The modeling and simulation for GPS/INS integrated navigation system

A new integrated navigation system designed for small size Unmanned Aerial Vehicles (UAVs) is presented. The proposed system is based on a number of low-cost avionics sensors, including Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Sensors (VBS). The use of an Aircraft Dynamics Models (ADMs) to provide additional information to compensate for the shortcomings of Vision Based Navigation (VBN) and MEMS-IMU sensors in high-dynamics attitude determination tasks is also considered. Additionally, the research concentrates on the potential of carrier-phase GNSS for Attitude Determination (GAD) using interferometric techniques. The main objective is to design a compact, light and relatively inexpensive system capable of providing the required navigation performance (position and attitude data) in all phases of flight of small UAVs, with a special focus on precision approach and landing, where VBN techniques can be fully exploited in a multi-sensor data fusion architecture. An Extended Kalman Filter (EKF) is developed to integrate the information provided by the different sensors and to provide estimates of position, velocity and attitude of the UAV platform in real-time. Three different integrated navigation system architectures are implemented. The first architecture uses VBN at 20 Hz and GNSS at 1 Hz to augment the MEMS-IMU running at 100 Hz. The second mode also includes the ADM (computations performed at 100 Hz) to provide augmentation of the attitude channel. The third fusion architecture uses GNSS based attitude values. The simulations are carried out on the AEROSONDE UAV performing high-dynamics manoeuvres repre-sentative of the UAV operational flight envelope. Simulation of the VBN-IMU-GNSS (VIG) integrated navigation system shows that the system can attain position, velocity and attitude accuracies complying with Category Two (CAT II) precision approach requirements. Simulation of the VBN-IMU-GNSS-ADM (VIGA) system also shows promising results, since the achieved attitude accuracy is higher using the ADM-VBN-IMU than using VBN-IMU only. However, due to rapid divergence of the ADM virtual sensor, there is a need for frequent re-initialisation of the ADM data module, which is strongly dependent on the UAV flight dynamics and the specific manoeuvring transitions performed. In the simulation of the third integrated navigation system, the VIG system is augmented by employing the GAD, forming the VIG-GAD (VIGGA) system architecture. The performances achieved with the VIG, VIGA and VIGGA integrated Navigation and Guidance System (NGS) are presented and are in line with the International Civil Aviation Organization (ICAO) precision approach requirements.

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.

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.

A multi-sensor integrated solution that combine complementary features of the Global Positioning System (GPS), inertial navigation system (INS), and magnetometer is presented due to GPS-aided inertial navigation system (INS) provides poor observability of heading angle. In addition, Based on the principle of federated Kalman filtering and Adaptive Extended Kalman Filter, the algorithm is presented also for accuracy of positioning and attitude, rapidity, and error tolerance of the navigation system. The algorithm is implemented in the integrated navigation system. Experimental results show that the observability issue is solved and improvement in accuracy.

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.

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.

The constraints of weight, volume and power for Small unmanned air vehicle (UAV) restrict the application of sensors with heavy and good performance and powerful processors. This paper presents a real-time solution of autonomous flight navigation and its results for small UAV by applying small, cheap, low precision and low-power integrated navigation system, which includes Strap-down Inertial Navigation System (SINS) based on Micro-electro-mechanical system (MEMS) inertial sensors, Global Positioning System (GPS) receiver and magnetometer. The Square-Root Unscented Kalman filter (SR-UKF) for data fusion using in this MEMS-SINS/GPS/ magnetometer integrated navigation system provides continuous and reliable navigation results for the loops of guidance and control for the small UAV with autonomous flight. The whole integrated navigation system algorithm is implemented within low-power embedded microprocessors. The real-time flight test results show that the MEMS-SINS/GPS/magnetometer integrated navigation system is effective and accurate.

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 extended Kalman filter (EKF) has been used widely in Global Position System (GPS) and Strapdown Inertial Navigation Systems (SINS) integrated navigation systems,which simply linearizes all nonlinear models.However, careful treatment of the nonlinearity of the system models is particularly critical when the integrated systems use low cost micro-electro-mechanical (MEMS) inertial sensors. Hence application of the EKF in MEMS inertial sensor-based integrated navigation systems will likely lead to inaccurate results. To overcome the shortcomings of the EKF, the unscented Kalman filter (UKF) has been proposed.The UKF does not require the linearization of the system models. Alternatively it uses a set of deterministically selected "sigma-points",which completely capture the true mean and covariance of the original random vector. Then these sigma-points are propagated through the nonlinear models.This captures the mean and covariance to second order accuracy for arbitrary nonlinear functions. In this paper the EKF and UKF are applied in GPS/SINS integrated systems respectively. Simulation result demonstrates that the performance of the UKF is better than the EKF in MEMS inertial sensor-based SINS/GPS integrated systems.

An Integrated Navigation System (INS) is expected to serve as a tool helping in reduction of maritime casualties and ensuring the safety of navigation. Such an INS should provide a maneuvering support system and integrate the information delivered to human operators aiming at the improvement of the navigation safety. The authors have developed a supporting system in INS for collision avoidance aiming at reduction of the operators’ work load and supporting the decision-making for navigators. 1)2) The prototypes of INS were set in the shiphandling simulators and simulation studies were carried out in order to improve and evaluate the system. Many kinds of supporting methods have been tested. Finally, several methods have been proposed for assisting the decision-making during the collision avoidance. These supporting methods use data provided by ship position detection and guidance systems, which are basic technologies of INS. The authors discuss the effectiveness of their method. The authors have also developed the collision avoidance expert system that solves the collision avoidance problem and advise a desirable collision avoidance action to the operator. Some experiments on the intelligent support of decision-making in INS were conducted at the ship handling simulator equipped with INS and collision avoidance expert system.

There are the scale factor error of LDV (laser Doppler velocimeter) and the misalignment between the SINS (Strapdown inertial navigation system) and the vehicle in a SINS/LDV integrated navigation system. In this paper, the effects of these errors on the attitude, velocity and position of dead reckoning are derived, and a new online calibration method aiming to calibrate the scale factor of LDV and the misalignment between the SINS and the vehicle for the integrated system is put forward. This method, which is utilize the velocity and position of the Global Position System (GPS) as references, use the velocity error and position error of dead reckoning to estimate these errors. Through simulation and experiment, the validity and feasibility of the method are verified. The results show that the scale factor and the misalignment can be calibrated with satisfying accuracy, and the related research can provide technical support for high precision navigation of SINS/LDV integrated navigation systems.

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.

The high precision positioning and navigation of underwater vehicles is one of the key technologies for deep-sea exploration and ocean exploration, the underwater localization systems can be categorized into two main types: Inertial Navigation System (INS) and acoustic positioning system. INS is widely used in underwater navigation on account of its autonomy, however, without the information aiding from additional sensors, the errors of inertial measurement units accumulate with time passes. The acoustic positioning system can be divided into three types: Long Base Line (LBL), Short Base Line (SBL) and Ultra Short Base Line (USBL). They are defined according to the distance between transponders. The short baseline (SBL) positioning system includes more than 3 transponders to form an acoustic array, and the formation of the array is normally triangle or quadrilateral. The distance between the transponder is accurately measured and generally more than 10m. The acoustic array coordinate system is built based on the transponder and its relationship with the carrier coordinate system of the vehicle is determined by conventional method, the main disadvantage of the SBL positioning system is that the baseline length is relatively large in order to achieve high accuracy in deep-water measurement. However, as the lateral length of the vehicles is small, it is difficult to make sure the transponders be placed way from each other, meanwhile it is difficult to calibrate multiple transponders and avoid noise. To solve this problem, this paper present an inertial navigation system and single range navigation system (SRNS) based on one acoustic transponder, the navigation system consist of an acoustic transponder, a pressure transponder and inertial sensors mounted on the vehicle and an answering device transponder which is laoid out at tens of meters deep straight under the buoy. This paper studies the measurement equation's nonlinearity effect of the INS/SRNS, establishes state equation and measurement equation of the INS/SRNS integrated system. To deal with the nonlinear noise problems of transponders, meanwhile, in view that fault detection based on traditional state chi-square hypothesis testing cannot determine the specific cause of the fault, and could only determine the validity of measure information, a new fault detection algorithm based on dual-state method is adopted. The application of this method on underwater integrated navigation system demonstrates that the algorithm can rapidly and accurately detecting and identify the faults in the system. Experiments on QIANDAO Lake show that the proposed method restrains the divergence trend error of the integrated system's navigation, which verify the effectiveness of the single range integrated navigation.

SINS (Strap-down Inertial Navigation System) and GPS (Global positioning system) integrated navigation system has been extensively applied in many areas. In-flight alignment (IFA) is one of the key technologies. In this paper, an iterative unscented Kalman filter (IUKF) for SINS/GPS integrated navigation system is proposed to achieve IFA approach. Firstly, the nonlinear error equation of SINS is established, and the difference between the position and velocity of the two navigation systems is taken as the observation variables. Then, an IUKF is proposed to minimize the truncation error caused by Taylor expansion and reduce the influence of initial state value on the system state. Finally, simulation results can prove that the IUKF improves the navigation precision and robustness of system compared with the counterpart of UKF.KeywordsIntegrated navigation systemIterative filterIn-flight alignment