Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.

Thanks to advances in the development of Micro-Electromechanical Systems (MEMS), it has been possible to fabricate small dimension and cheap accelerometers and gyros, which are being used in many applications where the GPS/INS integration is carried out, as for example to identify track defects, navigation, geo-referencing, agriculture, etc. Although these MEMS devices have a low-cost, they present different errors which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modelling of these errors is necessary in order to improve the system performance. In this work, Allan Variance and Power Spectral Density techniques are used to identify the random processes that affect the inertial sensor data. Once the random components are identified, they are modelled using first-order Gauss-Markov and random walk processes. Two models are assessed augmenting the states of the Extended Kalman Filter (EKF) to 6 and 9. Subsequently, another analysis and modelling of the inertial sensors which combines Autoregressive Filters and Wavelet De-noising is implemented and in this case the EKF of the loosely coupled GPS/INS integration strategy is augmented with 6, 12 and 18 states. Finally, the results show a comparison between these sensor error models with real data under GPS outage conditions.

The Internal Configuration Access Port (ICAP) is the core component of any dynamic partial reconfigurable system implemented in Xilinx SRAM-based Field Programmable Gate Arrays (FPGAs). We developed a new high speed ICAP controller, named AC_ICAP, completely implemented in hardware. In addition to similar solutions to accelerate the management of partial bitstreams and frames, AC_ICAP also supports run-time reconfiguration of LUTs without requiring precomputed partial bitstreams. This last characteristic was possible by performing reverse engineering on the bitstream. Besides, we adapted this hardware-based solution to provide IP cores accessible from the MicroBlaze processor. To this end, the controller was extended and three versions were implemented to evaluate its performance when connected to Peripheral Local Bus (PLB), Fast Simplex Link (FSL), and AXI interfaces of the processor. In consequence, the controller can exploit the flexibility that the processor offers but taking advantage of the hardware speed-up. It was implemented in both Virtex-5 and Kintex7 FPGAs. Results of reconfiguration time showed that run-time reconfiguration of single LUTs in Virtex-5 devices was performed in less than 5 μs which implies a speed-up of more than 380x compared to the Xilinx XPS_HWICAP controller.