WUB-IP : a high-precision UWB positioning scheme for indoor multi-user applications

High-precision positioning scheme, an important part of the indoor navigation system, can be implemented using an ultra-wide band (UWB) based ranging system. Recently, solutions for precise positioning in dense multi-path and non-line-of-sight (NLOS) conditions have attracted a lot of attention in literature. On the other hand, it is expected that Waveform Division Multiple Access (WDMA) technology for multi-user UWB positioning application will be indispensable in the near future. In this regard, a WDMA-UWB based positioning scheme is investigated in this paper, for enhancing the performance of positioning accuracy in multi-user applications. In accordance with practical requirements of indoor positioning, we propose a new indoor positioning scheme, termed as WUB-IP. This scheme adopts WDMA for multiple access, and utilizes an entropy-based approach for the Time of Arrival (TOA) estimation. Moreover, a transfer learning approach is used for ranging error mitigation in NLOS conditions, in order to improve the positioning accuracy in NLOS conditions. System-level simulations demonstrate that the proposed scheme enhances the performance of indoor positioning for multi-user applications

Entropy-Based TOA Estimation and SVM-Based Ranging Error Mitigation in UWB Ranging Systems

The major challenges for Ultra-wide Band (UWB) indoor ranging systems are the dense multipath and non-line-of-sight (NLOS) problems of the indoor environment. To precisely estimate the time of arrival (TOA) of the first path (FP) in such a poor environment, a novel approach of entropy-based TOA estimation and support vector machine (SVM) regression-based ranging error mitigation is proposed in this paper. The proposed method can estimate the TOA precisely by measuring the randomness of the received signals and mitigate the ranging error without the recognition of the channel conditions. The entropy is used to measure the randomness of the received signals and the FP can be determined by the decision of the sample which is followed by a great entropy decrease. The SVM regression is employed to perform the ranging-error mitigation by the modeling of the regressor between the characteristics of received signals and the ranging error. The presented numerical simulation results show that the proposed approach achieves significant performance improvements in the CM1 to CM4 channels of the IEEE 802.15.4a standard, as compared to conventional approaches