Distance Measurements in UWB-Radio Localization Systems Corrected with a Feedforward Neural Network Model

Abstract

An ultra-wideband (UWB) localization system is an alternative in a GPS-denied environment. However, a distance measurement with UWB modules using a two-way communication protocol induces an orientation-dependent error. Previous research studied this error by looking at parameters such as the received power and the channel response signal. In this paper, the neural network (NN) method for correcting the orientation-induced distance error without the need to calculate the signal strength, obtain the channel response or know any parameters of the antenna and the UWB modules is presented. The NN method utilizes only the measured distance and the tag orientation, and implements an NN model obtained by machine learning, using measurements at different distances and orientations of the two UWB modules. The verification of the experimental setup with 12 anchors and a tag shows that with the proposed NN method, 5 cm better root mean square error values (RMSEs) are obtained for the measured distance between the anchors and the tag compared to the calibration method that did not include orientation information. With the least-square estimator, 14 cm RMSE in 3D is obtained with the NN model corrected distances, with a 9 cm improvement compared to when raw distances are used. The method produces better results without the need to obtain the UWB module’s diagnostics parameters that are required to calculate the received signal strength or channel response, and in this way maintain the minimum packet size for the ranging protocol.

Measurement of Orientation and Distance Change Using Circularly Polarized UWB Signals

“…Table III compares the proposed technique with other works that reported various rotation or distance change measurements. In [10], [11], [13] the precision is reported as the standard deviation, whereas the resolution declared by authors in quoted in [4], [21]. The 'range' column denotes the maximum distance over which the measurement was performed, as reported in respective publications.…”

“…Furthermore, due to the popular use of Ultra-Wide Band (UWB) signals for very precise localization [10]- [13], the localisation and orientation measurement could be combined into a single system, that provides full information about the tagged object. With the rapid development of low-cost UWB CP antenna technology, e.g.…”

Analysis and Improvement of Indoor Positioning Accuracy for UWB Sensors

“…The calculation of TOF is affected by the clock drift, frequency drift, fading, shadowing, multi-path propagation, etc. Although more delicate ranging methods such as the asymmetric double-sided two-way-ranging (TWR) method [ 39 ] or the TWR method integrated with neural network model [ 40 ] have been proposed, noise in the measurement of TOF is unavoidable. Let and be the ideal TOF and the measured TOF, respectively, and be the noise in TOF measurement.…”

An RSS Transform—Based WKNN for Indoor Positioning

“…However, the extensive deployment of wireless infrastructure and the proliferation of mobile devices have facilitated positioning in indoor scenes, and positioning based on received signal strength (RSS) has been an attractive solution [ 1 ]. Common wireless signals such as Bluetooth [ 2 ], Wi-Fi [ 3 ], ultra-wideband (UWB) [ 4 ], and radio frequency identification (RFID) [ 5 ] are often used for positioning. Positioning is also dependent on the existing position calculation algorithms, such as direct positioning, geometrical calculations, and fingerprint localization.…”