A Precise Position and Orientation Algorithm Based on IMM-SINS/GNSS in Aerophotogrammetry System

The conventional Kalman filter assumes that the system model and the observation model is linearity. Also the noise in system and observation model is white noise. However the practical environment is very complex. When we use Kalman filter to estimate, we can not got the transcendent information. The paper considers the characteristic of aircraft, research the structure and scheme of INS/CNS/GPS integrated navigation system. The error model of integrated navigation system has been set up. The status of navigation system has been estimated with Kalman filter, and the simulation calculation has been progressed. The paper presents a method, which use neural network ensembles to deal with the Kalman filter. This method improves the generalization and stabilization ability. The simulation shows that the arithmetic has high precision.

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.

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 this study, the integrated navigation system, consisting of radio and inertial navigation system (INS) altimeters, is presented. INS and the radio altimeter have different benefits and drawbacks. The integration is achieved by using an indirect Kalman filter. Hereby, the error models of the navigators are used by the Kalman filter to estimate vertical channel parameters of the navigation system. In the open loop system, INS is the main source of information, and radio altimeter provides discrete aiding data to support the estimations. At the next step of the study, in case of abrupt faults, the performance of the integrated system is examined. The optimal Kalman filter reacts with abnormal estimates to this situation as expected. To recover such a possible malfunctioning, the robust Kalman filter algorithm is suggested.

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.

It is a reasonable question. Why would we want to go through the time and effort to integrate two different navigation systems? The simple answer is that we integrate multiple systems when no one system by itself will satisfy all the necessary requirements. More generally, we integrate multiple systems in order to construct a super-system that in some way outperforms any of the individual systems.We are going to be spending a lot of time developing the framework behind the Kalman filter and ultimately driving toward the goal of developing an architecture that will enable us to integrate multiple navigation systems into a single integrated unit. We will start off by looking at navigation system integration and consider some key issues along with goals and requirements. We will investigate subsystem characteristics and will look at some challenges to integration.Navigation engineers must be cognizant of the fact that their product is a component within a larger system. This larger system is typically a vehicle (specifically, a land, air, sea, or space vehicle). As I have told many students throughout my years of teaching: an inertial navigation system will not transport me from New York to Chicago. It will tell me where I am. It will tell me how fast I am going. It will tell me the current attitude of my vehicle and how fast it is turning. However, it will not answer the most basic navigation question that all drivers, pilots, and captains must answer in order to get where they want to go: Which way to steer?The position, velocity, and attitude information determined by the navigation system, in virtually every case, is handed off to another system. Frequently it is either a flight management system or a flight control system or some kind of display for the pilot (or driver or captain...). It is also possible that it may be pointing a sensor or a weapon or providing the position and velocity needed to process synthetic aperture radar images. In any case, the navigation system needs to provide its information in a form and with a quality that satisfies the needs of the downstream user or 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.

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.

This paper proposes an in-coordinate interval adaptive Kalman filter for INS/GPS/SMNS (Inertial Navigation System/Global Position System/Scene Matching Navigation System) integrated navigation system in which SMNS has the characteristics of randomness and in-coordinate interval when it outputs the reliable positioning information. The proposed filter sets the period of the INS parameters analysis as reference, the periods of parameters output of GPS and SMNS are adjusted to be integral multiple of the INS analysis period by interpolation or extrapolation method; The system is updated only based on INS information when the observation information don't reach, it is updated based on all navigation information when the observation information reach; In addition, this method can overcome the drawbacks of the traditional Kalman filtering method when the system measurement noise is unknown. The proposed algorithm and the method proposed by reference [8] are applied to INS/GPS/SMNS integrated navigation system. Simulation results show that the in-coordinate interval adaptive Kalman filter outperforms the traditional Kalman filter.

For the long-range voyage and light Autonomous Underwater Vehicle (AUV), the high-precision navigation and positioning is the key point in the completion of the task. Therefore, this paper designs an underwater integrated navigation system, including the strapdown inertial navigation system (SINS), Doppler velocity loci (DVL), magnetometers and depth sensor. Based on the principle of each navigation sensor and the corresponding error model, the mathematic model of each sub-filter of the integrated navigation system is firstly established. Secondly, combining the combination filter and federated filtering theory, the integrated navigation system based on federated filtering is designed. Finally, to eliminate the influence of unknown or time-varying statistical characteristics on the Kalman filter (KF), a new federated filtering algorithm based on Sage-Husa adaptive KF is proposed to estimate each local filter. And the corresponding simulation results will show the performance of the research.

This paper proposes an improved initial alignment method for a strap-down inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation system with large misalignment angles. Its methodology is based on the three-dimensional special Euclidean group and extended Kalman filter (SE2(3)/EKF) and aims to overcome the challenges of achieving fast alignment under large misalignment angles using traditional methods. To accurately characterize the state errors of attitude, velocity, and position, these elements are constructed as elements of a Lie group. The nonlinear error on the Lie group can then be well quantified. Additionally, a group vector mixed error model is developed, taking into account the zero bias errors of gyroscopes and accelerometers. Using this new error definition, a GNSS-assisted SINS dynamic initial alignment algorithm is derived, which is based on the invariance of velocity and position measurements. Simulation experiments demonstrate that the alignment method based on SE2(3)/EKF can achieve a higher accuracy in various scenarios with large misalignment angles, while the attitude error can be rapidly reduced to a lower level.

Aiming at the characteristics of large volume, low degree of integration in traditional missile-borne distributed tightly-coupled navigation system, a compact integrated navigation system is designed. Low cost IMU(inertial measurement unit) and GNSS receiver are used as navigation sensors, and the algorithm is programmed on a navigation computer designed based on DSP C6747+FPGA shallow-parallel architecture, the size of the whole system is only 13cm∗14cm∗12cm without external antenna. In terms of algorithms, an adaptive Kalman filter algorithm is adopted for information fusion. Meanwhile the status of GNSS is judged by a new proposed algorithm before Kalman filter to avoid the affect of abnormal values of pseudo-range and pseudo-range rate. In addition, satellite-selection algorithm is designed and used step by step to avoid the influence of long time cost of satellite-selection algorithm. Finally, the results of vehicle experiments show that the designed system has very high navigation precision, the dynamic level positioning error is less than 2m(1σ) and velocity error is less than 0.2m/s(1σ).

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.

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.