Global Positioning System Outages Research Articles

A Novel Hybrid Fusion Algorithm for Low-Cost GPS/INS Integrated Navigation System During GPS Outages

It is the main challenge for Global Positioning System (GPS)/Inertial Navigation System (INS) to achieve reliable and low-cost positioning solutions during GPS outages. A new GPS/INS hybrid method is proposed to bridge GPS outages. Firstly, a data pre-processing algorithm based on empirical mode decomposition (EMD) for wavelet de-noising is developed to reduce the uncertain noise of IMU raw measurements and provide accurate information for subsequent GPS/INS data fusion and training samples. Then, the interactive multi-model extended Kalman filter(IMM-EKF) algorithm is proposed to improve the robustness of Kalman filter output and the accuracy of model training target output. Finally, a new intelligent structure of GPS/INS based on Extreme Learning Machine (ELM) is proposed. When the GPS is available, the IMM-EKF is used to fuse the GPS and de-noised INS data, and the de-noised INS data and the outputs of IMM-EKF are used to train the ELM. During GPS outages, the ELM is used to predict and correct the INS position error. In order to evaluate the effectiveness of the proposed method, 3 tests were performed in the actual field test. The comparison results show that the proposed fusion method can significantly improve the accuracy and reliability of positioning during GPS outages.

Research on a mixed prediction method to vehicle integrated navigation systems

Aiming to improve the positioning accuracy of vehicle integrated navigation system (strapdown inertial navigation system/Global Positioning System) when Global Positioning System signal is blocked, a mixed prediction method combined with radial basis function neural network, time series analysis, and unscented Kalman filter algorithms is proposed. The method is composed by dual modes of radial basis function neural network training and prediction. When Global Positioning System works properly, radial basis function neural network and time series analysis are trained by the error between Global Positioning System and strapdown inertial navigation system. Furthermore, the predicted values of both radial basis function neural network and time series analysis are applied to unscented Kalman filter measurement updates during Global Positioning System outages. The performance of this method is verified by computer simulation. The simulation results indicated that the proposed method can provide higher positioning precision than unscented Kalman filter, especially when Global Positioning System signal temporary outages occur.

TRAIN LOCALIZATION USING AN ADAPTIVE MULTISENSOR DATA FUSION TECHNIQUE

This work deals with the development of an adaptive multisensor data fusion technique for the accurate estimation of the trains position and velocity. The proposed technique will work with the Train Collision Avoidance System (TCAS) used in Indian railways during Global Positioning System (GPS) outages. The determination of accurate position of trains is a challenging task for the TCAS during GPS outages. The accuracy of the proposed Volterra Recursive Least Square (VRLS) based adaptive multisensor data fusion technique is evaluated by generating two kinematic profiles for a passenger train running between Silchar–Lumding broad gauge route in Indian railways. The effect of accelerometer bias is also considered during the analysis. It is observed that the developed technique can provide a better estimate of the position and velocity for the TCAS especially during GPS outages and without using any additional railway infrastructure. The simulation results indicate that the proposed technique is superior to the earlier works in terms of achieving better positional accuracy in presence of accelerometer bias.

A Robust EMD-Based RVFL Network Fusion Algorithm for Low-Cost GPS/INS Integrated System

Global positioning system (GPS) and inertial navigation system (INS) are commonly combined to overcome disadvantages of each and constitute an integrated system that realizes long-term precision. However, the performance of the integrated system deteriorates on which GPS is unavailable. Especially when low-cost inertial sensors based on the microelectromechanical system (MEMS) are used, performance of the integrated system degrades severely over time. In this study, in order to minimize the adverse impact of high-level stochastic noise from low-cost MEMS sensors, denoising technology based on empirical mode decomposition (EMD) is employed to improve signal quality before navigation solution by which significant improvement of removing noise is achieved. Moreover, a random vector functional link (RVFL) network-based fusion algorithm is presented to estimate and compensate position error during GPS outage such that error accumulation is suppressed quickly when INS is working standalone. Performance of the proposed approach is evaluated by experimental results. It is indicated from comparison that the proposed algorithm takes advantages such as better accuracy and lower complexity and is more robust than the commonly reported methods and is more appropriate for real-time and low-cost application.

A Nonlinear Observer for Attitude Estimation of Vehicle-Mounted Satcom-on-the-Move

In this paper, a low-cost attitude estimation system (AES) is designed to estimate the attitude information of the vehicle utilizing MEMS inertial measurement unit (MIMU) and single baseline global positioning system (GPS). The proposed AES adopts a nonlinear attitude observer to fuse different sensors and estimate the vehicle's attitude. With the help of single baseline GPS, a maneuver accelerations compensation algorithm is used to improve the accuracy of the attitude angles and gyro biases. Modified quaternions are also used for the attitude observer to separate the yaw angle from the gravity angles easily when the single baseline GPS information is absent. The proposed AES judges the maneuver state of the vehicle depending on a switch criteria and adjusts the gain of the observer during GPS outages. The rules can efficiently detect the maneuver state of the vehicle and make full use of the inertial sensor outputs than classical rules. The experimental tests show that the proposed AES has a good attitude estimation accuracy, which meets the requirements of the Satcom-on-the-Move (SOTM) systems.

Dual-optimization for a MEMS-INS/GPS system during GPS outages based on the cubature Kalman filter and neural networks

Abstract To improve the performance of a microelectromechanical-system (MEMS)-based inertial navigation system (INS)/Global Positioning System (GPS) integrated navigation system, a dual optimization scheme comprising a cubature Kalman filter (CKF)-multiple layer perceptron (MLP) and radial basis function (RBF)-CKF is proposed for the compensation of position and velocity errors during GPS outages. The proposed method has advantages: (i) The generalization ability of the CKF-MLP is much better than that of other methods, such as the extended Kalman filter (EKF)-MLP and unscented Kalman filter (UKF)-MLP, and the proposed CKF-MLP provides highly accurate position information even during long GPS outages; (ii) The accuracy of the error estimation of the RBF-CKF is higher than that of other neural-network-assisted CKF methods, such as the Adaboost-CKF and random forest (RF)-CKF, and the proposed CKF-MLP can estimate the weights of the MLP adaptively and achieve an appropriate internal structure when the GPS signal is available; (iii) The RBF is added to the CKF to establish the relationship between filter parameters and estimation error in the proposed RBF-CKF; (iv) During a GPS outage, position errors and velocity errors are predicted and compensated for in the proposed dual optimization scheme. Field test data are collected to evaluate the proposed solution. Experimental results show that (i) the root-mean-square error of the velocity for an eastern position is reduced by 77% to 0.34 m/s using the proposed CKF-MLP strategy; (ii) the root-mean-square error of a northern position determined by the proposed RBF-CKF remains at 23.11 m even during long outages of 500 s, and the RBF-CKF effectively suppresses the divergence seen for other methods; and (iii) the dual optimization process using different estimators provides better error compensation results than a single optimization method, which demonstrates that the proposed solution leads to the better performance of a MEMS-based INS/GPS navigation system.

Filter Kalman for solving the problem of coordinates unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) are increasingly used in military and scientific research. Some miniaturized UAVs rely entirely on the global positioning system (GPS) for navigation. GPS is vulnerable to accidental or deliberate interference that can cause it to fail. It is not unusual, even in a benign environment, for a GPS outage to occur for periods of seconds to minutes. For UAVs relying solely on GPS for navigation such an event can be catastrophic. This article proposes an extended Kalman filter approach to estimate the location of a UAV when its GPS connection is lost, using inter-UAV distance measurements Increasing the accuracy of coordinate’s determination is one of the most crucial tasks of the modern UAV navigation. This task can be solved by using different variants of integration of navigation systems. One of the modern variants of integration is the combination of GPS/GLONASS-navigation with the extended Kalman filter, which estimates the accuracy recursively with the help of incomplete and noisy measurements. Currently different variations of extended Kalman filter exist and are under development, which include various number of variable states [1]. This article will show the utilization efficiency of extended Kalman filter in modern developments.

A novel GPS/DVL/MEMS-INS smartphone sensors integrated method to enhance autonomous navigation, guidance and control system of AUSVs based on ADSF Combined Filter

A reliable navigation, guidance and control system of Autonomous Unmanned Surface Vehicle (AUSV) is the major challenge for most researchers, especially when Global Positioning System (GPS) signal outages or its accuracy decreases in some places on the Earth’s surface. Our paper introduces a novel navigation, guidance and control system of AUSV as follows; the proposed navigation system of AUSV is based on integrated Micro Electric Mechanical System –Inertial Navigation System (MEMS-INS) smartphone sensors with GPS and Doppler Velocity Log (DVL) to correct AUSV navigation system errors. The guidance and control system of AUSV is based on Ultrasonic sensors, Raspberry Pi microcontroller, and digital compass sensor. The ultrasonic sensor is used to detect and avoid obstacles on the path of AUSV. The Raspberry Pi microcontroller and digital compass are used to guide AUSV in the required path.Since the GPS and DVL signals are used as the reference sources to correct errors of MEMS-INS system, the accuracy of GPS and DVL decreases due to a bad weather and due to associated noise of DVL measurement, which effects on a whole AUSV navigation system. To resolve this problem, the Adaptive Data Sharing Factor (ADSF) using Combined Filter (CF) is used as integrated method. According to proposed ADSF CF integrated method, the reference source (GPS or DVL) which has the beast accurate system will be used to correct the navigation errors. At same time the least accuracy system will detect and avoid.The efficiency of navigation, guidance and control system of AUSV based on proposed GPS/DVL/MEMS-INS ADFS CF integrated method is tested on surface reference trajectory with four obstacles located on the path of AUSV. During GPS outages, the AUSV was able to reach the target location to implement several tasks such as monitoring, exploring, tracking and returned back to base station depending on the stored path in its memory with high efficiency. Based on estimated results, the proposed navigation system could provide continuous navigation solution and reduce the navigation error to about 85.43% compared to GPS/DVL/MEMS CKF method and to about 68.14% compared to GPS/DVL/MEMS Constant DSF Combined Filter.

Improving the RISS/GNSS Land-Vehicles Integrated Navigation System Using Magnetic Azimuth Updates

Navigation of land or self-driving vehicles is essential for safe and accurate travel. The global navigation satellite systems (GNSSs), such as global positioning system (GPS) are the primary sources of navigation information for such purpose. However, high-rise buildings in urban canyons block the GPS satellites signals. Alternatively, inertial navigation system (INS) is typically working as a backup. A reduced inertial sensor system (RISS) is used instead of the full INS to achieve the same purpose in land vehicles navigation with fewer sensors and computations. Unfortunately, the RISS solution drifts over time, but this can be mitigated when integrated with the GPS. However, the integration solution drifts in the case of GPS signal loss (outages). Therefore, the position errors grow especially during extended periods of GPS outages. Azimuth/heading angle is critical to keep the vehicle on the route. In this paper, an azimuth update estimated from a calibrated magnetometer is introduced to improve the accuracy of the overall system. A new approach is proposed for pre-processing the magnetometer data utilizing a discrete-cosine-transform (DCT)-based pre-filtering stage. The obtained azimuth is utilized in updating the RISS system during the whole trajectory and mainly during GPS outage periods. The proposed approach significantly decreases both the azimuth error and the position error growth rate when driving in urban canyons where the GPS signals are blocked. Finally, the proposed system was tested on a real road trajectory data for a metropolitan area. The results demonstrate that the accuracy of the whole system improved, especially during the GPS outage periods.

A Fusion Methodology to Bridge GPS Outages for INS/GPS Integrated Navigation System

The performance of an inertial navigation system (INS) and global positioning system (GPS) integrated navigation system is reduced during GPS outages. To bridge GPS outages, a fusion methodology to provide pseudo GPS position information is proposed. The methodology consists of two parts, empirical mode decomposition threshold filtering (EMDTF) and a long short-term memory (LSTM) neural network. The EMDTF eliminates the noise in inertial sensors and provides more accurate data for subsequent calculations. The LSTM uses the current specific forces and angular rates to predict the pseudo GPS position during GPS outages. To evaluate the effectiveness of the proposed methodology, numerical simulations and real field tests are employed. Compared with the traditional artificial neural networks, the results illustrate the proposed methodology can significantly improve the navigation accuracy during GPS outages and the model is simpler.

A Novel BPNN-Based Method to Overcome the GPS Outages for INS/GPS System

In order to improve the performance of the global positioning system (GPS) and inertial navigation system (INS) integrated system during GPS outages, a novel fusion algorithm based on back propagation neural network (BPNN) is proposed. A new model is built which relates the INS velocity, inertial measurement unit (IMU) outputs and the duration of GPS outages to the GPS position increment. Performance of the proposed method has been experimentally evaluated in a land vehicle navigation test. The test results show: (1) the proposed model can efficiently predict the increment of position and compensate the INS errors accumulation during GPS outage; (2) the advantage of new model on positioning accuracy becomes more obvious when the GPS observations are unavailable for a long time; (3) utilizing the current and past 2-step information as the input of BPNN model can effectively balance the computation burden and accuracy.

Seamless global positioning system/inertial navigation system navigation method based on square-root cubature Kalman filter and random forest regression.

In this paper, a seamless navigation dual-model based on Square-Root Cubature Kalman Filter (SRCKF) and Random Forest Regression (RFR) is developed to enhance the performance of the Global Positioning System (GPS)/Inertial Navigation System (INS) integrated navigation system. By using the proposed method, the system can ensure seamless navigation ability even during GPS signal outages. In the proposed dual-model, sub-model 1 that directly relates the specific force of INS to the measurement of filter and sub-model 2 that directly relates the cubature points and innovation of SRCKF to the error caused by filter are established. Combined with SRCKF and RFR algorithms, the dual-model system can predict and estimate the velocity and position of the vehicle seamlessly when GPS signals are blocked. Field test data are collected to evaluate the proposed solution, and the experimental results show that the model proposed has obvious improvement in navigation accuracy by comparison. The prominent advantages of the proposed seamless navigation method include the following: (i) the proposed dual-model can effectively provide corrections to standalone INS during GPS outages, which outperforms traditional widely used single model; (ii) the proposed combination of SRCKF and RFR achieves better performance in the prediction of INS errors than other combination algorithms.

A Low-Cost Dead Reckoning Navigation System for an AUV Using a Robust AHRS: Design and Experimental Analysis

In navigation and motion control of an autonomous vehicle, estimation of attitude and heading is an important issue, especially when the localization sensors such as global positioning system (GPS) are not available and the vehicle is navigated by the dead reckoning (DR) strategies. In this paper, based on a new modeling framework, an extended Kalman filter (EKF) is utilized for estimation of attitude, heading, and gyroscope sensor bias using a low-cost microelectromechanical system (MEMS) inertial sensor. The algorithm is developed for accurate estimation of attitude and heading in the presence of external disturbances including external body accelerations and magnetic disturbances. In this study, using the proposed attitude and heading reference system (AHRS) and an identified surge dynamic model of an autonomous underwater vehicle, a low-cost model-aided DR navigation system has been designed for an AUV. The proposed algorithm application is evaluated by experimental tests in different acceleration bound and existence of external magnetic disturbances for an AUV. The results indicate that the roll, pitch, and heading are estimated by mean value errors about 0.35°, 0.29°, and 1.27°, respectively. Moreover, they indicate that a relative navigation error about 8% of the traveling distance can be achieved using the developed approach in during GPS outages.

Enhancing Localization Accuracy of MEMS-INS/GPS/In-Vehicle Sensors Integration During GPS Outages

In this paper, we propose a novel localization methodology to enhance the accuracy from two aspects, i.e., adapting to the uncertain noise of microelectromechanical system-based inertial navigation system (MEMS-INS) and accurately predicting INS errors. First, an interacting multiple model (IMM)-based sequential two-stage Kalman filter is proposed to fuse the information of MEMS-INS, global positioning system (GPS), and in-vehicle sensors. Three bias filters are built with different covariance matrices to cover a wide range of noise characteristics. Then, IMM algorithm provides a soft switching among the three bias filters to adapt to the uncertain noise of MEMS-INS. Further, an elaborate predictor is developed to accurately predict INS errors during GPS outages. The elaborate predictor comprises an online trained autoregressive integrated moving average (ARIMA) model and an offline trained extreme learning machine (ELM) model. The ARIMA model is designed to predict the basic accumulation process of INS errors, while the ELM model is designed to correct the errors caused by the changes of noise characteristics. Thus, the INS errors can be properly compensated when GPS observations are not available. In all, the proposed localization methodology can achieve accurate performance when facing uncertain noises of MEMS-INS and GPS outages simultaneously. To verify the effectiveness of the proposed methodology, road test experiments with various driving scenarios were performed. The experimental results illustrate the feasibility and effectiveness of the proposed methodology.

Multi-sensor fusion methodology for enhanced land vehicle positioning

It is a main challenge for land vehicles to achieve reliable and low-cost navigation solution in various situations, especially when Global Positioning System (GPS) is not available. To address this challenge, we propose an enhanced multi-sensor fusion methodology to fuse the information from low-cost GPS, MEMS Inertial Measurement Unit (IMU), and digital compass in this paper. First, a key data preprocessing algorithm based on Empirical Mode Decomposition (EMD) interval threshold filter is developed to remove the noises in inertial sensors so as to offer more accurate information for subsequent modeling. Then, a Least-Squares Support Vector Machine (LSSVM)-based nonlinear autoregressive with exogenous input (NARX) model (LSSVM-NARX) is designed and augmented with Kalman filter (KF) to construct a novel LSSVM-NARX/KF hybrid strategy. In case of GPS outages, the recently updated LSSVM-NARX is adopted to predict and compensate for the INS position errors. Finally, the performance of proposed methodology was evaluated with real-world data collected in urban settings including typical driving maneuvers. The results indicate that the proposed methodology can achieve remarkable enhancement in positioning accuracy in GPS-denied environments.

Hybrid Algorithm Based on MDF-CKF and RF for GPS/INS System During GPS Outages (April 2018)

This paper presents a dual-model solution for global positioning system (GPS)/inertial navigation system (INS) during GPS outages, which integrates with multiple-decrease factor cubature Kalman filter (MDF-CKF) and random forest (RF) that can be used for modeling and compensating the velocity and positioning errors. The prominent advantages of the proposed solution include: 1) filter divergence is restrained and robustness is improved with the proposed MDF-CKF method and 2) the error compensation accuracy of the RF-based dual model is higher than a normal artificial neural network-based single model. The process of the proposed solution contains: 1) the proposed MDF-CKF algorithm is employed for GPS/INS information fusion when GPS signal is valid; 2) in the meantime, the velocity, acceleration, and specific force data from IMU and INS are separately used to train the RF; and 3) when GPS outage occurs, position and velocity errors are predicted by the RF-based dual model. The experimental results show that: 1) the maximum improvement of the proposed MDF-CKF in position estimation accuracy against the traditional algorithm is 83.6%; 2) the RF-based dual model can effectively suppress the divergence than the radial basis function and INS-only mode; and 3) the dual model performs better than a single model for error modeling and compensation.

Enhanced Kalman Filter using Noisy Input Gaussian Process Regression for Bridging GPS Outages in a POS

A Position and Orientation System (POS) integrating an Inertial Navigation Systems (INS) and the Global Positioning System (GPS) is a key component of remote sensing motion compensation. It can provide reliable and high-frequency high-precision motion information using a Kalman Filter (KF) during GPS availability. However, the performance of a POS significantly degrades during GPS outages. To maintain reliable POS outputs, this paper proposes a new hybrid predictor based on modelling the nonlinear time-series data-driven INS-errors using Noisy Input Gaussian Process Regression (NIGPR), which takes the input noise into account. The proposed approach is used to learn the nonlinear INS-errors model when GPS signals are available. When GPS outages occur, it starts to predict the observation measurement, and then feeds it to a KF as a virtual update to estimate all the INS errors. The proposed approach is verified in a real airplane, which combines a POS and Synthetic Aperture Radar (SAR). Experimental results show that the proposed approach significantly improves the performance of the POS, with improvements more than 90% better than a KF and 10% better than a Gaussian Process Regression (GPR/KF) combination during various GPS outages.

Seamless autonomous navigation based on the motion constraint of the mobile robot

PurposeThe purpose of this paper is to propose a seamless autonomous navigation method based on the motion constraint of the mobile robot, which is able to meet the practical need of maintaining the navigation accuracy during global positioning system (GPS) outages.Design/methodology/approachThe seamless method uses the motion constraint of the mobile robot to establish the filter model of the system, in which the virtual observation about the speed is used to overcome the shortage of the navigation accuracy during GPS outages. The corresponding motion constraint model of the mobile robot is established. The proposed seamless navigation scheme includes two parts: the micro inertial navigation system (MINS)/GPS-integrated filter model and the motion constraint filter model. When the satellite signals are good, the system works on the MINS/GPS-integrated mode. If some obstacles block the GPS signals, the motion constraint measurement equation will be effective so as to improve the navigation accuracy of the mobile robot.FindingsThree different vehicle tests of the mobile robot show that the seamless navigation method can overcome the shortage of the navigation accuracy during GPS outages, so as to improve the navigation performance in practical applications.Originality/valueA seamless navigation system based on the motion constraint of the mobile robot is proposed to overcome the shortage of the navigation accuracy during GPS outages, thus improving the adaptability of the robot navigation.

A RLS-SVM Aided Fusion Methodology for INS during GPS Outages

In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS) outages, a novel robust least squares support vector machine (LS-SVM)-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS). The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics.

GPS/INS/Odometer Data Fusion for Land Vehicle Localization in GPS Denied Environment

The main purpose of this paper is to present a fusion approach to bridge the period of Global Positioning System (GPS) outages using two proprioceptive sensors that are the Inertial Navigation System (INS) and the odometer in order to assure a continuous localization for land vehicle in urban areas where GPS signal blockage is very often. Odometer and GPS measures are exploited to correct inertial sensor errors. In fact, during GPS availability, INS is integrated with GPS to provide accurate localization solution; whereas during GPS outages, the odometer measurements are used to correct the INS error thereby improving the positioning accuracy and assuring the continuity of navigation solution. The problem of estimation of vehicle localization is realized by Kalman Filter (KF) that merges sensor measurements. The paper thus introduces results from simulation and real data.