Global Positioning Systems, Inertial Navigation, and Integration

By M. Grewal, L. Weill, A. Andrews. 2000

1,880 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Geography, Planning and Development

View Paper: https://www.semanticscholar.org/paper/01afb5172323bf54096b9f4bf90fdc349b16a577

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Citations in the corpus

Improving Adaptive Kalman Estimation in GPS/INS Integration (2007, background): “The uncertainty in the covariance parameters of the process noise (Q) and the observation errors (R) has a signiﬁcant impact on Kalman ﬁltering performance (Grewal and Andrews, 1993; Grewal and Weil, 2001; Salychev, 2004).”
Mobile Robot Localisation and Mapping in Extensive Outdoor Environments (2002, background): “GPS (Global Positioning System) [126, 62, 6] is a satellite based localisation system that is capable of providing a three-dimensional global location estimate at almost any point on (or above) the planet surface.”
Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications (2009, background): “in the long term due to sensors’ bias error drift, scale factor instability, and misalignment [3]–[5].”
Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations (2010, background): “Filtering problems in the continuous-discrete time domain often arise in numerous applications such as target tracking [8], [41], navigation [21], stochastic control [4], and finance [35].”
On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems (2007, background): “Examples of such applications are GPS and inertial navigation [5], [6], target tracking [7]–[9], estimation of biological processes [10], telecommunications [11], [12], stochastic optimal control [13], [14] and inverse problems in physics [15].”
In-Car Positioning and Navigation Technologies—A Survey (2009, methodology): “Detailed descriptions of the various coordinate systems used in navigation, together with common coordinate transformations, are found in the standard textbooks on inertial navigation [28], [30], [32], [61], [65], [66].”
A Nonlinear Bayesian Filtering Framework for ECG Denoising (2007, methodology): “Another approach known as sequential measurement incorporation [35], uses different observations one-by-one to aid the stability of the KF.”
A Nonlinear Filtering Approach for the Attitude and Dynamic Body Acceleration Estimation Based on Inertial and Magnetic Sensors: Bio-Logging Application (2011, methodology): “The prototype “logger,” the attitude representation, and sensor models are described here.”
Bayesian Filtering and Smoothing (2013, background): “GPS/INS navigation (Grewal et al., 2001; Bar-Shalom et al., 2001) is a form of integrated inertial navigation where the inertial navigation system (INS) is combined with a GPS receiver unit.”
Issues, progress and new results in robust adaptive control (2006, neutral): “This requires careful design of the KFs, perhaps robustified through the use of " fake-white-plant-noise " – a time-honoured design trick in linear and nonlinear estimation practice [67].”
GPS/IMU data fusion using multisensor Kalman filtering: introduction of contextual aspects (2006, methodology): “(8) is theoretically a v 2 distribution with l degrees of freedom [11], whose probability density function (pdf) is drawn in Fig.”