Abstract
M.Ing.
The Graphics Processor Unit (GPU) has been in the gaming industry for several years
now. Of late though programmers and scientists have started to use the parallel processing
or stream processing capabilities of the GPU in general numerical applications.
The Monte Carlo method is a processing intensive methods, as it evaluates systems with
stochastic components. The stochastic components require several iterations of the systems
to develop an idea of how the systems reacts to the stochastic inputs. The stream
processing capabilities of GPUs are used for the analysis of such systems.
Evaluating low-cost Inertial Measurement Units (IMU) for utilisation in Inertial Navigation
Systems (INS) is a processing intensive process. The non-deterministic or stochastic
error components of the IMUs output signal requires multiple simulation runs to properly
evaluate the IMUs performance when applied as input to an INS. The GPU makes use
of stream processing, which allows simultaneous execution of the same algorithm on multiple
data sets. Accordingly Monte Carlo techniques are applied to create trajectories for
multiple possible outputs of the INS based on stochastically varying inputs from the IMU.
The processing power of the GPU allows simultaneous Monte Carlo analysis of several
IMUs. Each IMU requires a sensor error model, which entails calibration of each IMU to
obtain numerical values for the main error sources of lowcost IMUs namely scale factor,
non-orthogonality, bias, random walk and white noise. Three low-cost MEMS IMUs was
calibrated to obtain numerical values for their sensor error models. Simultaneous Monte
Carlo analysis of each of the IMUs is then done on the GPU with a resulting circular
error probability plot. The circular error probability indicates the accuracy and precision
of each IMU relative to a reference trajectory and the other IMUs trajectories.
Results obtained indicate the GPU to be an alternative processing platform, for large
amounts of data, to that of the CPU. Monte Carlo simulations on the GPU was performed
200 % faster than Monte Carlo simulations on the CPU. Results obtained from the Monte
Carlo simulations, indicated the Random Walk error to be the main source of error in
low-cost IMUs. The CEP results was used to determine the e ect of the various error
sources on the INS output.