Star Centroid Positioning Error Correction Aided by Gyroscope Output in INS and CNS

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.

The US Army has a requirement for a highly accurate inertial/TACAN hybrid navigation system. This paper describes the navigation system architecture developed to meet this requirement, the specific hardware selected and the development status. At the beginning of the Integrated Inertial Navigation System (IINS) development program it was anticipated that significant technological advances would take place in its life cycle. In addition, mission requirements were expected to change as the system evolved. Particular attention was therefore given to the IINS system architecture, specifically the partitioning of hardware and software functions as well as the means of data transmittal between the subsystem elements. The paper describes in detail the design trade-offs made as well as hardware selections. The system is configured using several different manufacturers' equipment making the data interface and its configuration control of great importance. The above considerations lead to the conclusion that the appropriate data bus interface standard would be MIL-STD-1553. This interface standard not only facilitates configuration control during system development, but will allow rapid integration with future aircraft systems that will use it as a primary means of data transmittal. The paper describes in detail the signal interface between the subsystem element, onboard mission equipment and flight instruments. The most significant change anticipated in the course of the IINS life cycle is the substitution of Global Positioning System User Equipment for the external update function currently provided by TACAN. Another area where technology insertion may be deemed appropriate is the substitution of a strapdown inertial navigation unit (INU) employing ring laser gyros (RLG) for the currently employed conventional gimballed INU. The INU employed in the IINS is built to a form, fit and function specification, which will allow substitution of a RLG INU when such a unit is qualified and in production. The system hardware and software is structured so that these changes can be accomplished with a minimum of effort. The paper also describes the federated processing structure which allows this flexibility with particular attention to the inertial/TACAN update algorithm and data bus control functions. The paper concludes with a brief summary of the development status of the IINS, currently in full scale engineering development.

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

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.

Based on analyzing current independent navigation methods, this paper presents a new underwater navigation system, which is integrated inertial navigation system (INS) and side scan sonar (SSS). Firstly we introduce the component of the underwater navigation system and the functions of each unit in the system. Then, we study the algorithm of the matching navigation used in the system. A fast image match method by integrating wavelet transform and improved sequential similarity detection algorithm (SSDA) is given out. Based on above researches, we implement simulation experiments. Experimental results show that the system presented in this paper can acquire high navigation accuracy under the background with characteristic seabed relief. Finally, some valuable conclusions and suggestions are given.

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.

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.

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 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.

Integration of inertial navigation system (INS) and global navigation satellite system (GNSS) is usually implemented in engineering applications by way of Kalman-like filtering. This form of INS/GNSS integration is prone to attitude initialization failure, especially when the host vehicle is moving freely. This paper proposes an online constrained-optimization method to simultaneously estimate the attitude and other related parameters including GNSS antenna's lever arm and inertial sensor biases. This new technique benefits from self-initialization in which no prior attitude or sensor measurement noise information is required. Numerical results are reported to validate its effectiveness and prospect in high accurate INS/GNSS applications.

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 novel intelligent adaptive Kalman Filter for estimating the Submarine's velocity: With experimental evaluation

Navigation includes the integration of methodologies and systems for estimating time-varying position, velocity and attitude of moving objects. Navigation incorporating the integrated inertial navigation system (INS) and global positioning system (GPS) generally requires extensive evaluations of nonlinear equations involving double integration. Currently, integrated navigation systems are commonly implemented using the extended Kalman filter (EKF). The EKF assumes a linearized process, measurement models and Gaussian noise distributions. These assumptions are unrealistic for highly nonlinear systems like land vehicle navigation and may cause filter divergence. A particle filter (PF) is developed to enhance integrated INS/GPS system performance as it can easily deal with nonlinearity and non-Gaussian noises. In this paper, a hybrid extended particle filter (HEPF) is developed as an alternative to the well-known EKF to achieve better navigation data accuracy for low-cost microelectromechanical system sensors. The results show that the HEPF performs better than the EKF during GPS outages, especially when simulated outages are located in periods with high vehicle dynamics.