Fingerprinting localization based on neural networks and ultra-wideband signals

Abstract

International audienceFingerprinting techniques have been proved as an effective techniques for determining the position of a mobile user in an indoor environment and in challenging environments such as mines, canyons, and tunnels where common localization techniques based on time of arrival (TOA) or received signal strength (RSS) are subject to big positioning errors. In this paper, a fingerprinting based localization technique using neural networks and ultra-wideband signals (UWB) is presented as an alternative. The fingerprinting database is built with signatures extracted from channel impulse responses (CIR) obtained by processing an IR-UWB indoor propagation measurement campaign. The construction of the neural networks and the adopted approach are described. Positioning performances are evaluated with different selected signatures and different sizes of the fingerprinting database

Performance Comparison of WiFi and UWB Fingerprinting Indoor Positioning Systems

“…The results show that UWB-combo outperforms WiFi-opt in all density scenarios, always showing a lower median than WiFi; UWB-combo and WiFi-opt present some outliers in the MD and HD scenarios, respectively, ranging however on tight intervals close to the median error. Interestingly, the results show that when the two technologies are compared under fair conditions, the performance advantage of UWB, although still significant, is not as wide as the results in [12][13][14] might suggest.…”

“…The performance analysis has typically focused on evaluating the probability of the correct identification of the region in which the Tx is located. A different approach is proposed in [12][13][14]: several CIR features are considered as possible fingerprints, mainly in conjunction with a neural network-based estimation phase. Among others, two specific CIR features have emerged as preferred choices: CIR energy and RMS-DS.…”

“…Previous work thus clearly identifies energy and RMS-DS as the most suitable options to represent an UWB CIR; as a result, these were adopted in this work, as detailed in Section 4. In analogy with WiFi, the performance analysis in [12][13][14] was carried out in terms of TN position estimation rather than region identification; however, no performance comparison with WiFi was provided and, as compared to the typical WiFi deployment in the offline phase, much more extensive CIR measurement campaigns were carried out.…”

“…It is demonstrated that these schemes are highly accurate when line-of-sight (LoS) and perfect time synchronization conditions are verified between ANs and the TN; however, several factors, such as (a) direct path excess-delay/blockage due to non-LoS (NLoS) propagation; (b) clock drifts due to asynchronism; and (c) interference from other users/networks, lead to a significant decrease in accuracy, as a result of their negative impact on the ToA estimation [6,7]. In order to cope with the decrease in accuracy of lateration techniques, the application of the fingerprinting approach has been recently proposed for UWB [8][9][10][11][12][13][14]; in general, fingerprinting relies on two phases: the first, referred to as the offline phase, is a collection of data related to the signal propagation (fingerprints) and with respect to predefined reference node (RN) positions in the area of interest. During the second phase (online phase), the TN position is estimated through the application of pattern-matching techniques between RNs and TN fingerprints.…”

“…Differently from [12,13], the work is based on simulation, allowing us to derive an average performance comparison not depending on a single AN/RN topology, as a result of the randomization of topologies carried out, within the area, through of the adoption of Poisson point processes (PPPs).…”