A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

A two phase framework for visible light-based positioning in an indoor environment: performance, latency, and illumination

Recently with the advancement of solid state lighting and the application thereof to Visible Light Communications (VLC), the concept of Visible Light Positioning (VLP) has been targeted as a very attractive indoor positioning system (IPS) due to its ubiquity, directionality, spatial reuse, and relatively high modulation bandwidth. IPSs, in general, have 4 major components (1) a modulation, (2) a multiple access scheme, (3) a channel measurement, and (4) a positioning algorithm. A number of VLP approaches have been proposed in the literature and primarily focus on a fixed combination of these elements and moreover evaluate the quality of the contribution often by accuracy or precision alone. In this dissertation, we provide a novel two-phase indoor positioning algorithmic framework that is able to increase robustness when subject to insufficient anchor luminaries and also incorporate any combination of the four major IPS components. The first phase provides robust and timely albeit less accurate positioning proximity estimates without requiring more than a single luminary anchor using time division access to On Off Keying (OOK) modulated signals while the second phase provides a more accurate, conventional, positioning estimate approach using a novel geometric constrained triangulation algorithm based on angle of arrival (AoA) measurements. However, this approach is still an application of a specific combination of IPS components. To achieve a broader impact, the framework is employed on a collection of IPS component combinations ranging from (1) pulsed modulations to multicarrier modulations, (2) time, frequency, and code division multiple access, (3) received signal strength (RSS), time of flight (ToF), and AoA, as well as (4) trilateration and triangulation positioning algorithms. Results illustrate full room positioning coverage ranging with median accuracies ranging from 3.09 cm to 12.07 cm at 50% duty cycle illumination levels. The framework further allows for duty cycle variation to include dimming modulations and results range from 3.62 cm to 13.15 cm at 20% duty cycle while 2.06 cm to 8.44 cm at a 78% duty cycle. Testbed results reinforce this frameworks applicability. Lastly, a novel latency constrained optimization algorithm can be overlaid on the two phase framework to decide when to simply use the coarse estimate or when to expend more computational resources on a potentially more accurate fine estimate. The creation of the two phase framework enables robust, illumination, latency sensitive positioning with the ability to be applied within a vast array of system deployment constraints

Design, analysis and optimization of visible light communications based indoor access systems for mobile and internet of things applications

Demands for indoor broadband wireless access services are expected to outstrip the spectrum capacity in the near-term spectrum crunch . Deploying additional femtocells to address spectrum crunch is cost-inefficient due to the backhaul challenge and the exorbitant system maintenance. According to an Alcatel-Lucent report, most mobile Internet access traffic happens indoors. To alleviate the spectrum crunch and the backhaul challenge problems, visible light communication (VLC) emerges as an attractive candidate for indoor wireless access in the 5G architecture. In particular, VLC utilizes LED or fluorescent lamps to send out imperceptible flickering light that can be captured by a smart phone camera or photodetector. Leveraging power line communication and the available indoor infrastructure, VLC can be utilized with a small one-time cost. VLC also facilitates the great advantage of being able to jointly perform illumination and communications. Integration of VLC into the existing indoor wireless access networks embraces many challenges, such as lack of uplink infrastructure, excessive delay caused by blockage in heterogeneous networks, and overhead of power consumption. In addition, applying VLC to Internet-of-Things (IoT) applications, such as communication and localization, faces the challenges including ultra-low power requirement, limited modulation bandwidth, and heavy computation and sensing at the device end. In this dissertation, to overcome the challenges of VLC, a VLC enhanced WiFi system is designed by incorporating VLC downlink and WiFi uplink to connect mobile devices to the Internet. To further enhance robustness and throughput, WiFi and VLC are aggregated in parallel by leveraging the bonding technique in Linux operating system. Based on dynamic resource allocation, the delay performance of heterogeneous RF-VLC network is analyzed and evaluated for two different configurations - aggregation and non-aggregation. To mitigate the power consumption overhead of VLC, a problem of minimizing the total power consumption of a general multi-user VLC indoor network while satisfying users traffic demands and maintaining an acceptable level of illumination is formulated. The optimization problem is solved by the efficient column generation algorithm. With ultra-low power consumption, VLC backscatter harvests energy from indoor light sources and transmits optical signals by modulating the reflected light from a reflector. A novel pixelated VLC backscatter is proposed and prototyped to address the limited modulation bandwidth by enabling more advanced modulation scheme than the state-of-the-art on-off keying (OOK) scheme and allowing for the first time orthogonal multiple access. VLC-based indoor access system is also suitable for indoor localization due to its unique properties, such as utilization of existing ubiquitous lighting infrastructure, high location and orientation accuracy, and no interruption to RF-based devices. A novel retroreflector-based visible light localization system is proposed and prototyped to establish an almost zero-delay backward channel using a retroreflector to reflect light back to its source. This system can localize passive IoT devices without requiring computation and heavy sensing (e.g., camera) at the device end