindingsThe experimental results show that the INS/CNS/DNS/XNAV deep integrated navigation can suppress the drift of the accelerometer to a certain extent to improve the precision of position and velocity determination. In addition, this integrated navigation method can reduce the required accuracy of inertial navigation, thereby reducing the cost of missile manufacturing and realising low-cost and high-precision navigation.Originality/valueThe velocity information provided by the DNS can suppress the pulsar TOA drift, thereby improving the positioning accuracy of the XNAV. This reflects the “deep” integration of these two navigation methods.

Multi-scale asynchronous fusion algorithm for multi-sensor integrated navigation system

The asynchronous fusion method is the key technology of multi-sensor integrated navigation system which is independently observed by multiple auxiliary navigation sensors with different data output rates. The multi-scale asynchronous fusion can effectively increase the accuracy of integrated navigation system, but the existing multi-scale asynchronous fusion algorithm has the characteristic of heavy computational burden and requires the data output rates of auxiliary navigation sensors to meet unrealistic special requirements. To solve this problem, a multi-scale asynchronous fusion algorithm for the multi-sensor integrated navigation system is presented based on state block vector and wavelet transform. This algorithm first converts the system’s original state equation into the relationship between the state block vector and the state point vector to obtain the state equation of the multi-scale dynamic system, and expresses the original measurement equation as the relationship with the state block vector to obtain the measurement equation of the multi-scale dynamic system. Then, the special implementation process of this algorithm is studied and expressed in details, on the basis of the matrix operator with scale and wavelet property. Finally, a multi-sensor integrated navigation system composed of strapdown inertial navigation system, celestial navigation system, global navigation satellite system, and air data system is designed and experimented validation analysis. Experiment results show that this algorithm can significantly reduce the average root mean square error and improve the accuracy of navigation parameters, compared with asynchronous preprocessing Kalman filter and asynchronous direct Kalman filter. It would have a wide prospect in multi-sensor integrated navigation systems.

Star Sensor Simulation with Application of the k-NN Method in Star Identification Problem

Simulation of the navigation systems is required to develop efficient, reliable, and accurate algorithms for orientation problems. In this paper the astronavigation system is considered, and the star sensor simulation based on algorithmic mathematical model is discussed. The algorithms include the starry sky image obtaining, data processing, and stars identification. Two modifications of the known stars identification algorithm are proposed and related to the use of k-NN method in identification and stars selection criteria for increasing computational efficiency. Simulation of the described approach for star sensor is performed in MathWorks MATLAB. The simulation results show adequate results.

A SINS/CNS integrated navigation scheme with improved mathematical horizon reference

Strap-down inertial navigation system (SINS) and celestial navigation system (CNS) integrated navigation have been widely applied in ships, aircraft and spacecraft etc. However, in practice, its performance is highly depending on the horizon reference accuracy, which is usually difficult to guarantee. In this work, we propose a SINS/CNS integrated navigation scheme based on a novel mathematical horizon reference (MHR) determination method. More specifically, inertial coordinate system is used in constructing the MHR, where the attitude and position errors could be decoupled and compensated, respectively. Then, a more accurate MHR could be established, thus resulting in better navigation results. Comparative numerical simulations for a maneuvering flight have been conducted, and it is shown the proposed approach has the best converging speed as well as the highest navigation precision compared with SINS and existing MHR-based SINS/CNS schemes, and all-state optimal estimation could be achieved, thus enabling fully autonomous and high-precision navigation.

SIMU/Triple star sensors integrated navigation method of HALE UAV based on atmospheric refraction correction

AbstractTo achieve autonomous all-day flight by high-altitude long-endurance unmanned aerial vehicle (HALE UAV), a new navigation method with deep integration of strapdown inertial measurement unit (SIMU) and triple star sensors based on atmospheric refraction correction is proposed. By analysing the atmospheric refraction model, the stellar azimuth coordinate system is introduced and the coupling relationship between attitude and position is established. Based on the geometric relationship whereby all the stellar azimuth planes intersect on the common zenith direction, the sole celestial navigation system (CNS) method by stellar refraction with triple narrow fields of view (FOVs) is studied and a loss function is built to evaluate the navigation accuracy. Finally, the new SIMU/triple star sensors deep integrated navigation method with refraction correction upgraded from the traditional inertial navigation system (INS)/CNS integrated method can be established. The results of simulations show that the proposed method can effectively restrain navigation error of a HALE UAV in 24 h steady-state cruising in the stratosphere.

Attitude sensors relative angular misalignment estimation in integrated navigation systems

When using modern navigation systems as part of an on-board system, the navigation task can be solved in several ways: using positional, velocity and angular corrections, and systems using measurements of various physical nature—radio, such as short-range and long-range navigation radio systems, GLONASS/GPS satellite global navigation systems, optical—celestial navigation systems—can operate as correctors. The best performance of INS and its sensors (gyroscopes and accelerometers) errors estimation can be obtained when all types of correction are implemented simultaneously. At the same time, it is particularly difficult to implement correction according to attitude parameters due to the fact that the measuring axes of the INS and correctors may not match on board of moving objects. Such a mismatch is commonly called relative angular misalignment. The paper considers a possible approach to the attitude sensors relative angular misalignment estimation in integrated navigation systems (NS), carried out in motion when the NS is in operating mode.

Testiranje funkcionalnosti i primjenjivosti pametnih uređaja za ručni sustav astronomske navigacije

In this paper, the functionality and applicability of smart devices for the purpose of handheld celestial navigation systems is investigated. The main instrument used to determine observer position (altitude measurements) in celestial navigation is the sextant. The use of a sextant and almanac or computer is a classical approach to determining the observer's celestial position. This approach has two significant limitations, firstly the time window for the measurements is short, and secondly, the view of the ocean horizon must be clear. With the use of smart devices, we can overcome these two obstacles and create a so-called handheld celestial navigation system. Currently, smart devices have very accurate sensors to measure various physical quantities such as acceleration, angular velocity, orientation, etc. We are particularly interested in validating the orientation sensor for measuring the altitude and azimuth of the celestial body. The altitude of the celestial body is the primary parameter in determining the celestial position using a sextant. The idea is to replace the sextant with a smart device to measure the altitude and possibly the azimuth of the celestial body. To test this idea, two types of experiments are designed. In the first, a system on a tripod to obtain the most accurate measurements possible is set. Such tests will provide detailed information about the accuracy of the smart device's sensors and its applicability in measuring altitude and azimuth. The test system will essentially resemble a theodolite device. In the second experiment, a hands-free measurement experiment that resembles a sextant to test the idea for practical use and functionality in the process of celestial positioning is set. The observed data show that the results of the measurements under controlled conditions are promising and within reasonable bounds for the accuracy of celestial positioning. Estimates of the position error by the graphical method are in the range of 10 Nm to 30 Nm. In order to obtain a fully functional stand-alone celestial positioning system, the proposed assembly needs to be improved through several unchallenging upgrades. A fully functional system can be considered as a cheap off-the-shelf handheld Celestial Navigational System (CNS).

A novel optimal data fusion algorithm and its application for the integrated navigation system of missile

For Inertial Navigation System (INS)/Celestial Navigation System (CNS)/Global Navigation Satellite System (GNSS) integrated navigation system of the missile, the performance of data fusion algorithms based on the Cubature Kalman Filter (CKF) is seriously degraded when there are non-Gaussian noise and process-modeling errors in the system model. Therefore, a novel method is proposed, which is called Optimal Data Fusion algorithm based on the Adaptive Fading maximum Correntropy generalized high-degree CKF (AFCCKF-ODF). First, the Adaptive Fading maximum Correntropy generalized high-degree CKF (AFCCKF) is proposed and used as the local filter for the INS/GNSS and INS/CNS subsystems to improve the robustness of local state estimation. Then, the local state estimation is fused based on the minimum variance principle and high-degree cubature criterion to get the globally optimal state. Finally, the experimental results verify that the proposed algorithm can significantly improve the robustness of the missile-borne INS/CNS/GNSS integrated navigation system to non-Gaussian noise and process modeling error and obtain the global optimal navigation information.