An Orientation Navigation Approach Based on INS and Odometer Integration for Underground Unmanned Excavating Machine

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) are very practical method to get high precision navigation. GPS/INS data Fusion in navigation has been a research topic for decades. Usually the update time of GPS an INS is different. The update time of GPS is 20HZ, and the time of INS is 100HZ or higher. Since GPS and INS are mainly methods in navigation system. Techniques of fusing GPS and INS data are in high demand in navigation industry. Usually the Karman filter is used to fuse the data from GPS and INS. But the Kalman filter is model-dependent and a priori the model parameters need to be known. In this work, a generic and intelligent approach of GPS and INS data fuse using fuzzy interpolation before Kalman algorithm is proposed. First a simplified interpolation model is introduced in a ordinary GPS and INS data fusion. Second a fuzzy rule-based system is established to fuzzy interpolation with objective fuzzy method. Then the parameters of objective fuzzy system are identified, the best n-rule is determinate and the optimal rule base system is found. Finally this approach is applied on an example for a car GPS and INS data fusion, and the results are calculated with computer system to demonstrate the advantages of this approach.

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.

In this paper, we integrate the high-precision inertial navigation system (INS) with the GPS precise point positioning (PPP) by using the tightly-coupled architecture, which aims to obtain the preferable attitude and positioning information in the remote marine applications. The structure of INS/PPP tightly-coupled integrated navigation is presented firstly in the paper. Then, the observation model in the local geographic frame (ENU frame) is derived, where the Extend Kalman filter (EKF) is used for the information fusion. Finally, a real shipborne marine test is conducted to test the performance of INS/PPP tightly coupled integration. The experimental results show that the positioning accuracy of the integrated navigation system can be better than 10cm in the horizontal axis and 20cm for the vertical channel, and the attitude estimation error can be smaller than 1 arcsec. Besides this, the performance the tightly-coupled integration can be maintained during 100s simulated signal outages, while that performance of loosely-coupled integration will degrade significantly in such a case.

Suppression of the G-sensitive drift of laser gyro in dual-axis rotational inertial navigation system

In present days, real time navigation depends on Kalman filter to fuse data from Global Positioning System (GPS) and Inertial Navigation System (INS). But there exist drawbacks like long design time, requirement of prior knowledge in fusing data from GPS and INS using Kalman Filter. In order to overcome the drawbacks of Kalman filter, GPS and INS integration are done using Artificial Neural Networks (ANN) like FFNNs. But still there exists certain drawbacks like less accuracy in FFNNs for GPS and INS data integration. Therefore, this paper introduces Higher Order Neural Networks (HONNs) approach for INS and GPS data integration to overcome the drawback. A higher order feed forward neural network architecture with optimum number of nodes is used. In the given architectures, the replacement of summation at each node by multiplication results in more powerful mapping, because of its capability of processing higher-order information from training data. Performance comparison of HONNs with FFNNs shows that the proposed architecture provides satisfactory results in terms of error rate and number of epochs. The HONNs like Multiplicative Neural Network (MNN) module and Sigma-Pi-ANN module and Traditional Feed Forward Neural Networks(FFNNs) like Radial Basis Function Neural Network (RBF) module and Back propagation Neural Network (BPN) module are trained to predict the INS position error and provide accurate position of the moving aircrafts. Sigma-Pi ANN is found to be the best in terms of accuracy, number of epochs and execution time among the two HONNs.

Since 2020, the BeiDou-3 global navigation satellite system has provided real-time precision single-point positioning enhancement services (PPP-B2b). The real-time correction provideded by this service can make real-time precision single-point positioning achieve decimeter-level. However, a dense urban environment may cause frequent interruption of GNSS signals, and PPP-B2b alone cannot achieve continuous precision positioning. This study documents a study that attempts to combine PPP-B2b with an inertial navigation system (INS). A real-time tightly coupled integration model of PPP-B2b/INS based on PPP-B2b services was constructed, and a performance analysis of the tightly coupled integration positioning of PPP-B2b/INS in different urban environments was performed. The experiment shows that in an open-sky environment, the decimeter-level positioning accuracy (North: 31.7; East: 21.3; Up: 39.6 cm and North: 34.1; East: 27.6; Up: 41.0 cm) can only be achieved based on PPP-B2b (BDS only and GPS only), PPP-B2b/INS tightly coupled integration positioning accuracy (North: 17.2; East: 10.3; Up: 19.4 cm), and the convergence time is faster; In a dense urban environment, GNSS signals are frequently interrupted, and the positioning accuracy (North: 141.3; East: 95.2; Up: 156.6 cm and North: 147.5; East: 96.4; Up: 159.3 cm) increases significantly only based on PPP-B2b (BDS only and GPS only), and positioning interruption occurs frequently. However, PPP-B2b/INS tightly coupled integration could still achieve continuous decimetre-level positioning (North: 30.0; East: 28.4; Up: 46.3 cm).

How to achieve continuous, reliable and accurate positioning performance using low-cost sensors is one of the main challenges for aviation navigation system. Global Positioning System (GPS) can provide the primary means of navigation in a number of aviation navigation applications (e.g., manned and unmanned aircraft vehicle, airport ground vehicle). However, GPS signal deteriorations typically occur due to aircraft itself during maneuvering, ionospheric scintillation, Doppler shift, multipath and so on. Thus, there is a need to research GPS augmentation strategies which can be used in the Communication, Navigation, Surveillance/Air Traffic Management. GPS integration with Inertial Navigation system (INS) is one of the key strategies. But once GPS signal outages, the integrated navigation system works in pure INS, and positioning accuracy deteriorates with time. When using low cost GPS/INS integration, a primary problem is the rapid performance deteriorate during GPS outages. To provide continuous, accurate and reliable positioning information in aviation, discrete grey prediction model (DGPM) aided fusion methodology is proposed. The DGPM provides pseudo-GPS position information for INS during GPS outages. The mathematical model of integrated navigation system is established, including INS error equations, Kalman filter and DGPM. The model works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outages. To verify the feasibility and effectiveness of the proposed methodology, real road test is performed. The comparison results show that accuracy of longitude and latitude are improved by more than 80% and 70%, respectively. The DGPM can effectively provide position corrections for standalone INS during GPS outages.

With the continuous development of positioning technology in today’s world, the accuracy requirements for navigation and positioning are also getting higher and higher. Global Positioning and Navigation System (GPS) can provide high-precision long-term navigation and positioning information. However, it has a strong dependence on the external environment, which means that it is easily disturbed by environmental changes and affects the accuracy of navigation and positioning and even leads to positioning failure. The inertial navigation system (INS) is an autonomous navigation system. It uses sensors to measure the specific force and angular velocity of the carrier for positioning and navigation, which means that it is less affected by the environment. However, the inertial navigation device will produce a certain initial error due to the restriction of the manufacturing level, and the error will increase with time, so the inertial navigation method is not suitable for long-term navigation. Therefore, it is of great practical significance to realize satellite/inertial navigation integrated navigation by combining the respective advantages of satellite navigation and inertial navigation methods and avoiding their respective disadvantages. This paper is aimed at studying the satellite/inertial navigation integrated navigation method based on the improved Kalman filter algorithm. The satellite inertial navigation integrated navigation experiment is carried out based on the improved Kalman filter algorithm. In the experiment, the noise reduction experiment of the designed satellite inertial navigation system was carried out by using the filtering noise reduction function of the improved Kalman filter algorithm, and the conclusion was drawn after the experiment. The navigation accuracy of the satellite inertial navigation system is improved by a total of 2 m after the improved Kalman filter algorithm is used to filter the noise reduction.

Tightly coupled integrated navigation system via factor graph for UAV indoor localization

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.

Due to melting ice at the North Pole, navigation at high latitudes is increasing regularly. Therefore, navigation and survey at these high latitudes becomes more and more a key issue, especially for underwater operations where GNSS signals are not available. Navigation at high latitudes is a challenge for inertial navigation systems. Alignment of these systems with respect to North becomes more difficult when closer to the pole because the physical effect that is used becomes smaller and smaller when going to high latitudes. In order to evaluate heading accuracy and navigation performance of its inertial systems under these extreme conditions, iXBlue had the opportunity to test several of its navigation systems on a vessel up to 81 degrees of latitude during summer 2014. Thanks to the collaboration of the Norwegian Polar Institute, iXBlue has tested QUADRANS gyrocompass and PHINS navigation systems on the research Vessel Lance. The paper emphasizes on the potential difficulties encountered by inertial navigation systems at high latitudes. In a second part the paper presents and analyzes performance of all navigation systems and gyrocompasses under test in terms of heading accuracy and long term stability and navigation accuracy without GNSS aiding. The third part explains why we obtained extremely good results during this test campaign.

In this paper, an adaptive and robust Kalman filter algorithm based on the maximum correntropy criterion (MCC) is proposed to solve the problem of integrated navigation accuracy reduction, which is caused by the non-Gaussian noise and time-varying noise of GPS measurement in complex environment. Firstly, the Grubbs criterion was used to remove outliers, which are contained in the GPS measurement. Then, a fixed-length sliding window was used to estimate the decay factor adaptively. Based on the fixed-length sliding window method, the time-varying noises, which are considered in integrated navigation system, are addressed. Moreover, a MCC method is used to suppress the non-Gaussian noises, which are generated with external corruption. Finally, the method, which is proposed in this paper, is verified by the designed simulation and field tests. The results show that the influence of the non-Gaussian noise and time-varying noise of the GPS measurement is detected and isolated by the proposed algorithm, effectively. The navigation accuracy and stability are improved.

Nowadays, unmanned aerial vehicles (UAVs) play a vital role in many applications and have been successfully employed in a wide variety of applications such as road surveying, precision agriculture, landslide monitoring, cultural heritage mapping, and pipeline monitoring. All of these applications require accurate and stable navigation system. Currently, most of the commercially available UAVs rely on the integration of the Global Satellite Navigation Systems (GNSS) and Inertial Navigation Systems (INS) to estimate the position, velocity, and attitude. The small form factor of modern UAVs allowed them to operate in more challenging environments such as urban and natural canyons. In these environments, the GNSS availability cannot be guaranteed, and hence the navigation solution will deteriorate dramatically during these GNSS signal outages due to the drift exhibited by the inertial navigation solution. Using other aiding sensors is crucial to limit the accumulated errors associated with INS measurements. Onboard cameras can offer a useful clue to support the navigation solution during the GNSS signal outage periods. Varieties of monocular visual odometry based on photogrammetric and Structure from Motion (SfM) approaches have been proposed to assist the navigation estimation process. The main problem of using the monocular VO technique is the loss of scale if neither external measurement nor a priori knowledge about the surrounding environment are available. Moreover, the estimated camera pose from VO is prone to drift with time. This paper introduces a novel approach for estimating the navigation states of an Unmanned Aerial Vehicle (UAV) by integrating the visual information that obtained from a monocular camera with the Inertial Measurement Unit (IMU) observations via an Extended Kalman Filter (EKF). Most of the current monocular VO algorithms rely on a calibrated camera model and apply the conventional photogrammetric and SfM approaches. While these approaches can help towards estimating the relative rotation and translation by tracking image features and applying geometrical constraints, they cannot estimate the motion scale using only the image visual features.

Demonstration that updating an inertial navigation system (INS) with VOR/DME information (from one or two stations) by means of a maximum-likelihood filter results in substantial improvements in navigational accuracy over that obtained by the use of a single VOR/DME (current practice). When continuously updating, the use of a high-quality INS (0.01 deg/hr gyro drift) instead of a low-quality INS (1.0 deg/hr gyro drift) does not substantially improve position accuracy. In-flight alignment (or realignment) of an INS to an accuracy comparable to that of ground alignment can be accomplished by using two DMEs. Several reduced-order suboptimal filters were found to perform nearly optimally.

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.