Indoor wireless communications and applications

Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter

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

A survey of localization in wireless sensor network

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network

Detection of UWB ranging measurement quality for collaborative indoor positioning

Wireless communication signals have become popular alternatives for indoor positioning and navigation due to lack of navigation satellite signals in such environments. The signal characteristics determine the method used for positioning as well as the positioning accuracy. Ultra-wideband (UWB) signals, with a typical bandwidth of over 1 GHz, overcome multipath problems in complicated environments. Hence, potentially achieves centimetre-level ranging accuracy in open areas. However, signals can be disrupted when placed in environments with obstructions and cause large ranging errors. This paper proposes a ranging measurement quality indicator (RQI) which detects the UWB measurement quality based on the received signal strength pattern. With a detection validity of more than 83%, the RQI is then implemented in a ranging-based collaborative positioning system. The relative constraint of the collaborative network is adjusted adaptively according to the detected RQI. The proposed detection and positioning algorithm improves positioning accuracy by 80% compared to non-adaptive collaborative positioning