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

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

Robust Positioning in the Presence of Multipath and NLOS GNSS Signals

GNSS signals can be blocked and reflected by nearby objects, such as buildings, walls, and vehicles. They can also be reflected by the ground and by water. These effects are the dominant source of GNSS positioning errors in dense urban environments, though they can have an impact almost anywhere. Non- line-of-sight (NLOS) reception occurs when the direct path from the transmitter to the receiver is blocked and signals are received only via a reflected path. Multipath interference occurs, as the name suggests, when a signal is received via multiple paths. This can be via the direct path and one or more reflected paths, or it can be via multiple reflected paths. As their error characteristics are different, NLOS and multipath interference typically require different mitigation techniques, though some techniques are applicable to both. Antenna design and advanced receiver signal processing techniques can substantially reduce multipath errors. Unless an antenna array is used, NLOS reception has to be detected using the receiver's ranging and carrier-power-to-noise-density ratio (C/N0) measurements and mitigated within the positioning algorithm. Some NLOS mitigation techniques can also be used to combat severe multipath interference. Multipath interference, but not NLOS reception, can also be mitigated by comparing or combining code and carrier measurements, comparing ranging and C/N0 measurements from signals on different frequencies, and analyzing the time evolution of the ranging and C/N0 measurements

Adaptive AOA-Aided TOA Self-Positioning for Mobile Wireless Sensor Networks

Location-awareness is crucial and becoming increasingly important to many applications in wireless sensor networks. This paper presents a network-based positioning system and outlines recent work in which we have developed an efficient principled approach to localize a mobile sensor using time of arrival (TOA) and angle of arrival (AOA) information employing multiple seeds in the line-of-sight scenario. By receiving the periodic broadcasts from the seeds, the mobile target sensors can obtain adequate observations and localize themselves automatically. The proposed positioning scheme performs location estimation in three phases: (I) AOA-aided TOA measurement, (II) Geometrical positioning with particle filter, and (III) Adaptive fuzzy control. Based on the distance measurements and the initial position estimate, adaptive fuzzy control scheme is applied to solve the localization adjustment problem. The simulations show that the proposed approach provides adaptive flexibility and robust improvement in position estimation

A Study of Environment Noise in Ultra-Wideband Indoor Position Tracking

This work is motivated by the problem of improving the accuracy of indoor ultra-wideband (UWB) position tracking through the study of the environment noise that affects such a system. Current systems can provide accuracy in the range of 30-100 cm in a small building, suitable for applications that require rough room-level precision such as asset tracking and surveillance. Our long-term goal is to improve the accuracy to 1 cm or better, expanding potential applications to telepresence, augmented reality, training and entertainment. This work investigates the possibility of systematically observing the measurement noise of an UWB position tracking system and building a map of it throughout a facility. In order to understand the effect of environment noise on UWB indoor positioning and in turn filter out the effects of this noise, it is important to have an idea of what this measurement noise looks like in a real world scenario. In this work, an understanding of the measurement noise is gained by taking many measurements using a commercially-available UWB positioning system installed in a real world scenario and analyzing these measurements in various ways. To the author\u27s knowledge, no one has used such an exhaustive approach to analyze measurement noise in UWB indoor positioning. The results of this work show that the measurement noise that affects a UWB indoor position tracking system can be effectively modeled using a weighted sum of Gaussians, is stable over time and is locally similar. Furthermore, a particle filter augmented with a measurement noise map is proposed to improve position tracking accuracy. Finally, a metric is proposed that can be used to quantify expected system performance based on sensor location, sensor orientation and facility floorplan. Using this metric, a procedure is developed to determine the parameters, i.e. sensor position, sensor orientation and potentially others, of the physical installation of the UWB tracking system that will produce minimum measurement error based on sensor geometry and physical facility constraints

Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems

[Abstract] Indoor positioning systems based on radio frequency inherently present multipath-related phenomena. This causes ranging systems such as ultra-wideband (UWB) to lose accuracy when detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will face critical errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques applied to a previous classification and mitigation of the propagation effects. For this purpose, real-world cross-scenarios are considered, where the data extracted from low-cost UWB devices for training the algorithms come from a scenario different from that considered for the test. The experimental results reveal that machine learning (ML) techniques are suitable for detecting non-line-of-sight (NLOS) ranging values in this situation.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

Outlier Detection for 3D-Mapping-Aided GNSS Positioning

This paper takes 3D-mapping-aided (3DMA) GNSS as an example and investigates the outlier detection for pattern matching based positioning. Three different test statistics, two in the measurement domain and one in the position domain, are presented. Two 3D city maps with different levels of detail were used, one of which contained two obvious errors, to demonstrate the performance of 3DMA GNSS positioning in the presence of errors in the mapping data. The experiments tested were conducted alongside busy roads in the London Borough of Camden, where a total of 8 sets of 2-minute static pedestrian navigation data were collected with a u-blox EVK M8T GNSS receiver. The results confirm that both 3D mapping errors and temporary environmental changes (such as passing vehicles) can have a significant negative impact on the performance of 3DMA GNSS positioning. After applying outlier detection, single-epoch 3DMA GNSS algorithm reduces the horizontal RMS position error by approximately 15% compared to that without outlier detection. The filtering algorithm attenuates the effects of temporary environmental changes, providing an improvement of about 15% over single-epoch positioning, while the outlier algorithm further reduces the RMS error to a comparable level to that of using high-accuracy maps, about 4.7m