The 17th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’06) A STUDY OF THE EFFECTS OF REFERENCE POINT DENSITY ON TOA-BASED UWB INDOOR POSITIONING SYSTEMS Muzaffer Kanaan, Ferit Ozan Akg¨ ul, Bardia Alavi, Kaveh Pahlavan Center for Wireless Information Network Studies, Worcester Polytechnic Institute Worcester, MA, 01609, USA muzafferkanaan@gmail.com, {ferit, bardia, kaveh}@wpi.edu ABSTRACT The performance of Ultra-Wideband (UWB) indoor position- ing systems based on Time of Arrival (TOA) techniques is generally affected by the density of reference points (RPs), as well as undetected direct path (UDP) conditions. For a fixed number of reference points (RPs), the performance of some indoor positioning algorithms tends to degrade as the size of the area is increased, i.e. the RP density is decreased. In this paper, we evaluate the effects of RP density on the performance of different positioning algorithms in the pres- ence of empirical distance measurement error (DME) models derived from UWB measurements in typical indoor environ- ments. We then present functional relationships between RP density and positioning mean-square error (MSE) for these algorithms. These relationships can be used for more effec- tive indoor positioning system design and deployment. Fi- nally, we investigate the effects of bandwidth with respect to improving the performance of these algorithms. I. I NTRODUCTION Recent advances in accurate location estimation for the in- door setting are paving the way for wireless location-aware networking [1]. Unlike the outdoor case, the indoor envi- ronment introduces unique challenges which require differ- ent approaches to the positioning problem. The popular GPS cannot be used for accurate indoor positioning since satellite signals will be greatly deteriorated, making indoor position- ing either impossible or very inaccurate. Figure 1 shows the high-level architecture of an indoor po- sitioning system. There are three main metrics that are used to estimate location: Time-of-Arrival (TOA) [2], Angle-of- Arrival (AOA) [3], and Received Signal Strength (RSS) [4]. TOA has attracted a lot of attention recently due to the inter- est in Ultra-Wideband (UWB) systems, whose signal format is ideally suited for accurate TOA measurements in an indoor environment [5]. For the rest of the paper, we will focus on TOA-based UWB systems. Apart from the inherent stochastic variations of the chan- nel (which can induce distance measurement error, or DME), the indoor environment itself also does not necessarily stay static. Indoor areas can be remodeled, made larger, or por- tions of it can be rebuilt with different building materials. This will change the RP density and by extension, the esti- mation accuracy that we can obtain from the network used for positioning. The RP density, denoted by ρ, can be viewed as a measure of the number of RPs per unit area, and is de- fined as Location metrics (AOA, TOA, RSS etc.) Reference Point (RP) #1 Reference Point (RP) #2 Reference Point (RP) #N Positioning Algorithm Higher-layer applications (e.g. display system) Location estimate Received RF Signal Figure 1: High-level architecture of an indoor positioning system ρ = N/A (1) where N is the number of RPs covering a given indoor area, and A is the size of the area, generally given in m 2 . We have noted in our prior work [6] that given a fixed number of RPs, the performance of certain positioning algo- rithms tends to degrade as the size of the area to be covered is increased (i.e the RP density is decreased). This obser- vation makes intuitive sense since the DP will be attenuated more as the distance between the RP and the sensor is in- creased. This will give rise to more distance measurement error (DME) which, in turn, will lead to degraded location estimation performance. Although the effects of RP density on location estimation accuracy has been known, the exact nature of the functional relationship between these two quantities has not, to the best of our knowledge, been formulated to date. This raises a valid question: why is it important to characterize this relationship? The answer fundamentally lies in the fact that different indoor positioning applications have different requirements for esti- mation accuracy. For example, in a commercial application (such as inventory tracking in a warehouse), low accuracy might be acceptable. However, in a public-safety or military application (such as keeping track of the locations of fire- fighters or soldiers within a building), much higher accuracy would be needed. This implies that the RP densities required for these two application domains would be different. Knowl- edge of the functional relationship between RP density and estimation accuracy enables a system designer to figure out how many RPs are required to meet a given accuracy target, thereby results in a cost-effective network deployment. 1-4244-0330-8/06/$20.00 c 2006 IEEE