GPS monitoring in urban zones: calibration and quantification of multipath effects Michael Kochly *1a , Tracy Kijewski-Correa a , James Stowell b a Dept. of Civil Eng., University of Notre Dame, 156 Fitzpatrick Hall, Notre Dame, IN, USA 46556 b Leica Geosystems Inc., 4855 Peachtree Industrial Blvd., Norcross, GA, USA 30042 ABSTRACT Health monitoring is becoming an increasingly valuable tool for assessment of aging infrastructure in urban zones. For such applications, Global Positioning Systems (GPS) present a promising monitoring technique — one that is able to capture the total displacements of a structure. However, due to the relative infancy of this technology, there are still issues to be resolved, including the characterization and removal of multipath effects. This paper discusses the manifestation and removal of multipath errors by examining the full-scale response of a tall building to demonstrate the accuracy of high precision GPS in comparison with traditional sensors like accelerometers. Keywords: Structural health monitoring, GPS, multipath effects 1. INTRODUCTION Civil Infrastructure Systems (CIS) across the United States are faced with the effects of aging and impacts by natural, and even manmade, hazards, requiring a means to accurately and continuously quantify their condition. Though the use of Structural Health Monitoring (SHM), potential problems can be diagnosed in the early stages through the use of embedded sensors, thus avoiding expensive and lengthy closures for repairs, and more importantly, before potential loss of human life. This philosophy is growing in acceptance throughout the United States for applications to bridges as well as buildings to allow a rapid assessment after an event, such as an earthquake (BORP, 2003; Celebi et al., 2004), to provide an indication of response level under service events for the operation of building systems, e.g., elevators, or to monitor existing cracks or settlements as part of a long-term maintenance program. However, since infrastructure is largely concentrated in urban zones, any viable health monitoring technology used in these applications must be capable of operating in this environment. In the past, health monitoring has been accomplished using traditional sensors, such as accelerometers and strain gages. While providing useful data, these sensors are incapable of providing the total, global displacements of a structure, as data is lost in the double integration process of accelerations and strain gages only offer a localized displacement. Thus, valuable information such as the mean and background component of wind response, permanent offsets resulting from earthquakes, settlement, and thermal expansion can not be captured by traditional sensing technologies. Other global displacement sensors, like terrestrial position systems (TPS), suffer from a limited range and require clear line of sight to the reflector, which is not possible in foggy, hazy, or stormy conditions. With sampling rates of up to 20 Hz, global positioning systems (GPS) provide a viable means to continuously measure the absolute, bi-axial displacements of buildings of virtually any height and do so in real time, making them well suited for health monitoring applications (Kijewski-Correa, 2004). As such, its potential has been explored by a number of authors, beginning with the work of Celebi (Celebi and Sanli, 2004) and Tamura (Tamura et al., 2002) and followed by more recent applications by Brownjohn (2003) and the second author whose GPS unit has been operational for a number of years on a tall building in the Chicago Full-Scale Monitoring Program (Kijewski-Correa and Kareem, 2003). This latter application highlighted the potentials of GPS, while underscoring a number of issues associated with monitoring in dense urban environments, particularly the multipath effect, the largest untreatable error source present in GPS measurements. This paper looks at the removal of multipath from full-scale data sets recorded as part of the Chicago Full-Scale Monitoring Project (Phase I), comparing the corrected data sets to more traditional accelerometer * mkochly@nd.edu; phone 1 574 631 4307