OpenShoe OpenShoe Matlab Implementation
ZUPT aided INS software components for an embedded platform
Status: Beta
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johnolof
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%> @mainpage The OpenShoe Matlab Implemenation %> %> \section sec1 Introduction %> This is the documentation for the OpenShoe Matlab Implemenation. The %> OpenShoe Matlab Implementation is a Matlab script (library) for processing %> inertial measurement unit (IMU) data using a Kalma filter based zero-velocity %> aided inertial navigation system algorithm. The main (skeleton) file that %> should be called to run the algorithm is \a main.m. All settings for the %> algorithm are done in the file \a settings.m. The processing of the data is %> done by the functions zero_velocity_detector and ZUPTaidedINS, located %> in the files \a zero_velocity_detector.m and \a ZUPTaidedINS.m, respectively. %> The result from running the algorithm is plotted by calling the script %> \a view_data.m. %> %> \section sec2 The zero-velocity aided inertial navigation algorithm %> The zero-velocity aided inertial navigation system algorithm is %> implemented using a complimentary feedback filtering structure. That is, %> the inertial navigation system works as the backbone of the system and %> a Kalman filter is used to estimate (track) the perturbations (errors) %> in the inertial navigation system. When a zero-velocity observation %> is done, the Kalman filter estimates the current perturbations (errors) %> in the navigation state estimate of the inertial navigation system; the %> estimated perturbations are feedback into the inertial navigation system %> to correct its internal states. The user can choose between four %> different state space models to be used in the Kalman filter. The %> default model is a nine state model, having the position, velocity, and %> attitude perturbations as states. The user can then choose between %> adding sensor biases errors and/or scale factors errors as additional %> states to the default model. %> %> To determine when a zero-velocity update should be applied in the Kalman %> filter, a zero-velocity detection algorithm is used. The zero-velocity %> detection algorithm monitors the signal measured by the inertial %> measurement unit, and based upon the prior information about the signal %> at different motion dynamics it chooses between the hypotheses that the %> navigation system is stationary or is moving. The user can choose %> between four different zero-velocity detection algorithms, the SHOE %> (\a GLRT.m) detector, the acceleration moving variance (\a MV.m) %> detector, the acceleration magnitude (\a MAG.m) detector, and the %> angular rate energy (\a ARE.m) detector. Details about these detectors %> can be found in the papers %> %> \li <A href="http://dx.doi.org/10.1109/TBME.2010.2060723">Zero-Velocity Detection -- An Algorithm Evaluation</A> %> \li <A href="http://dx.doi.org/10.1109/IPIN.2010.5646936">Evaluation of Zero-Velocity Detectors for Foot-Mounted Inertial Navigation Systems</A> %> %> \section sec3 Algorithm settings and configuartions %> All settings for the algorithm and the inertial measurement data file %> that should be used are controlled from the function settings.m. The %> default settings are such that the algorithm produces a good output %> when processing, the with the program, provided inertial measurement %> unit data and with the default state space model. For other data sets or when using %> the higher order state space models the settings may have to be tuned in %> order for the algorithm to produce a good result. Information on how to %> tune the system can be found in the paper %> %> \li <A href="http://dx.doi.org/10.1109/IPIN.2010.5646939">Performance characterisation of foot-mounted ZUPT-aided INSs and other related systems</A> %> %> \section sec4 The inertial measurement unit data %> The inertial measurement unit data that comes with the OpenShoe Matlab %> Implementation code has been recorded using a MicroStrain 3DX-GX2 inertial %> measurement unit, with a dynamic range of +-18g and 1200 deg/s, and a %> sample rate of 250 Hz. The inertial measurement unit was mounted in the %> sole of the right side shoe of the user, and the user was walking in a %> closed loop trajectory where he returned to his starting position within %> +-1 cm. The gate speed for the different data sets are 5 km/h and 7 km/h.
