Integrated Satellite-Navigation for Challenging Environments

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Positioning and navigation information is becoming ubiquitous as automobiles, mobile telephones and even clothing are being equipped with navigation systems/sensors. Most navigation systems rely, at least in part, on Global Navigation Satellite System (GNSS) technology such as the U.S. Global Positioning System (GPS). Unfortunately, GNSS signals that reach the Earth have extremely low power, and system performance degrades in areas where satellite signals are attenuated or obstructed by such things as foliage and buildings. In other scenarios, particularly in urban areas, the satellite signals are reflected prior to being received which limits a receiver’s ability to measure the direct (ideal) distance to the satellite. In both cases, the GNSS receiver cannot track the satellite signals reliably, if at all. This problem can be tackled in two ways: by improving the capability of GNSS receivers, and by integrating GNSS with other sensors. The number and availability of low-cost sensors has increased dramatically in recent years, especially in smart phones and automobiles, and include WiFi radios (and the online data they can access), cameras, light sensors, accelerometers, gyroscopes and magnetometers. The challenge, however, is to determine how to best integrate these sensors with GNSS in order to yield a more accurate and reliable position solution, especially in areas where GNSS is not performing optimally. With this in mind, this project will improve GNSS-based navigation in degraded environments using different approaches by: 1) Enhancing current GNSS receiver performance by using a new architecture where all information from all satellites is used to compute the position. This contrasts with traditional architectures where only a subset of data from each satellite is used to compute a (sub-optimal) position. The result will be more accurate position solutions in more locations, thus enabling more applications. 2) Incorporating 3D building models that are becoming more widely available (e.g., Google Earth). Advanced signal processing methods with GNSS receivers will be integrated with 3D building model data to identify and correct reflected signals that reach the receiver. In this way, instead of only trying to minimize the error from the reflected signal (as is traditionally done), this approach uses the reflected signal in a constructive manner to improve performance. 3) Using an upward-facing camera, such as those found on the front of smart phones, to compute position information and merge this information with GNSS. The research will exploit benefits of an upward-facing camera setup (relative to forward-facing cameras which are more typically used for navigation) to improve vision-based navigation. Also, for the first time, the skyline variability in urban canyons – that is, the outline of the sky that is visible between buildings at a particular location – will be used as a means to determine the absolute position of the user. Later stages of the project will merge these approaches in order to yield optimal performance in a wider range of environments. The algorithms and software developed as part of this project will be of benefit to GNSS receiver manufacturers and to navigation system developers that integrate GNSS with the sensors listed above. With the GNSS-enabled market expected to be worth over $1T (CAD) between 2014 and 2020 this work will help Canada remain a leader in this rapidly growing field. More importantly, the students trained during this research will possess the relevant knowledge and skill to contribute positively to the navigation industry upon graduation.