@inproceedings{Carvalho2009Inertialb,

title = {GPS-aided inertial navigation performance evaluation using the Extended Kalman Filter},

author = {Carvalho and F M R Bruno and M G S Carvalho and R T},

url = {https://www.sige.ita.br/edicoes-anteriores/2009/st/VIII_2.pdf},

year  = {2009},

date = {2009-01-01},

booktitle = {Simp\'{o}sio de Aplica\c{c}\~{o}es Operacionais em \'{A}reas de Defesa 2009 (SIGE2009)},

abstract = {We discuss the performance of a GPS-aided inertial navigation system that employs the Extended Kalman Filter (EKF) and GPS position measurements for estimating and correcting position errors on the inertial navigation system readings. A proper error model to account for position, velocity and attitude errors is described and validate. It also estimates the bias drift of both accelerometers and gyrometers, and GPS clock bias. Results on the EKF performance are presented for different flight trajectories. Key words Inertial Navigation, GPS, Kalman Filter.},

keywords = {GPS, Inertial Navigation, Kalman Filter},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Jacques2007lb,

title = {In-Flight Alignment in INS-Aiding with Switched Feedforward/Feedback of Error Estimates},

author = {Jacques W aldmann},

url = {https://www.sige.ita.br/edicoes-anteriores/2007/st/GE_26.pdf},

year  = {2007},

date = {2007-01-01},

booktitle = {Simp\'{o}sio de Aplica\c{c}\~{o}es Operacionais em \'{A}reas de Defesa 2007 (SIGE2007)},

abstract = {Unmanned air vehicles often resort to a low-cost inertial measurement unit (IM U) in an inertial navigation system (INS) to estimate position and velocity. Stand-alone INS operation yields unbounded estimation errors. Such behavior motivates INS aiding by auxiliary position and velocity sensors to limit navigation error. Acceleration maneuvers and IM U rotation with respect to the vehicle are used to enhance the observability of INS error dynamics, in conjunction with Kalman filter-based sensor fusion to estimate position and velocity errors, IM U misalignment and sensor errors. This work investigates feedforward and feedback of error estimates for INS aiding. Feedforward integration is used to estimate IM U misalignment and sensor errors with adequate accuracy, and then switches to feedback integration for in-flight alignment (IFA), that is INS reset and IM U calibration during operation. A M onte Carlo simulation provides evidence supporting the approach.},

keywords = {autonomous vehicles, in-flight alignment, Inertial Navigation, robotics, sensor fusion},

pubstate = {published},

tppubtype = {inproceedings}

}