Topic
GPS/INS
About: GPS/INS is a research topic. Over the lifetime, 3554 publications have been published within this topic receiving 62784 citations.
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TL;DR: The compensation method for GPS and an odometers is introduced and new compensation methods are proposed for an odometer to consider the effect of coordinate transformation errors and the scale factor error.
Abstract: For more accurate navigation, lever arm compensation is considered. The compensation method for GPS and an odometer is introduced and new compensation methods are proposed for an odometer to consider the effect of coordinate transformation errors and the scale factor error. The methods are applied to a GPS/INS/odometer integrated system and the simulation and experimental results show its effectiveness. Navigation is defined as all the related theories and technologies for obtaining position, velocity and attitude of a vehicle. With the use of navigation technology, one can know his/her position and can plan trajectory for their destination. Nowadays, especially, the need for the navigation of land vehicles has rapidly increased. In the near future, ubiquitous personal navigation will be spread and navigation will become a more essential technology. The Global Positioning System (GPS) is a satellite- based radio navigation system (1). It allows a user with a receiver to obtain accurate position information anywhere on the globe. It provides position information whose errors would not increase with respect to time. However, a signal from the GPS satellite cannot often arrive at a receiver in an urban area. In those cases, it cannot provide any position information.
74 citations
TL;DR: A new algorithm of cycle slip detection and identification has been developed that can efficiently detect and identify the cycle slips and subsequently improve the navigation performance of the integrated PPP GPS system.
Abstract: The recently developed integrated Precise Point Positioning (PPP) GPS/INS system can be useful to many applications, such as UAV navigation systems, land vehicle/machine automation and mobile mapping systems. Since carrier phase measurements are the primary observables in PPP GPS, cycle slips, which often occur due to high dynamics, signal obstructions and low satellite elevation, must be detected and repaired in order to ensure the navigation performance. In this research, a new algorithm of cycle slip detection and identification has been developed. With the aiding from INS, the proposed method jointly uses WL and EWL phase combinations to uniquely determine cycle slips in the L1 and L2 frequencies. To verify the efficiency of the algorithm, both tactical-grade and consumer-grade IMUs are tested by using a real dataset collected from two field tests. The results indicate that the proposed algorithm can efficiently detect and identify the cycle slips and subsequently improve the navigation performance of the integrated system.
72 citations
TL;DR: This method uses radial basis function (RBF) neural network coupled with time series analysis to forecast the measurement update of KF, resulting in reliable performance during GPS outages, and is more effective than two other methods.
Abstract: Position and orientation system (POS) is a key technology widely used in remote sensing applications, which integrates inertial navigation system (INS) and GPS using a Kalman filter (KF) to provide high-accuracy position, velocity, and attitude information for remote sensing motion compensation. However, when GPS signal is blocked, the POS accuracy will decrease owing to the unbounded INS error accumulation. To improve the reliability and accuracy of POS, this paper proposes a hybrid prediction method for bridging GPS outages. This method uses radial basis function (RBF) neural network coupled with time series analysis to forecast the measurement update of KF, resulting in reliable performance during GPS outages. In verifying the proposed hybrid prediction method, a flight experiment was conducted in 2011, based on a high-precision Laser POS. Experimental results show that the proposed hybrid prediction method is more effective than two other methods (KF and RBF neural network).
72 citations
16 Sep 2005
TL;DR: In this article, the performance of EKF-based and sigma-point Kalman filter-based tightly coupled GPS/INS systems is compared in numerical simulations, including situations with less than four satellites in view, and the simulation results were confirmed by post-processing of raw GPS and inertial sensor data that was recorded during a test drive.
Abstract: In this paper, the fusion of GPS pseudorange and deltarange measurements with inertial sensor data is adressed. For many years, extended Kalman filters (EKF) have been applied for this task with great success. However, from a theoretical point of view, the EKF is a sub-optimal choice: The system dynamics model, which is given by the inertial navigation strapdown equations, as well as the pseudorange and deltarange measurement models are nonlinear. The EKF approximates the propagation of Gaussian random vectors through these nonlinear equations by a linear transformation. This allows to capture the variance-covariance matrix of the propagated Gaussian random vectors with first order accuracy only. The family of sigma-point Kalman filters (SPKF) offers an approximation of variance-covariance matrix which is accurate to at least second order. Therefore, the performance of EKF-based and SPKFbased tightly coupled GPS/INS systems is compared in numerical simulations. Different inertial sensor grades from MEMS to FOG and a variety of scenarios are investigated, including situations with less than four satellites in view. Additionally, the simulation results were confirmed by the post-processing of raw GPS and inertial sensor data that was recorded during a test drive. It was found that except for specific situations without practical relevance, EKF and SPKF offer an identical performance. This is due to the fact that for tightly coupled - as well as loosely coupled - GPS/INS integration the higher-order transformation terms are negligible, which is shown analytically.
72 citations
TL;DR: The results indicate that the proposed method can improve the position, velocity and attitude accuracy of the integrated system, especially the position parameters, over long GPS outages.
Abstract: The integration of Global Positioning Systems (GPS) with Inertial Navigation Systems (INS) has been very actively studied and widely applied for many years. Some sensors and artificial intelligence methods have been applied to handle GPS outages in GPS/INS integrated navigation. However, the integrated system using the above method still results in seriously degraded navigation solutions over long GPS outages. To deal with the problem, this paper presents a GPS/INS/odometer integrated system using a fuzzy neural network (FNN) for land vehicle navigation applications. Provided that the measurement type of GPS and odometer is the same, the topology of a FNN used in a GPS/INS/odometer integrated system is constructed. The information from GPS, odometer and IMU is input into a FNN system for network training during signal availability, while the FNN model receives the observations from IMU and odometer to generate odometer velocity correction to enhance resolution accuracy over long GPS outages. An actual experiment was performed to validate the new algorithm. The results indicate that the proposed method can improve the position, velocity and attitude accuracy of the integrated system, especially the position parameters, over long GPS outages.
72 citations