Topic
GPS/INS
About: GPS/INS is a research topic. Over the lifetime, 3554 publications have been published within this topic receiving 62784 citations.
Papers published on a yearly basis
Papers
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01 Jan 2001
TL;DR: This master thesis report presents the developement of an INS/GPS navigation loop written in ANSI C++ using a standard matrix library and the resulting navigation filter estimates the attitude within two degrees with 95% confidence and the position within two meter using 95%confidence.
Abstract: This master thesis report presents the developement of an INS/GPS navigation loop written in ANSI C++ using a standard matrix library. The filter have been tested on an Unmanned Aerial Vehicle (UAV) called Brumby. Here data have been logged from the Inertial Measurement Unit (IMU) and the Global Positioning System (GPS) receiver. This data have then been postprocessed and run through the navigation filter for estimation of position, attitude and velocity of the vehicle during the flights. The error feedback to the Inertial Navigation System (INS) is done with a complement filter implemented using a kalman filter written in information form.The resulting navigation filter estimates the attitude within two degrees with 95% confidence and the position within two meter using 95% confidence.
48 citations
20 Jan 2006
TL;DR: In this article, the authors describe the design of a tightly coupled GPS/INS integration system based on nonlinear Kalman filtering methods, which uses a set of weighted samples (sigma points) to capture the first and second order moments of the prior random variable.
Abstract: This paper describes the design of a tightly coupled GPS/INS integration system based on nonlinear Kalman filtering methods. The traditional methods include linearization of the system around a nominal trajectory, and the extended Kalman filtering (EKF) method which linearizes the system around the previous estimate, or the predication, whichever is available. The recently proposed sigma-point Kalman filtering (SPKF) method uses a set of weighted samples (sigma points) to completely capture the first and second order moments of the prior random variable. In contrast to the EKF, the SPKF has a simpler implementation as it does not require the Jacobian matrices – the computation of which may lead to analytical or computational problems in some applications. This research is conducted under the Australian Cooperative Research Centre (CRC) for Spatial Information (CRC-SI) project 1.3 'Integrated Positioning and Geo-referencing Platform'. The project aims to develop a generic hardware/software platform for positioning and imaging sensor integration. The current work focuses on development of software and algorithms, and a field programmable gate arrays (FPGA) based GPS/INS data logging system. In the current development phase, a tightly coupled GPS/INS integration system based on a linearization around the INS solution has been designed and implemented. The system uses the GPS pseudorange and Doppler measurements to estimate the INS errors. This paper describes further developments of the integration filter design based on the EKF and SPKF methodologies, in order to compare the performance of nonlinear filtering approaches. Experimental results are presented and further planned developments are outlined.
47 citations
TL;DR: A Kalman estimator is formulates as an optimal tracker/navigator for GPS by considering the signal and navigation processing performed by a GPS set not as separate functions, but as a single integrated function.
Abstract: This paper formulates a Kalman estimator as an optimal tracker/navigator for GPS. The approach is motivated by considering the signal and navigation processing performed by a GPS set not as separate functions, but as a single integrated function. The practical realization of this integrated tracker/navigator is discussed, and simulation results are presented which permit comparison of its performance with that of more conventional, partitioned designs.
47 citations
Patent•
10 Nov 2010TL;DR: In this article, a method for augmenting GPS data using an in-vehicle vision-based module is provided for determining vehicle position using position-related data obtained from a position module.
Abstract: A method is provided for augmenting GPS data using an in-vehicle vision-based module. A vehicle position is determined utilizing position-related data obtained from a position module. A position error is estimated on a periodic basis. A determination is made whether the position error estimate exceeds a first predetermined error threshold. Tracking data is generated for the vehicle over a course of travel utilizing captured images from the in-vehicle vision based module. The tracking data is integrated with the position-related data to estimate the vehicle position in response to the position error estimate exceeding the first predetermined error threshold. A determination is made whether the position error estimate decreases below a second predetermined error threshold. The vehicle position is re-determined using only the position-related data when the position error estimate decreases below the second predetermined error threshold.
47 citations
01 Nov 2012
TL;DR: The design of the particle filter and the heuristic heading approach is described and the use of a building floor plan to further aid navigation is investigated using a particle filter approach whereby particles which cross walls are removed and those which navigate in open spaces are allowed to continue.
Abstract: Foot mounted inertial navigation is an effective method for obtaining high quality pedestrian navigation solutions from MEMS sensors. Zero-Velocity information from stationary periods in the step-cycle can be used to regularly correct position drift and update estimates of the inertial sensor biases, hence dramatically improving the navigation solution.
47 citations