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

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.

Self-calibration and Collaborative Localization for UWB Positioning Systems

“…This piece of information is normally retrieved by performing the so-called calibration procedure in which manual innervation is required to calculate the exact position of the fixed nodes. Several solutions [7,18,57,83] show that for an average office size (in the order of 6x4 m 2 ) with LOS conditions, four anchors should be sufficient to track hundreds of tags and therefore the installation process is usually smooth and cost effective. Once enough fixed nodes have been placed, the tags can estimate the distances to each of the anchors and calculate their own locations using algorithms such as multilateration.…”

Indoor Tracking by Adding IMU and UWB using Unscented Kalman Filter

“…But here the authors considered a two -dimensional system only. Demonstration of calculating the TOA values utilizing the two-way-ranging, positioning using the least squares (LS) and tracking with help of the EKF algorithm presented in [13].…”

Indoor Tracking by Adding IMU and UWB Using Unscented Kalman Filter

“…Then represents the process noise of velocity and represents the process noise of position, because of integrating acceleration two times. The velocity and acceleration values can be obtained from IMU.The equation of state at time with uniform acceleration motion is given in(12) (12) The matrix form of the state equation written as(13) Where represents the transition matrix of state, represents the driving matrix of noise. The process noise vector with zero mean and covariance matrix is given by And And Let the observation vector be , including the correct distance from reference sensor to target sensor and the observation noise…”