Um estudo sobre arquiteturas de hardware para técnicas de fusão sensorial através do EKF e da estimação de estados baseada em filtros híbridos otimizados

This work presents a study and hardware implementation of a sensor fusion technique of 9 Degrees Of Freedom (DOF) Inertial Measurement Units (IMUs) based on the Extended Kalman Filter (EKF) as a fusion mechanism; with the objective of estimating the position (attitude) and orientation (heading) in real time. The IMU used is composed of sensors (gyroscope, accelerometer and magnetometer) in the three orthogonal axes (X , Y and Z). The sensor fusion technique was based on two Extended Kalman Filters (EKFs), one for attitude estimation (roll and pitch) and the other for estimating the heading (yaw). Therefore, the gyroscope was used as the model of the system in both cases, while the accelerometer and the magnetometer were used independently as measurement models. These sensors were calibrated prior to filtration (processing). The study of this approach of sensorial fusion was carried out through a bench (platform) manufactured in 3D printing to facilitate movement during calibration and tests of IMUs. In this study, sensor readings and data processing were done through Arduino and Matlab R2015a, respectively. The architecture was embedded in Field Programmable Gate Array (FPGA) and developed using the hardware description language VHDL (Very High Description Language). The architecture operates at a frequency of 50MHz and makes use of 27-bits floating-point operators. Additionally, the communication between the IMU and the FPGA was performed through the I2C protocol implemented in software, through the processor Nios II. Therefore, the readings of the measurements provided by the sensors were made by Nios II, while the data processing was performed in the FPGA-embedded architecture. Regarding the study of the performance of different optimized hybrid filters for state estimation problems, four different types of resamplings (multinomial, systematic, stratified and residual) were considered. This was done with the aim of evaluating different approaches that could be used in applications where it is essential to have a good quality of estimation of state variables (eg multi-sensor network in an exoskeleton). These approaches combine the Particle Filter (PF), the Kalman Filter (KF), and the Particle Swarm Optimization (PSO) algorithm. Where the KF and the PSO were implemented in the sampling step and after the resampling of the PF, respectively. Furthermore, two methods of adding diversity were used (Opposition Based Learning (OBL) and Attractive and Repulsive (AR)). In order to evaluate these hybrid approaches two nonlinear systems (benchmarks) were considered. Finally, the hardware results presented in this work showed a significant gain in the processing speed of the sensor fusion, since the processing in the architecture is 6023 times faster than the same algorithm implemented in Nios II (operating at 100 MHz). With respect to the results of the optimized hybrid filters, the ones that reached the lowest error (RMSE) in the state estimation were the UKF PF OPSO for the benchmark 1 and the UKF PF PSO for the benchmark 2.