Ephemeris error correction in celestial navigation system considering Earth’s precession-nutation

In the integrated inertial navigation system (INS) and celestial navigation system (CNS), the attitudes and positions of probes are estimated based on the auxiliary measurement of an infrared Earth sensor. The poor accuracy of the infrared Earth sensor compared to that of the star sensor significantly limits the navigation accuracy of the integrated navigation system. This paper proposes a novel INS/CNS integrated navigation system that uses the specific force with noncumulative error to correct infrared Earth measurements. The probe azimuth angle and elevation angle relative to the Earth's inertial frame are first estimated according to the specific force provided by the INS to filter the infrared Earth sensor's measurement error. Then, the corrected infrared Earth measurement assists the CNS to obtain accurate celestial measurements. The integrated INS/CNS navigation system in the tightly coupled mode finally estimates the accurate probe attitude and position. Compared to the integrated navigation system, which directly uses the uncorrected infrared Earth measurement, the yaw, pitch, and roll angle are improved under the proposed method by 76.73%, 77.64%, and 75.39% on average, respectively; the latitude and longitude estimation errors improve under the proposed method by 70.44% and 61.92% on average, respectively. In short, the proposed method is a feasible and highly accurate approach to the INS/CNS integrated navigation system.

High dynamic aircraft is a very attractive new generation vehicles, in which provides near space aviation with large flight envelope both speed and altitude, for example the hypersonic vehicles. The complex flight environments for high dynamic vehicles require high accuracy and stability navigation scheme. Since the conventional Strapdown Inertial Navigation System (SINS) and Global Position System (GPS) federal integrated scheme based on EKF (Extended Kalman Filter) is invalidation in GPS single blackout situation because of high speed flight, a new high precision and stability integrated navigation approach is presented in this paper, in which the SINS, GPS and Celestial Navigation System (CNS) is combined as a federal information fusion configuration based on nonlinear Unscented Kalman Filter (UKF) algorithm. Firstly, the new integrated system state error is modeled. According to this error model, the SINS system is used as the navigation solution mathematic platform. The SINS combine with GPS constitute one error estimation filter subsystem based on UKF to obtain local optimal estimation, and the SINS combine with CNS constitute another error estimation subsystem. A non-reset federated configuration filter based on partial information is proposed to fuse two local optimal estimations to get global optimal error estimation, and the global optimal estimation is used to correct the SINS navigation solution. The χ 2 fault detection method is used to detect the subsystem fault, and the fault subsystem is isolation through fault interval to protect system away from the divergence. The integrated system takes advantages of SINS, GPS and CNS to an immense improvement for high accuracy and reliably high dynamic navigation application. Simulation result shows that federated fusion of using GPS and CNS to revise SINS solution is reasonable and availably with good estimation performance, which are satisfied with the demands of high dynamic flight navigation. The UKF is superior than EKF based integrated scheme, in which has smaller estimation error and quickly convergence rate.

In a traditional integrated inertial navigation system (INS) and celestial navigation system (CNS) setup, measurements from both systems are only fused at the data output phase. Navigation ...

Maneuvering spacecraft, a state-of-art platform, is paid more and more attention on for the upcoming space exploring. The integration of CNS (Celestial Navigation System) and INS (Inertial Navigation System) is the promising autonomous navigation candidate for maneuvering spacecraft. However, the star image of CNS may be heavily degraded by the large attitude variation in the spacecraft maneuvering. The effect of the attitude variation on the star image is twofold: the enlarged areas and the reduced pixel gray value of the star image. Both of them will degrade the extracting accuracy of the star image center directly. Therefore, the feasibility of CNS for the maneuvering spacecraft mainly depends on the mitigation of the effect of the attitude variation on the star image. In this paper, the star image motion is modeled when the spacecraft attitude is maneuvering firstly. The pixel gray value of the star image is also formulated in the span of exposure according to the camera exposure model. Then, the motion compensation values for the star image are deduced with the INS angular velocity in the rolling, yaw, and pitch axes respectively. The compensated star image's center is extracted accurately in the following. Finally, the compensation method is validated by simulations. The simulation results prove that the star image center extraction is as accurate as 0.1 pixel with the aiding of a tactical INS even though the spacecraft angular velocity is as large as 30º/s.

Celestial navigation system (CNS) is a completely autonomous navigation technology with strong anti-interference ability and high navigation accuracy, which has a great application prospect. Aim at defect of traditional integrated navigation system using attitude angle measurement celestial navigation system and strapdown inertial navigation system (SINS), which can only correct of strapdown inertial navigation system’s gyro drift. This paper proposes an improved integrated navigation system that add double-star positioning celestial navigation to traditional SINS/CNS integrated navigation system, meanwhile, introduces the fault detection technology based on statistical distance to solve the problem that inaccuracy of measurement information leads to the integrated navigation system’s performance degradation. By analyzing the simulation results, the improved navigation system can inhibit divergence of system and obtain high accurate and stable navigation information.

The fusion of multi-sensor data can provide more accurate and reliable navigation performance than single-sensor methods. However, the general Federated Kalman Filter (FKF) is not suitable for large changes of complex nonlinear systems parameters and is not optimized for effective information sharing coefficients to estimate navigation preferences. This study concerns research on the FKF method for a nonlinear adaptive model based on an improved Genetic Algorithm (GA) for the Strapdown Inertial Navigation System (SINS) / Celestial Navigation System (CNS) / Global Positioning System (GPS) integrated multi-sensor navigation system. An improved fitness function avoids the premature convergence problem of a general GA and decimal coding improves its performance. The improved GA is used to build the adaptive FKF model and to select the optimized information sharing coefficients of the FKF. An Unscented Kalman Filter (UKF) is used to deal with the nonlinearity of integrated navigation system. Finally, a solution and implementation of the system is proposed and verified experimentally.

In this paper, we investigate the problem of autonomous integrated navigation of strapdown inertial navigation system (SINS)/ celestial navigation system (CNS) for a ballistic missile. To handle the nonlinear missile dynamic model, three filtering methods including extended Kalman filter (EKF), extended Kalman particle filter (EKPF) and unscented particle filter (UPF) are utilized to fusion the integrated navigation system and improve the navigation accuracy. The performance of the navigation system based on the three methods is analyzed and compared. Simulation results indicate that the UPF can provide the highest navigation accuracy with the price of highest computation burden.

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.

To solve the problem of information fusion in the strapdown inertial navigation system (SINS)/celestial navigation system (CNS)/global positioning system (GPS) integrated navigation system described by the nonlinear/non-Gaussian error models, a new algorithm called the federated unscented particle filtering (FUPF) algorithm was introduced. In this algorithm, the unscented particle filter (UPF) served as the local filter, the federated filter was used to fuse outputs of all local filters, and the global filter result was obtained. Because the algorithm was not confined to the assumption of Gaussian noise, it was of great significance to integrated navigation systems described by the non-Gaussian noise. The proposed algorithm was tested in a vehicle’s maneuvering trajectory, which included six flight phases: climbing, level flight, left turning, level flight, right turning and level flight. Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter (FUKF). For instance, the mean of position-error decreases from (0.640×10−6 rad, 0.667×10−6 rad, 4.25 m) of FUKF to (0.403×10−6 rad, 0.251×10−6 rad, 1.36 m) of FUPF. In comparison of the FUKF, the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.

A Star Sensor based Fiber Optical Gyroscope Error Separation method (SSFES) was proposed, which can be applied in the Fiber Optical Gyroscope (FOG) performance evaluation and fault diagnosis for the space-borne Inertial Navigation System (INS) & Celestial Navigation System (CNS) combined navigation system. The method takes the satellite rotational angular rate and the attitude quaternion to separate the long-term drift component from the FOG output and provides the useful information for FOG error compensation and model construction. The output quaternion of the star sensor was utilized to reconstruct the satellite rotational angular rate information, which was then used to separate the error component from the FOG output. Simulations have been carried out to verify the method and have shown that the error component of FOG output can be extracted efficiently by SSFES and the algorithm is easy to be performed by the onboard computers.

The composition and overall structure of high precise and high dynamic star simulator system is presented, and a new method is put forward based on sub-pixel display technology to improve the dynamic star simulator to display the star point more precisely, which can reduce the position deviations of both geometric center and the energy center of the star point. Meanwhile, a new method for fast and accurate retrieval of the navigational star in the entire celestial sphere is presented by partitioning the SAO (Smithsonian Astrophysical Observatory) star catalogue, analyzing the four elements of the spacecraft attitude to determine where the visual axis points to, eliminating the search and match of navigational stars far from the visual axis so as to reduce the search area, hence decreasing the time that the whole celestial stars traversal required. Through simulation testing, results show that the speed of this quick search method to select the celestial navigational star has been increased by nearly 10 times.

Relative position and attitude estimation for satellite formation with coupled translational and rotational dynamics

SINS/ANS integration for augmented performance navigation solution using unscented Kalman filtering

With the development of space programs, autonomous navigation of spacecraft is required in case of emergencies. Celestial navigation is a kind of fully autonomous navigation method, which has widely been used in many space missions. Because of the measurement noise in the celestial navigation system, the geometric relations between the spacecraft and various celestial bodies have a great impact on navigation accuracy. The positions of the sun, Earth, and other planets are fixed at a certain time, but stars are distributed all over the sky. The position of the measured star is mainly determined by the pointing direction of the star sensor. To explicitly demonstrate the effect of different pointing directions, an improved observability analysis method based on the hybrid condition number of the observability matrix for a nonlinear system is presented. The impact of the pointing direction of the star sensor on the navigation accuracy is analyzed using this analysis method, with the star-Earth angle used as a measurement for a near-Earth satellite. Simulations show that the value of the hybrid condition number is in good agreement with the position estimation error. This analysis method and the corresponding analysis results are useful in the design, construction, and analysis of celestial navigation systems for spacecraft.

This paper presents a novel adaptive iterated extended Kalman filter (AIEKF) for relative position and attitude estimation, taking into account the influence of model uncertainty. Considering a nonlinear stochastic discrete‐time system with unknown disturbance, the AIEKF algorithm adopts the Gauss‐Newton iterative optimization steps to implement a maximum a posteriori (MAP) estimation, and the switch‐mode combination technique is used to achieve the adaptive capability. The mean‐square estimation error (MSE) of the state estimate is derived. It is proved that the AIEKF can yield a smaller MSE than that of the traditional extended Kalman filter (EKF) or iterated extended Kalman filter (IEKF). The performance advantage of the AIEKF is illustrated via Monte Carlo simulations on a typical relative position and attitude estimation application. Through comparisons in different scenarios, the presented algorithm is shown to improve adaptability and ensure estimation accuracy.