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Showing papers on "GPS/INS published in 2020"


Journal ArticleDOI
TL;DR: The comparison results show that the proposed fusion method can significantly improve the accuracy and reliability of positioning during GPS outages.
Abstract: 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.

28 citations


Journal ArticleDOI
TL;DR: A novel approach to deal with the uncertainty of error covariance and state noise in vehicle navigation, wherein the integration of Square-root Cubature Kalman Filters and Interacting Multiple Model and IMM are also introduced.
Abstract: Information fusion of the GPS/INS integrated system is always related to characteristics of the inertial system and the sensor feature, yet prior knowledge is still difficult to obtain in real applications. To deal with the uncertainty of error covariance and state noise in vehicle navigation, this paper presents a novel approach, wherein the integration of Square-root Cubature Kalman Filters (SCKF) and Interacting Multiple Model (IMM) are also introduced. In the framework of IMM, the SCKFs with different covariance are designed to reflect various vehicle dynamics. Besides, since the IMM-SCKF can switch flexibly among the filters, the transition probability matrix is computed with maximum likelihood method to adapt to different noise characteristics. The performance of the proposed algorithm is guaranteed by theoretical analyses, and a series of vehicular experiments with different maneuvers are carried out in an urban environment. The results indicate that, in comparison with the CKF and the IMM-CKF, the accuracy of velocity and attitude are increased by the proposed strategy.

24 citations


Journal ArticleDOI
07 Jun 2020-Sensors
TL;DR: A low-cost, high-precision vehicle navigation system for deep urban multipath environments using time-differenced carrier phase (TDCP) measurements to eliminate the need to determine integer ambiguity that is time-invariant and combined TDCP-based GPS with an inertial navigation system to overcome deep urbanmultipath environments.
Abstract: In this study, we developed a low-cost, high-precision vehicle navigation system for deep urban multipath environments using time-differenced carrier phase (TDCP) measurements. Although many studies are being conducted to navigate autonomous vehicles using the global positioning system (GPS), it is difficult to obtain accurate navigation solutions due to multipath errors in urban environments. Low-cost GPS receivers that determine the solution based on pseudorange measurements are vulnerable to multipath errors. We used carrier phase measurements that are more robust for multipath errors. Without correction information from reference stations, the limited information of a low-cost, single-frequency receiver makes it difficult to quickly and accurately determine integer ambiguity of carrier phase measurements. We used TDCP measurements to eliminate the need to determine integer ambiguity that is time-invariant and we combined TDCP-based GPS with an inertial navigation system to overcome deep urban multipath environments. Furthermore, we considered a cycle slip algorithm for its accuracy and a multi-constellation navigation system for its availability. The results of dynamic field tests in a deep urban area indicated that it could achieve horizontal accuracy of at the submeter level.

20 citations


Journal ArticleDOI
TL;DR: A low-cost method for GPS/INS fusion and error compensation of the GPS/ INS fusion algorithm during GPS interruption is proposed and an outdoor test indicates that the accuracy of the positioning has improved by 67% in each axis during an interruption.
Abstract: Inertial navigation is an edge computing-based method for determining the position and orientation of a moving vehicle that operates according to Newton’s laws of motion on which all the computations are performed at the edge level without need to other far resources. One of the most crucial struggles in Global Positioning System (GPS) and Inertial Navigation System (INS) fusion algorithms is that the accuracy of the algorithm is reduced during GPS interruptions. In this paper, a low-cost method for GPS/INS fusion and error compensation of the GPS/INS fusion algorithm during GPS interruption is proposed. To further enhance the reliability and performance of the GPS/INS fusion algorithm, a Robust Kalman Filter (RKF) is used to compensate the influence of gross error from INS observations. When GPS data is interrupted, Kalman filter observations will not be updated, and the accuracy of the position will continuously decrease over time. To bridge GPS data interruption, an artificial neural network-based fusion method is proposed to provide missing position information. A well-trained neural network is used to predict and compensate the interrupted position signal error. Finally, to evaluate the effectiveness of the proposed method, an outdoor test using a custom-designed hardware, GPS, and INS sensors is employed. The results indicate that the accuracy of the positioning has improved by 67% in each axis during an interruption. The proposed algorithm can enhance the accuracy of the GPS/INS integrated system in the required navigation performance.

16 citations


Journal ArticleDOI
Hongmei Chen1, Wang Xian-Bo1, Jianjuan Liu1, Jun Wang1, Wen Ye 
TL;DR: This paper considers the localization and navigation of multiple collaborative UAVs in networks with GPS/INS/UWB jammers in the case that the measurements are missed or randomly delayed by a sampling period, and proposes an improved method of cooperation location for the swarm.
Abstract: Location awareness and navigation promote varieties of emerging applications of mobile collaborative multiple uncrewed aerial vehicles (UAVs). Cooperative UAVs fuse the global position system (GPS), inertial navigation systems (INS), peer to peer ranging radios derived from relative navigation of ultra-wideband (UWB) under complicated environments. Those information sources can be incorporated into spatiotemporal cooperation posited by the intra-user measurement of INS and GPS, and the inter-user measurement of the relative navigation of swarm UAVs. This paper considers the localization and navigation of multiple collaborative UAVs in networks with GPS/INS/UWB jammers in the case that the measurements are missed or randomly delayed by a sampling period. In a navigation situation with a partially denied navigation signals (e.g.GPS Jammers for some UAVs, UWB jammers for others, etc.), we propose an improved method of cooperation location for the swarm, allowing measurement jammers concerning the normal sigma point belief propagation (SPBP). This algorithm integrates message passing based on the Bayesian framework, a sigma point belief propagation of random packet loss (SPBP-RPL) to exploit spatiotemporal cooperation and measurement knowledge. Compared with existing general sigma point belief propagation, the advantages of the novel method are validated through a simulation of swarm UAVs with GPS/INS/UWB. Results show that the algorithm of combining spatiotemporal cooperation with measurement knowledge reduces the location uncertainty of swarm UAVs agents and improves location accuracy remarkably.

9 citations


Journal ArticleDOI
TL;DR: A novel hybrid algorithm based on Gated Recurrent Unit (GRU) and interacting multiple model adaptive robust cubature Kalman filter (IMM-ARCKF) is proposed to solve the uncertainty of system model and measurement noise statics in the application of INS/GPS on the road.
Abstract: In order to ensure that Inertial Navigation System/Global Positioning System integrated navigation system (INS/GPS) can still provide high precision positioning results when GPS outages, a novel hybrid algorithm based on Gated Recurrent Unit (GRU) and interacting multiple model adaptive robust cubature Kalman filter (IMM-ARCKF) is proposed. Firstly, the IMM-ARCKF algorithm is proposed to solve the uncertainty of system model and measurement noise statics in the application of INS/GPS on the road. Then, GRU neural network is introduced into INS/GPS system which includes two modes of training and prediction. When GPS signal can be received, the GRU neural network works in the training mode. When GPS outages, the GRU neural network predicts the GPS position increment. Finally, the effectiveness of the algorithm is evaluated by the experiment and analysis. From the data of the experiment, the proposed algorithm can improve the positioning accuracy during GPS outages.

8 citations


Journal ArticleDOI
TL;DR: It is verified that the average velocity measurements can be substituted for Kalman filter measurements, and the performance improvements are confirmed through simulations and experiments.
Abstract: In this paper, we proposed an algorithm that improves the heading accuracy of the global positioning system (GPS)/inertial navigation system (INS) integrated system used in automobiles by manipulating INS velocity and GPS velocity measurements. Two velocities are provided by the GPS receiver: the velocity calculated using the position difference and the velocity calculated using the Doppler shift. The velocity obtained using the position difference is an average velocity for a certain time period, which is inaccurate under dynamic conditions because of its time delay. In contrast, the velocity from the Doppler shift is an instantaneous velocity and has no time delay. However, it also relies on pseudo-range error and noise, which degrades the heading estimation accuracy in low dynamic situations. Thus, although the velocity measurements can improve heading accuracy, the navigation performance is often degraded when the velocity measurements are used for GPS/INS integration. To improve the heading accuracy by solving the aforementioned problems, we proposed a heading accuracy improvement algorithm that employs the average velocity measurements obtained using the averaged GPS velocity and the average velocity of the INS. Since the proposed average velocity measurements are calculated using long baseline, the proposed algorithm can improve the heading accuracy without using other sensors, especially in the case of low dynamic situations. It can be easily applied to the existing GPS/INS integrated system, making it suitable for use in automotive navigation systems. In this research, it is verified that the average velocity measurements can be substituted for Kalman filter measurements, and the performance improvements are confirmed through simulations and experiments.

6 citations


Proceedings ArticleDOI
16 Oct 2020
TL;DR: In this paper, the authors considered that the GPS antenna and the Inertial Navigation System (INS) are not in the same position in the actual UAV integrated navigation system.
Abstract: GPS/INS combination is a commonly used integrated navigation mode. Inertial navigation system (INS) is a typical navigation mode, which can complete the acquisition of navigation parameters, but it will cause error accumulation problem when it is used alone, so using GPS to provide INS with a useful correction information. This article considers that the GPS antenna and the INS are not in the same position in the actual UAV integrated navigation system. Therefore, there is a lever arm that makes the observation equation nonlinear, In order to solve the problems, an improved filter is adopted. Finally, the data are used for comparative experiments, the stability of the algorithm is verified.

3 citations


Proceedings ArticleDOI
01 Nov 2020
TL;DR: In this paper, an integrated navigation algorithm based on Kalman filter in underground garage is presented, where the LIDAR/INS integrated navigation will take place the indoor Dead Reckoning(DR) algorithm.
Abstract: This paper presents an integrated navigation algorithm based on Kalman filter in underground garage. Traditional vehicular navigation algorithms are mainly employed in passenger vehicles traveling on the ground. Vehicles are generally equipped with Global Positioning System(GPS) and Inertial Navigation System(INS) to provide accurate positioning data for vehicles. However, the GPS receiver cannot acquire the positioning signal from the satellite, due to the ground barrier, after the vehicle enters the underground garage. The GPS/Inertial Navigation System(INS) integrated navigation cannot work normally. Inertial navigation can only calculate the position data in a short time, and the positioning accuracy diverges after a period of time. In this case, this paper introduces the integrated navigation algorithm of Light Detection and Ranging(LIDAR) scanner and inertial device. The LIDAR/INS integrated navigation will take place the indoor Dead Reckoning(DR) algorithm. The accuracy and stability of LIDAR scanner indoor navigation and positioning provide calibrated positioning for inertial navigation in Kalman filter. Finally, continuous indoor and outdoor seamless vehicle navigation driving trajectories are achieved by the navigation system.

2 citations


Journal ArticleDOI
TL;DR: An arbitrary misalignment angle error propagation model which does not rely on small misal alignment angles assumption and two simplified versions are presented, suitable to implement the in-motion alignment with GPS aided.
Abstract: Low-cost GPS/INS integration system is the ideal combination of navigation and positioning. However, the sensitivity of low-cost INS is not good enough for the initial alignment in statics base bef...

2 citations


Posted ContentDOI
14 Dec 2020
TL;DR: An extended Kalman filter derivation for loosely coupled GPS (Global Positioning System)/INS (Inertial Navigation System) integration based on quaternion attitude representation using the Earth-Centered Earth (ECEF) Frame is presented.
Abstract: This paper presents an extended Kalman filter derivation for loosely coupled GPS (Global Positioning System)/INS (Inertial Navigation System) integration based on quaternion attitude representation using the Earth-Centered Earth (ECEF) Frame. In this loosely coupling integration, both the position and velocity estimates from GPS receiver are used as the measurements to extended Kalman filter, and then they are integrated with inertial measurements from inertial measurement units (IMU) to estimate the attitude, position and velocity of an air vehicle. The velocity estimates which have centimeter level estimation error from the GPS receiver are used to improve the filter performance. For attitude estimation, the global attitude parameterization is given by a quaternion and a multiplicative quaternion-error approach is used to guarantee a normalization constraint of quaternion in the filter. Simulation results are shown to obtain the estimation of the attitude, position, velocity, biases and scale factors and to evaluate the performance of the EKF with the measurement combination composed of the two different t

DOI
01 Jan 2020
TL;DR: A new integrated positioning system that effectively combines INS, GPS, ultrasonic sensor, and barometer in GPS-denied environments and bears good potential to maintain positioning accuracy in harsh urban environments is proposed.
Abstract: Accuracy of an integrated Global Positioning System (GPS) / Inertial Navigation System (INS) relies heavily on the visibility of GPS satellites. Especially, its accuracy is dramatically degraded in urban canyon due to signal obstructions due to large structures. In this paper, we propose a new integrated positioning system that effectively combines INS, GPS, ultrasonic sensor, and barometer in GPS-denied environments. In the proposed system, the ultrasonic sensor provides velocity information along the forward direction of moving vehicle. The barometer output provides height information compensated for the pressure variation due to fast vehicle movements. To evaluate the performance of the proposed system, an experiment was carried out by mounting the proposed system on a test car. By the experiment result, it was confirmed that the proposed system bears good potential to maintain positioning accuracy in harsh urban environments.

Journal ArticleDOI
12 Apr 2020
TL;DR: A method which combines the original integrated GPS/INS with tri-axis rotation angles estimation and velocity constraints with results have shown that the rotation angles estimator helps to determine the Euler angles correctly, thereby increasing the quality of the position and velocity estimation.
Abstract: Use your smartphone to scan this QR code and download this article ABSTRACT Nowadays, autonomous robots are capable of replacing people with hard work or in dangerous environments, so this field is rapidly developing. One of the most important tasks in controlling these robots is to determine its current position. The Global Positioning System (GPS) was originally developed for military purposes but is nowwidely used for civilian purposes such asmapping, navigation for land vehicles, marine, etc. However, GPS has some disadvantages like the update rate is low or sometimes the satellites' signal is suspended. Another navigation system is the Inertial Navigation System (INS) can allow you to determine position, velocity and attitude from the subject's status, like acceleration and rotation rate. Essentially, INS is a dead-reckoning system so it has a huge cumulative error. An effective method is to integrate GPS with INS, in which the center is a nonlinear estimator (e.g. the Extended Kalman filter) to determine the navigation error, from which it can update the position the object more accurately. To improve even better accuracy, this paper proposes new method which combines the original integrated GPS/INS with tri-axis rotation angles estimation and velocity constraints. The experimental system is built on a low-cost IMU with tri-axis gyroscope, accelerometer and magnetometer and a GPS module to verify the model algorithm. Experiment results have shown that the rotation angles estimator helps us to determine the Euler angles correctly, thereby increasing the quality of the position and velocity estimation. In practice, the accuracy of roll and pitch angle is 2 degrees, the error of yaw angle is still large. The achieved horizontal accuracy is 2m when the GPS signal is stable and 3m when the GPS signal is lost in a short period. Compared with individual GPS, the error of the integrated system is about 10% smaller.