Wireless sensor network technology has now been widely adopted. In many applications, distributed sensor nodes collect data at different locations and the location information of each node is required. The Global Positioning System is commonly used to identify the location of the nodes in such networks. Although GPS localization has consistent long-term accuracy, it is limited by the inherent dependency on a direct line of sight to 4 or more external satellites. The increasing demand for an embedded system providing reliable navigation solutions regardless of its operational environment has motivated investigations into the use of integrated systems that combine inertial sensors with GPS receivers. This research proposes a hardware architecture for location-based wireless sensor networks. In this architecture, each sensor node consists of a GPS receiver, a reduced set of low cost micro-electro-mechanical-system-based INS and a wireless transceiver. Sensor nodes in WSN are often equipped with irreplaceable batteries, which makes the power consumption crucial. To reduce the energy consumption, a microcontroller is used to control the power supply. Besides, a motion detection scheme is proposed by taking advantage of the ultra low-power wake-up function of the microcontroller. A low-power featured digital signal processor is used to accomplish the navigation computation using the Kalman filter for GPS/INS data fusion. Non-Holonomic Constraints derived velocity updates are applied to reduce the position errors. Field tests are conducted to verify the real-time performance of the proposed system with a positioning update rate of 20 Hz. The first test shows that the 2D INS/GPS integration can maintain the average system position error within 5 meters during a 60-second GPS outage. The second test used low cost inertial sensors. The average position error was 10.17 meters during a 20-second outage. The largest RMS value of position errors among these outages was within 14.5 meters. Furthermore, additional accuracy improvements of approximately 1.4 meters were achieved by utilizing NHC during GPS outages. The third test shows that the average error during a 30-second outage is approximately 20.6 meters for the on-foot scenario and 26.7 meters for the in-vehicle scenario. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2012-01-13 14:46:45.44

Processing GPS carrier-phase measurements can provide high positioning accuracy for several navigation applications. However, if not detected, cycle slips in the measured phase can strongly deteriorate the positioning accuracy. Cycle slips frequently occur in areas surrounded by trees, buildings, and other obstacles. The dynamics experienced by the GPS receiver in kinematic mode of navigation also increases the possibility of cycle slips. Detection and correction of these cycle-slips is essential for reliable navigation. One way of detecting and correcting for cycle slips is to use another system to be integrated with GPS. Inertial Navigation Systems (INS), using three-axis accelerometers and three-axis gyroscopes, is integrated with GPS to provide more reliable navigation solution. Moreover, INS was utilized in the past for GPS cycle slip detection and correction. For low cost applications, Micro-Electro-Mechanical-Systems (MEMS) accelerometers and gyroscopes are used inside INS. For land navigation, reduced inertial sensor system (RISS) utilizing two accelerometers, one gyroscope, and the vehicle odometer was suggested. MEMS-based RISS has the advantage of using less number of MEMS-based gyroscopes and accelerometers thus reducing the overall cost and avoiding the complex error characteristics associated with MEMS sensors. In this thesis, we investigate the use of MEMS – based RISS to aid GPS and detect and correct for cycle slips. The Kalman filter was employed in centralized fashion to integrate the measurements from both GPS and RISS. This thesis research also offers a new threshold selection criterion resulting in a more robust cycle slip detection and correction. The proposed method was tested in different scenarios of road tests in land vehicle. Results show accuracy iii improvement over the conventional double differenced pseudoranges-based integrated system. Moreover, the adaptive selection criterion of the detection threshold proposed in this thesis improves the detection rate, especially in the case of small-sized cycle slips. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2013-05-06 18:11:57.076

Navigation algorithms integrating measurements from multi-sensor systems overcome the problems that arise from using GPS navigation systems in standalone mode. Algorithms which integrate the data from 2D low-cost reduced inertial sensor system, consisting of a gyroscope and an odometer, along with a GPS via a Kalman filter has proved to be worthy in providing a consistent and more reliable navigation solution compared to the standalone GPS. It has been also shown to be beneficial, especially in GPS-denied environments such as urban canyons and tunnels. The main objective of this research is to narrow the idea-to-implementation gap that follows the algorithm development by realizing a low-cost real-time embedded navigation system that is capable of computing the data-fused positioning solution instantly. The role of the developed system is to synchronize the measurements from the three sensors, GPS, gyroscope and odometer, relative to the pulse per second signal generated from the GPS, after which the navigation algorithm is applied to the synchronized measurements to compute the navigation solution in real-time. Xilinx’s MicroBlaze soft-core processor on a Virtex-4 FPGA is utilized and customized for developing the real-time navigation system. The soft-core processor offers the flexibility to choose or implement a set of features and peripherals that are tailored to the specific application to be developed. An embedded system design model is chosen to act as a framework for the work flow to be carried through the system life cycle starting from the system specification phase and ending with the system release. The developed navigation system is tested first on a mobile robot to reveal system bugs and integration problems, and then on a land vehicle testing platform for further testing. The real-time solution from the implemented system when compared to the solution of a high-end navigation system, proved to be successful in providing a comparable consistent real-time navigation solution. Employing a soft-core processor in the kernel of the navigation system, provided the flexibility for communicating with the various sensors and the computation capability required by the Kalman filter integration algorithm. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2010-10-11 16:08:38.811