Showing papers on "GPS/INS published in 2018"

Robust Navigational System for a Transporter Using GPS/INS Fusion

Abstract: A fusion of inertial navigation system and global positioning system (GPS) has been proposed to implement a robust navigation system for a transporter. In several cases, the GPS signals are not reliable to localize a transporter. In order to overcome this difficulty, a fault detection and isolation (FDI) algorithm has been used to detect and isolate the contaminated satellite signals. When the FDI algorithm is used alone, and the number of reliable satellite signals becomes less than four owing to GPS signal errors, the receiver cannot estimate the position precisely. In this paper, a modified FDI algorithm has been proposed and applied for the navigation of a transporter on a flat yard in order to avoid losing its positioning while it passes through a structured area. In the modified FDI algorithm, signal-to-noise ratio, measurement of quality indicator, and Doppler shift data are incorporated. In order to verify the effectiveness of the proposed algorithm, the navigation experiments have been conducted on a campus using a mobile robot to simulate the transporter on a shipbuilding yard and the results are demonstrated in this paper.

A wavelet-extreme learning machine for low-cost INS/GPS navigation system in high-speed applications

Abstract: The combined navigation system consisting of both global positioning system (GPS) and inertial navigation system (INS) results in reliable, accurate, and continuous navigation capability when compared to either a GPS or an INS stand-alone system. To improve the overall performance of low-cost micro-electro-mechanical systems (MEMS)-based INS/GPS by considering a high level of stochastic noise on low-cost MEMS-based inertial sensors, a highly complex problems with noisy real data, a high-speed vehicle, and GPS signal outage during our experiments, we suggest two approaches at different steps: (1) improving the signal-to-noise ratio of the inertial sensor measurements and attenuating high-frequency noise using the discrete wavelet transform technique before data fusion while preserving important information like the vehicle motion information and (2) enhancing the positioning accuracy and speed by an extreme learning machine (ELM) which has the characteristics of quick learning speed and impressive generalization performance. We present a single-hidden layer feedforward neural network which is employed to optimize the estimation accuracy and speed by minimizing the error, especially in the high-speed vehicle and real-time implementation applications. To validate the performance of our proposed method, the results are compared with an adaptive neuro-fuzzy inference system (ANFIS) and an extended Kalman filter (EKF) method. The achieved accuracies are discussed. The results suggest a promising and superior prospect for ELM in the field of positioning for low-cost MEMS-based inertial sensors in the absence of GPS signal, as it outperforms ANFIS and EKF by approximately 50 and 70%, respectively.

Improvement in the UAV position estimation with low-cost GPS, INS and vision-based system: Application to a quadrotor UAV

Abstract: In this paper, we develop a position estimation system for Unmanned Aerial Vehicles formed by hardware and software. It is based on low-cost devices: GPS, commercial autopilot sensors and dense optical flow algorithm implemented in an onboard microcomputer. Comparative tests were conducted using our approach and the conventional one, where only fusion of GPS and inertial sensors are used. Experiments were conducted using a quadrotor in two flying modes: hovering and trajectory tracking in outdoor environments. Results demonstrate the effectiveness of the proposed approach in comparison with the conventional approaches presented in the vast majority of commercial drones.

An Improved Strong Tracking Cubature Kalman Filter for GPS/INS Integrated Navigation Systems.

Abstract: The cubature Kalman filter (CKF) is widely used in the application of GPS/INS integrated navigation systems. However, its performance may decline in accuracy and even diverge in the presence of process uncertainties. To solve the problem, a new algorithm named improved strong tracking seventh-degree spherical simplex-radial cubature Kalman filter (IST-7thSSRCKF) is proposed in this paper. In the proposed algorithm, the effect of process uncertainty is mitigated by using the improved strong tracking Kalman filter technique, in which the hypothesis testing method is adopted to identify the process uncertainty and the prior state estimate covariance in the CKF is further modified online according to the change in vehicle dynamics. In addition, a new seventh-degree spherical simplex-radial rule is employed to further improve the estimation accuracy of the strong tracking cubature Kalman filter. In this way, the proposed comprehensive algorithm integrates the advantage of 7thSSRCKF’s high accuracy and strong tracking filter’s strong robustness against process uncertainties. The GPS/INS integrated navigation problem with significant dynamic model errors is utilized to validate the performance of proposed IST-7thSSRCKF. Results demonstrate that the improved strong tracking cubature Kalman filter can achieve higher accuracy than the existing CKF and ST-CKF, and is more robust for the GPS/INS integrated navigation system.

A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System.

Abstract: Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.

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

Abstract: 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.

A Novel Adaptively-Robust Strategy Based on the Mahalanobis Distance for GPS/INS Integrated Navigation Systems.

Abstract: As an optimal estimation method, the Kalman filter is the most frequently-used data fusion strategy in the field of dynamic navigation and positioning. Nevertheless, the abnormal model errors seriously degrade performance of the conventional Kalman filter. The adaptive Kalman filter was put forward to control the influences of model errors. However, the adaptive Kalman filter based on the predicted residuals (innovation vector) requires reliable observation information, and its performance is significantly affected by outliers in the measurements. In this paper, a novel adaptively-robust strategy based on the Mahalanobis distance is proposed to weaken the effects of abnormal model deviations and outliers in the measurements. In the proposed scheme, the judging index is defined based on the Mahalanobis distance, and the adaptively-robust filtering is performed when the observations are reliable, otherwise, the robust filtering is performed based on the robust estimation method. Various experiments with the actual data of GPS/INS integrated navigation systems are implemented to examine validity of the proposed scheme. Results show that both the influences of model deviations and outliers are weakened effectively by using the proposed adaptive robust filtering scheme. Moreover, the proposed scheme is easy to implement with a reasonable calculation burden.

Loosely coupled GPS/INS integration with snap to road for low-cost land vehicle navigation: EKF-STR for low-cost applications

Abstract: Nowadays, the availability of the vehicle position gets more and more important. The use of the Global Positioning System (GPS) receiver has solved this problem. Nevertheless, this system could suffer from availability of the minimum number of visible satellite, especially in harsh environment. Thus, complementary system, such us Inertial Navigation System (INS) comes to help the GPS in order to guarantee the availability of the position in these environments. Nevertheless, low-cost Microelectromechanical System (MEMS) based INS integrated with the GPS has shown weak performances even in case of using Kalman filtering. To deal with this problem, this paper proposes a new approach based on loosely coupled GPS/INS integration using Extended Kalman Filter (EKF) and aided by the map matching technique Snap To Road (STR). Experimental tests of EKF aided by STR tehcnique have shown better performances than EKF alone even in harsh environment.

Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System

Abstract: The extended Kalman filter (EKF) as a primary integration scheme has been applied in the Global Positioning System (GPS) and inertial navigation system (INS) integrated system. Nevertheless, the inherent drawbacks of EKF contain not only instability caused by linearization, but also massive calculation of Jacobian matrix. To cope with this problem, the adaptive interacting multiple model (AIMM) filter method is proposed to enhance navigation performance. The soft-switching characteristic, which is provided by interacting multiple model algorithm, permits process noise to be converted between upper and lower limits, and the measurement covariance is regulated by Sage adaptive filtering on-line Moreover, since the pseudo-range and Doppler observations need to be updated, an updating policy for classified measurement is considered. Finally, the performance of the GPS/INS integration method on the basis of AIMM is evaluated by a real ship, and comparison results demonstrate that AIMM could achieve a more position accuracy.

A Novel Hybrid of a Fading Filter and an Extreme Learning Machine for GPS/INS during GPS Outages.

Abstract: In this paper, a novel algorithm based on the combination of a fading filter (FF) and an extreme learning machine (ELM) is presented for Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems. In order to increase the filtering accuracy of the model, a variable fading factor fading filter based on the fading factor is proposed. It adjusts the fading factor by the ratio of the estimated covariance before and after the moment which proves to have excellent performance in our experiment. An extreme learning machine based on a Fourier orthogonal basis function is introduced that considers the deterioration of the accuracy of the navigation system during GPS outages and has a higher positioning accuracy and faster learning speed than the typical neural network learning algorithm. In the end, a simulation and real road test are performed to verify the effectiveness of this algorithm. The results show that the accuracy of the fading filter based on a variable fading factor is clearly improved, and the proposed improved ELM algorithm can provide position corrections during GPS outages more effectively than the other algorithms (ELM and the traditional radial basis function neural network).

A hybrid error modeling for MEMS IMU in integrated GPS/INS navigation system

Abstract: Continuity of accurate navigational data for intelligent transportation applications has been widely provided by utilizing low-cost navigation systems through integrating GPS with micro-electro-mechanical-system (MEMS) inertial sensors. To achieve the required accuracy, augmentation of Kalman filter (KF) with nonlinear error modeling techniques such as fast orthogonal search (FOS) was introduced to enhance the navigational solution by estimating and eliminating a great part of both linear and nonlinear errors of azimuth angle sensed by MEMS gyro. Although this augmented approach enhanced the overall navigational accuracy to some extent, it still suffers from some drawbacks that diverge the system accuracy during GPS long outage periods. These drawbacks stem from the wide-variational behavior and high nonlinearities of the errors in MEMS gyros which make it difficult to depend on the non-adaptive linear error model provided by KF to model the two types of MEMS azimuth errors. In this paper we tried to minimize the effect of uncertainties associated with the KF azimuth prediction during the absence of GPS by introducing a hybrid error model which employs support vector machine (SVM) to model the KF output and FOS, based on autoregressive (AR) concept, to model the nonlinear azimuth errors. The performance of the proposed hybrid SVM-FOS approach is evaluated for GPS/ RISS (Reduced inertial sensor system integrated system) and the results were compared with the conventional KF and augmented KF-FOS approaches.

Aerial photography flight quality assessment with GPS/INS and DEM data

Abstract: The flight altitude, ground coverage, photo overlap, and other acquisition specifications of an aerial photography flight mission directly affect the quality and accuracy of the subsequent mapping tasks. To ensure smooth post-flight data processing and fulfill the pre-defined mapping accuracy, flight quality assessments should be carried out in time. This paper presents a novel and rigorous approach for flight quality evaluation of frame cameras with GPS/INS data and DEM, using geometric calculation rather than image analysis as in the conventional methods. This new approach is based mainly on the collinearity equations, in which the accuracy of a set of flight quality indicators is derived through a rigorous error propagation model and validated with scenario data. Theoretical analysis and practical flight test of an aerial photography mission using an UltraCamXp camera showed that the calculated photo overlap is accurate enough for flight quality assessment of 5 cm ground sample distance image, using the SRTMGL3 DEM and the POSAV510 GPS/INS data. An even better overlap accuracy could be achieved for coarser-resolution aerial photography. With this new approach, the flight quality evaluation can be conducted on site right after landing, providing accurate and timely information for decision making.

Research on UAV Flight Performance Test Method Based on Dual Antenna GPS/INS Integrated System

Abstract: With higher and higher requirements for UAV position accuracy in the application areas of technical reconnaissance, inspection, fixed-point delivering and so on, the existing testing technology cannot meet the high demand of UAV flight performance testing. In order to enhance the test accuracy of unmanned aerial vehicle (UAV) flight performance, a practical method based on the combination of dual antenna global positioning system (GPS) and inertial navigation system (INS) is presented. By performing double-difference processing on the carrier phase measured by the two antennas carried by the unmanned aircraft, a double-difference observation equation is established to achieve high-precision attitude measurement. The flight performance evaluation function model of UAV is established, with which the UAV flight performance is analyzed from three aspects: hovering accuracy, attitude stability and track accuracy. The static simulation test shows that the standard deviation of the attitude angle of the measurement system is less than 0.1 degrees, and the standard deviation of the positioning is less than 1 cm, which shows great progress in the area of UAV performance testing compared with traditional methods. The method can be universally applied to the testing of various kinds of UAVs including fixed wings, multi-rotors, helicopters, etc.

A comparison of methods for online lever arm estimation in GPS/INS integration

Abstract: A comparison of neural network, state augmentation, and multiple model-based approaches to online location of inertial sensors on a vehicle is presented that exploits dual-antenna carrier-phase-differential GNSS. The best technique among these is shown to yield a significant improvement on a priori calibration with a short window of data. Estimation of Inertial Measurement Unit (IMU) parameters is a mature field, with state augmentation being a strong favorite for practical implementation, to the potential detriment of other approaches. A simple modification of the standard state augmentation technique for determining IMU location is presented that determines which model of an enumerated set best fits the measurements of this IMU. A neural network is also trained on batches of IMU and GNSS data to identify the lever arm of the IMU. A comparison of these techniques is performed and it is demonstrated on simulated data that state augmentation outperforms these other methods.

Robust Bayesian filtering for positioning using GPS & INS in multipath environments

Abstract: While traveling on roads surrounded by high-rise buildings, positioning units that use global positioning system (GPS) data often suffer large multipath errors. To improve the accuracy of GPS units, we propose a novel robust Bayesian filtering algorithm that can distinguish outlier signals affected multipath errors. The proposed method implements sequential estimation using multiple hypothesis tracking. In the proposed method, an observed distribution of the GPS satellite signal is assumed to be a mixture of a Gaussian distribution of normal values and a Cauchy distribution of abnormal values due to multipath errors. The proposed method generates two hypotheses of the normal values and abnormal values. To limit the number of hypotheses, we introduced Gaussian mixture reduction based on Kullback-Leibler divergence. Experiments with real driving data show that the proposed method is more robust than previously reported approaches based on extended Kalman filters or optimization algorithms.

Design of Outdoor Following Vehicle System Based on GPS-INS Fusion Navigation Algorithm

Abstract: T382 obtain accurate positioning of the autonomous following vehicles in a complex environment, this paper develops a following system considering both the following vehicle and the mobile terminal, which is based on the precise location calculated by fusion navigation algorithm from GPS and INS. An outdoor following vehicle platform is set up and experiments are conducted with single INS sensor, single GPS sensor and combined GPS-INS sensor. The result shows that the system based on the proposed fusion algorithm can get more accurate location than the results calculated by information from single sensor. It also indicates that the proposed algorithm can eliminate the noise and accidental errors and improves the response efficiency. By tracking the vehicles movement of the following system, the proposed following system outperforms the existing system on flat ground and gentle slope.

Applications of Inertial Navigation Systems in Medical Engineering.

Abstract: Inertial navigation systems are of the most important and practical systems in determining the velocity, position and attitude of the vehicles and different equipment. In these systems, three accelerometers and three gyroscopes are used to measure linear accelerations and angular velocities of vehicles, respectively. By using the output of these sensors and special inertial algorithms in different frames, parameters of vehicle such as position, velocity and attitude can be calculated. These systems are used in medical equipment including, but not limited to MRI devices, intelligent patient beds, surgical robots and angiography equipment. In this paper, inertial navigation systems, inertial sensors such as accelerometers, gyroscopes and inertial navigation algorithm are introduced. Afterwards, different applicable samples of inertial navigation system in medical equipment are described. According to the study carried out in this paper, it is presented and proved that applying inertial navigation in medical equipment is granted with precise and fast positioning as well as attitude determination. Moreover, as this technique of utilizing inertial navigation is applied to medical devices, a high efficiency system in terms of specifying the position and attitude will be achieved.

GPS/INS (Inertial Navigation System) integration positioning method based on wavelet neural network

Abstract: The invention discloses a GPS/INS (Inertial Navigation System) integration positioning method based on a wavelet neural network. A fuzzy strong tracking unscented Kalman (FUZZY-TSUKF) has strong robustness and excellent real-time tracking capability. When the GPS works well, the WNN uses a prior position error estimated provided by the FUZZY-STSUKF as an input and uses acurrent position error estimated as an output for training, and thus a position error of the INS should can be modeled based on the previous output. Acurrent INS measurement error can be compensated by the model while the GPS is invalid, which effectively solves a problem of INS measurement error accumulation during GPS interruption. Therefore, the proposed GPS/INS fusion algorithm is more suitable for high dynamic environment such as urban canyon.

Continuous Estimation of Motion State in GPS/INS Integration Based on NARX Neural Network

Abstract: The accurate estimation of motion state has been the key node in the control of motion systems. The traditional GPS/INS (Global Position System/Inertial Navigation System) integration may be invalid in the outage of GPS signals. The continuous estimation framework and method were proposed to estimate the location in different environments. Firstly, a continuous framework was designed combining the traditional GPS/INS integration and the intelligent neural network. The methods were switched according to the condition of sensors to realize the continuous estimation. Secondly, NARX (Nonlinear Autoregressive with Exogenous Inputs) neural network was built to model the nonlinear mapping relation between INS and GPS. The time series data were analyzed in NARX neural network to excavate the data features in the time dimension. Lastly, the experiment was conducted to verify the method proposed. And the results showed that the solution is valid in the motion state estimation with INS when GPS is in the outage.

A low-cost GPS/INS integration methodology based on DGPM during GPS outages

Abstract: 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.

Driver behavior assessment based on loosely coupled GPS/INS integration in harsh environment

Abstract: Commercial location-based services use mainly the Global Positioning System (GPS) receiver data to develop the driver assistance and monitoring systems. Nevertheless, the GPS receiver has a lot of problems, and the important one is the signal availability in harsh environment such as urban canyon, tunnel and bridge. To overcome this drawback, the Inertial Navigation System (INS) comes to aid the GPS receiver using Kalman filtering to compute precise the position, velocity and acceleration in these environments. This paper matches the two complementary areas which are: GPS/INS integration and Driver Behavior Assessment (DBA). In the literature, these two fields have been deeply investigated separately. However, an accurate analysis of the driver behavior requires precise and available data (position, velocity and acceleration) even in harsh environment. This paper presents a new method for driving behavior assessment based on the loosely coupled GPS/INS integration that allows a precise results, especially in case of GPS outages which can be modeled in the driver behavior assessment part. This assessment uses the belief theory, to fuse risk information given from the Driver, Vehicle and Environment entities, and the fuzzy theory to reduce the complexity of the fusion problem. The obtained real test results show good performance of the developed algorithms as well as the risk models. In addition, the presented results show the capability of the belief theory to model the GPS outages and the quality of signals.

Ga based data fusion approach in an intelligent integrated gps/ins system

Abstract: A new concept regarding to the GPS/INS integration, based on artificial intelligence here is presented. Most integrated inertial navigation systems (INS) and global positioning systems (GPS) have been implemented using the Kalman filtering technique with its drawbacks related to the need for predefined INS error model and observability of at least four satellites. Most recently, an INS/GPS integration method using a hybridadaptive network based fuzzy inference system (ANFIS) has been proposed in literature. During the availability of GPS signal, the ANFIS is trained to map the error between the GPS and the INS. Then it will be used to predict the error of the INS position components during GPS signal blockage. As ANFIS will be employed in real time applications, the change in the system parameters (e.g., the number of membership functions, the step size, and step increase and decrease rates) to achieve the minimum training error during each time period is automated. This paper introduces a genetic optimization algorithm that is used to update the ANFIS parameters with the INS/GPS error function used as the objective function to be minimized. The results demonstrate the advantages of the genetically optimized ANFIS for INS/GPS Integration in comparison with conventional ANFIS specially in the cases when facing satellites’ outages. Coping with this problem plays an important role in assessment of the fusion approach in land navigation.