The fault diagnosis method of integrated navigation system based on covariance intersection filtering

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 integrated navigation system ensures maritime autonomous surface ships (MASSs) to safely, efficiently, and autonomously complete various operations in different complex navigation environments. Investigating robust algorithms for integrated navigation is crucial for enhancing the fault tolerance of the system and ensuring the stable and continuous output of the ship’s motion state. However, existing research primarily focused on optimising particular filtering algorithms or examining the foundations of information allocation within a predetermined integrated navigation structure. As such, strategies for enhancing the robustness of the MASS integrated navigation system and the design of subsystems for federated filters in the event of navigation sensor failures have not been sufficiently investigated for complex maritime navigation scenarios. Consequently, this research introduces an observability sharing factor accounting for both system characteristics and state estimation performance in integrated navigation systems, employing nonlinear sampling filtering. Subsequently, a robust integrated navigation framework with distributed federal filter is developed. Within this framework, an adaptive federated filtering integrated navigation algorithm is proposed based on the observability sharing factor to allocate information in federated filtering. Finally, both the theoretical correctness and effectiveness of the algorithm were verified through simulations and real-ship experiments to assist with the development of accurate and fault-tolerant maritime navigation systems.

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.

To improve the navigation accuracy of AUV (autonomous underwater vehicle), SINS (strapdown inertial navigation system), GPS (global positioning system), DVL (Doppler velocity log) and TAN (terrain aided navigation) are adopted in the AUV integrated navigation system. The mathematic model of the AUV integrated navigation system and the observation model of the chosen navigation sensors are built according to the system simulation experiments data. An improved filter based on RBF neural network for adjusting the information sharing factors is designed and implemented in the AUV integrated navigation system. Simulation experiments are carried out according to the mathematic model. It can be concluded from the simulation experiments that the navigation accuracy is improved substantially with the multiple sensors and federated filter in case that colored noise is engaged. The novel integrated navigation system is effective in prohibiting the divergence of the filter and improving fault tolerance ability and it greatly raises the precision of the navigation accuracy for the AUV integrated navigation system.

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.

In this article, a prediction method based on a radial basis function neural network and an improved unscented Kalman filter, is proposed to improve the accuracy of position and velocity of the Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) integrated navigation system, especially in the presence of GPS signal outages. The improved unscented Kalman filter based on the adaptive theory is adopted to enhance the positioning accuracy of the GPS/SINS integrated navigation system when a GPS signal is available. A radial basis function neural network and a non-stationary time series analysis are used to predict and compensate for the positioning error of the GPS/SINS integration in the presence of GPS signal outages to improve the reliability of the navigation system. The effectiveness of the proposed method is verified by the simulation experiment and vehicle test. The simulation and test results show that the proposed method can improve the position accuracy and velocity accuracy in different GPS outage time periods.

Sensors are essential in feedback control systems, because the performance is dependent on the measurements. Fault in sensors may lead to intolerable degradation of performance and even to instability. Therefore, the high performance expected with vector control may not be achieved with fault in sensors. Several approaches related to fault tolerant motor control have already been proposed. However, most of them consider the sensors fault-free and work about faults in motors and actuators. Furthermore, the purpose of this work is not only sensor fault tolerance but also sensor fault compensation. In a standard fault tolerant approach, the fault would be detected and the sensor would be isolated. The faulted sensor may have an off-set or scaling error and could still be used if its error is compensated. In this paper, this is done with a mathematical solution based on kernel regression that can compensate the measurement error generating more accurate and reliable estimates. This technique is described and applied in motor drives. Simulated and experimental results are presented and discussed.

Abstract. High accuracy positioning is crucial for various applications that require accurate and reliable positioning data, such as autonomous vehicles (AVs), agriculture, and intelligent transportation. Global Positioning System (GPS) is widely used in integration with Inertial Measurement Unit (IMU) and Visual Odometry (VO) to implement an accurate and robust navigation system. However, the performance of the integrated system can be severely degraded in urban environments due to non-line-of-sight (NLOS) reception and multipath interference. To overcome these challenges, a novel Fault Detection and Exclusion (FDE) algorithm aided with IMU and VO measurements is proposed to identify and isolate the contaminated satellite signals. The algorithm utilizes instantaneous measurements from IMU and VO to predict the current vehicle position, enabling the estimation of pseudorange errors in GPS measurements. A FDE algorithm based on Hierarchical clustering is then developed to identify GPS signals with significant errors based on the predicted pseudorange error. An experimental field test was conducted using a land vehicle to evaluate the effectiveness of the proposed algorithm. The results show that the GPS/IMU/VO integrated navigation system with the proposed FDE algorithm has significantly improved the positioning accuracy and reliability compared to the traditional system. The proposed algorithm achieves a positioning accuracy with a 3D Root Mean Square Error (RMSE) of 11.18m in urban environment, making an improvement of 68.2% over the traditional GPS/IMU/VO integrated navigation system.

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 connection with the increasing complexity of aircraft navigation support, with growing demands placed on them, it is increasingly necessary to develop integrated navigation data processing system by solving synthesis problems based on optimal filtering methods. One of the promising ways to improve aircraft navigation support is to create an integrated navigation system (INS). In this work the triadic structure of a promising integrated navigation system based on inertial and satellite technologies is proposed. Combining nautical meters as part of the triad concept allows the flexible use of various navigation data processing algorithms and trajectory optimization ones at different stages of flight. Based on optimal filtering theory methods the integrated navigation data processing algorithm, which is an extended Kalman filter, is synthesized. A distinctive feature of the algorithm is that the variables of the state vector which are to be measured, are the errors in the determination of appropriate navigation and timing parameters. Priori information about the dynamics of errors is more reliably known than the one about aircraft motion variables. It is shown that the use of the proposed navigation system as a navigation aid for problem solving of highly accurate aircraft position indication satisfies the requirements for navigation and timing data accuracy. The imitating statistical modeling of optimal filtering algorithm of the triad integrated system is carried out. The accuracy characteristics of subsystems for different operating configurations of the constellation are given. Analysis of the results of the accuracy characteristics study of INS in various operating conditions has showed that the positioning accuracy in the navigation computer is up to date satellite navigation receivers.

This paper presents the concept and method of InSAR aided INS navigation. InSAR interferometric phase is sensitive to the attitude of the platform and the terrain height, so it can be used to improve the precision of navigation substantially. InSAR/INS integrated navigation system can be used on the flight vehicle navigation and it can satisfy complicated topography and long-distance accurate autonomous navigation. The technical route of InSAR/INS integrated navigation system is discussed in detail in this article. We can obtain the location shift of azimuth and range by matching two interference fringes, one is produced by InSAR system and another is generated by reference path and digital elevation database. And the simulation results indicate the precise and efficient of the matching algorithm. With the aid of the location shift we can inverse the attitude and position information of the platform. The location shift and the IMU information are set to the all-state combined filter to obtain the navigation output.

This paper presents a new approach for integrated landing systems. Applicable avionics platforms are military aircraft, helicopters, and unmanned aerial vehicles. In this paper we focus on the introduction of NILS (new integrated landing system) in the Saab fourth generation fighter JAS39 Gripen. NILS is based on an integrated navigation system (NINS) which is covered in the paper as well. The landing system in older generations of aircraft is usually designed by combination of different equipment. In the fourth generation aircraft the same functionality is solved in software implemented in the aircraft system computers. Typical applications of Saab NINS are accurate random route navigation (RNAV) under instrument flight rules, inputs to the auto-pilot, ground collision avoidance system (GCAS), terrain following, landing system and weapon initialization. Future applications are inputs to head-up or head-down display synthetic terrain presentation, mid-air collision avoidance system (MCAS) and passive target ranging.

Integrated navigation systems have evolved in response to the increasing requirements for more accurate navigation of marine survey vessels. Some systems have been better than others in terms of choice of devices for combination, or in mathematical handling of data from the respective sensors, but all have been inflexible in terms of interchange of sensor types or adaptation of new devices. This paper studies the basic philosophy of navigation system design with primary consideration given to system objectives and control structure. The result is a sensor-independent approach to integrated navigation system design. INTRODUCTION At this point in time, there is no universal aid to navigation which can provide continuous position fixes and velocities on a worldwide, 24-hour per day basis, and still deliver the accuracies required by all users. There are several systems which offer accuracy and continuity, but operate over a limited range; e.g., doppler satellites yield worldwide coverage but give only periodic fix updates; while VLF radio systems yield nearly continuous, worldwide coverage but lack the accuracy required for precision navigation. The Global Positioning System (GPS), promises to give simultaneous solutions to many of these problems, but commercial availability is not likely to be realized before the late 1980's. The approach taken to meet user requirements in precise navigation has been the "integrated navigation system" a combination of two or more different navigation subsystems. This paper deals with the philosophy of integrated system design, reviews the deficiencies in some past approaches to system integration, and describes a design structure which permits flexibility in the choice of sensor subsystems and provides for longer product lifetime. CATEGORIES OF NAVIGATION AIDS Since the earliest days of navigation, mariners have used tools to assist in determining their position (position fix) and in setting and keeping a course over a period of time to reach a desired new position (dead reckoning). Ocean voyages depended upon natural phenomena, with celestial observations for position reference and magnetic compass directions to maintain headings. Local navigation (piloting) has always used line-of-sight reference to coastline features or reference markers to maintain a desired course from successive triangulation bearings. Twentieth century techniques have considerably improved life for the navigator, extending operation on a 24-hour per day, all-weather basis while increasing the accuracy of measurement. Three techniques of measurement form the basis for most of the navigation aids currently in use: radio wave systems, which use broadcast wavelength as a "yardstick" for distance or velocity measurements between the navigator and known reference points; acoustic systems, which perform range or rangerate measurements based on propogation time of sound through water; and gyroscopic systems, which measure orientation and angular velocities within the Earth's gravitational field. The following paragraphs describe systems from all of these categories, giving some specific strengths and weaknesses of each system type.

Many researches on use of local ground navigation systems can be found to overcome vulnerability of GNSS. Effective use of an altimeter is proposed in GNSS/DME integrated navigation systems. A weighted DOP based on statistics of measurement error is derived for a given vehicle motion trajectory. From the derived DOP, the vertical error is estimated. By comparing the estimated vertical error with error specification of the altimeter, use of the altimeter is determined in the GPS/DME integrated navigation systems. In order to show effectiveness of the proposed method, 50 times Monte-Carlo simulations were performed for a GPS/DME integrated navigation system. The results show that the proposed method gives more accurate navigation outputs when the number of GPS satellites in view varies.

Embedded system has outstanding characteristics in real-time, portability, application specificity and resource possession of the system. The application of embedded technology in integrated navigation system can make the system smaller and more specific, therefore, this paper designs and implements BDS /GPS/SINS integrated navigation system based on embedded system. The BDS/GPS/SINS integrated navigation system can combine the advantages of BDS, GPS and SINS with higher precision and better stability. The system design covers the hardware and software, including the overall design of the hardware, the selection of the sensor module, the algorithmic analysis of the software and the system transplant. The precision and reliability of BDS/GPS/SINS integrated navigation system were tested and verified by dynamic experiment. The results show that the error range of BDS/GPS/SINS integrated navigation system in longitude, latitude and height was better than GPS/SINS navigation system.