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Realistic simulation of Ultra-Wideband (UWB) positioning and tracking is a tough and challenging task. Lots of parameters have a significant impact on the final performance of the positioning system, such as parameters of the transmitted waveform, radio regulations, channel, receiver, ranging-, positioning-, and tracking-algorithm, and finally the geometric setup. A realistic simulation framework is needed to develop and optimize UWB methods and algorithms. This work proposes a novel framework for realistic UWB positioning simulations. All stages of influence have been modelled carefully. While ray-tracing simulators focus on a given user defined scenario, our approach uses statistically defined environments. Random processes are used to select channel impulse responses from a measurement database, according to an algorithm that introduces realistic large and small-scale variability with space. In particular, line-of-sight (LOS) and non-LOS (NLOS) channels are used at a defined ratio. In this work the application of the simulation framework is demonstrated to compare the performance of a least-squares (LS) positioning algorithm to a standard and an extended Kalman filter (SKF and EKF) tracking algorithm in environments with a large fraction of NLOS range measurements. The IEEE 802.15.4a standard is analyzed with respect to its positioning performance. It is shown that the localization accuracy and reliability can be improved significantly with the tracking algorithms, but their performance drop significantly with increasing probability of NLOS links.

We present a novel UWB indoor localization concept that performs the position estimation with a set of virtual anchor nodes, generated from a single physical anchor and floor plan information. Using range estimates to the virtual anchors, we perform multilateration to estimate the position of an agent. Previous work has shown the general applicability of this concept. In this contribution, we use a moving agent to exploit the correlation in successive positions using state-space concepts. A motion model for the agent and the measurement likelihood function allow for the use of the powerful framework of Bayesian state estimation. With this concept, we can propagate prior information on the agent position from one time step to the next. The statistical model for the ranging to the virtual anchors accounts for several imperfections, which lead to multimodal and heavy-tailed measurement distributions. We show how modified versions of the Kalman filter as well as a particle filter can account for these imperfections and yield accurate and robust position estimates. In a typical indoor pedestrian motion scenario, we can achieve an accuracy of about 45 cm for 90% of the estimates.