Evaluation of range-based indoor tracking algorithms by merging simulation and measurements

Abstract

Abstract Precise location information will play an important role in 5G networks, their applications and services, especially in indoor environments. Ultra-wideband (UWB) technology offers exceptional temporal resolution enabling the emergence of high accuracy ranging-based indoor localization systems. In order to reduce time to market, developers need a reliable, fast and efficient method to evaluate localization and tracking system designs in the selected high-dimensional parameter space. Purely measurement-based performance evaluation of such systems is costly and cumbersome, so we propose the use of a radio frequency (RF) ray-tracing simulator augmented with a noise model based on measurements with localization equipment in real environment. We demonstrate the proposed approach by evaluating the UWB-based two-way-ranging (TWR) localization with the least squares (LS) and with the extended Kalman filter (EKF) tracking algorithms. We analyze the degradation of tracking performance due to time spreading of range measurements. Moreover, on a set of fixed locations we also show that the proposed approach is sufficiently representative for the pure measurement-based evaluation. The results obtained in a reference office environment show that EKF algorithm is twice as efficient and more resilient to the effects of time spreading of range measurements as LS algorithm.

Keywords

• Indoor position tracking
• Ultra-wideband
• RF ray-tracing
• Tracking algorithm evaluation