Positioning Techniques in Indoor Environments Based on Stochastic Modeling of UWB Round-Trip-Time Measurements

In this paper, a technique for modeling propagation of ultrawideband (UWB) signals in indoor or outdoor environments is proposed, supporting the design of a positioning systems based on round-trip-time (RTT) measurements and on a particle filter. By assuming that nonlinear pulses are transmitted in an additive white Gaussian noise channel and are detected using a threshold-based receiver, it is shown that RTT measurements may be affected by non-Gaussian noise. RTT noise properties are analyzed, and the effects of non-Gaussian noise on the performance of an RTT-based positioning system are investigated. To this aim, a classical least-squares estimator, an extended Kalman filter, and a particle filter are compared when used to detect a slowly moving target in the presence of the modeled noise. It is shown that, in a realistic indoor environment, the particle filter solution may be a competitive solution, at a price of increased computational complexity. Experimental verifications validate the presented approach.