Fine-grained Indoor Localization Research Articles

Multistatic UWB Radar-Based Passive Human Tracking Using COTS Devices

Due to its high delay resolution, the ultra-wideband (UWB) technique has been widely adopted for fine-grained indoor localization. Instead of active positioning, UWB radar-based passive human tracking is explored using commercial off-the-shelf (COTS) devices. To extract the time-of-flight (ToF) reflected by the moving person, the accumulated channel impulse responses (CIR) and the corresponding variances are used to train the convolutional neural networks (CNN) model. Particle filter algorithm is adopted to track the moving person based on the extracted ToFs of all pairs of links. Experimental results show that the proposed CIR- and variance-based CNN models achieve less than 30-cm root-mean-square errors (RMSEs). Especially, the variance-based CNN model is robust to the scenario changing and promising for practical applications.

Toward Fine-Grained Indoor Localization Based on Massive MIMO-OFDM System: Experiment and Analysis

Fine-grained indoor localization has attracted attention recently because of the rapidly growing demand for indoor location-based services (ILBS). Specifically, massive (large-scale) multiple-input and multiple-output (MIMO) systems have received increasing attention due to high angular resolution. This paper presents an indoor localization testbed based on a massive MIMO orthogonal frequency-division multiplexing (OFDM) system, which supports physical-layer channel measurements. Instead of exploiting channel state information (CSI) directly for localization, we focus on positioning from the perspective of multipath components (MPCs), which are extracted from the CSI through the space-alternating generalized expectation-maximization (SAGE) algorithm. On top of the available MPCs, we propose a generalized fingerprinting system based on different single-metric and hybrid-metric schemes. We evaluate the impact of the varying antenna topologies, the size of the training set, the number of antennas, and the effective signal-to-noise ratio (SNR). The experimental results show that the proposed fingerprinting method can achieve centimeter-level positioning accuracy with a relatively small training set. Specifically, the distributed uniform linear array obtains the highest accuracy with about 1.63-2.5-cm mean absolute errors resulting from the high spatial resolution.

A new deep learning-based distance and position estimation model for range-based indoor localization systems

Many fine-grained indoor localization systems rely on accurate distance estimation between anchors and a target node to determine its exact position. The Received Signal Strength Indicator (RSSI) is commonly used for distance estimation because it is available in most low cost standard wireless devices. Despite the cost efficiency, the distance estimation accuracy in the RSSI-based ranging model needs to be enhanced, especially indoors. The RSSI is sensitive to multiple indoor factors that fluctuate in time and space and lead therefore to its variation. These factors are the origin of the distance estimation error increase in RSSI-based ranging models which in turn raise the position estimation error. Previous works have presented different in-site self-calibration processes to improve the accuracy of distance estimation using RSSI-to-distance samples. It permits to settle the parameters of the RSSI-based ranging model such as the Path Loss Model (PLM) and to mitigate the changing behavior of the RSSI. However, the RSSI measurement depends not only on the distance between the transmitter and the receiver but also on the indoor ambient temperature and humidity variations. Besides, indoor obstacles such as furniture, metallic surfaces or walls have also an impact on the RSSI measurements. We present in this paper a new RSSI-based indoor ranging model using deep learning on collected in-site samples to ensure efficient and autonomic calibration process. This permits to mitigate disturbing factors such as temperature, humidity and noise in order to increase the accuracy of both distance and position estimations. The experimental results have shown that our ranging model has improved not only the precision of distance estimation but also the position estimation in the range-based indoor localization systems.

Indoor Location Learning Over Wireless Fingerprinting System With Particle Markov Chain Model

This paper describes research toward a tracking system for locating persons indoor based on low-cost Bluetooth Low Energy (BLE) beacons. Wireless fingerprinting based on BLE beacons has emerged as an increasingly popular solution for fine-grained indoor localization. Inspired by the idea of mobility tracking used in the cellular network, this paper proposes a BLE-based tracking system, designated as BTrack, to learn the location area (LA) of an indoor user based on the reported wireless fingerprinting combined with statistical analysis. We propose a new particle Markov chain model to evaluate the LA-level performance regarding the visibility area in an environment with large obstacles. In the presence of sight obstructions, BTrack is evaluated using a real-world test bed built in a library with tall bookshelves. The performance of the proposed system is evaluated in terms of the mean distance error and the LA prediction accuracy considering the direct line-of-sight. Compared with the existing methods, BTrack reduces the average localization error by 25% and improves the average prediction accuracy by more than 16% given a random mobility pattern.

Light-emitting diodes aided indoor localization using visible light communication technology

This paper focuses on designing and analyzing a visible light-based communication and positioning system for indoor wireless communication under light-emitting diodes (LEDs) lighting environments. The indoor positioning system uses trichromatic white LEDs, both for illumination purposes and as transmitters, and an optical processor, based on a-SiC:H technology, as a mobile receiver. An on–off keying modulation scheme is used, proving a good trade-off between system performance and implementation complexity. The relationship between the transmitted data and the received output levels is decoded. LED bulbs work as transmitters, sending information together with different identifiers, IDs, related to their physical locations. Square and diamond topologies for the unit cell are analyzed, and a two-dimensional localization design, demonstrated by a prototype implementation, is presented. Fine-grained indoor localization is tested. The received signal is used in coded multiplexing techniques for supporting communications and navigation concomitantly on the same channel. The location and motion information are found by mapping the position and estimating the location areas.

Enhancing Localization Scalability and Accuracy via Opportunistic Sensing

Using a mobile phone for fine-grained indoor localization remains an open problem. Low-complexity approaches without infrastructure have not achieved accurate and reliable results due to various restrictions. Existing accurate solutions rely on dense anchor nodes for infrastructure and are therefore inconvenient and cumbersome. The problem of beacon signal blockage further reduces the effective coverage. In this paper, we investigate the problems associated with improving localization scalability and accuracy of a mobile phone via opportunistic anchor sensing, a new sensing paradigm which leverages opportunistically connected anchors. One key motivation is that the scalability of the infrastructure-based localization system can be improved by lifting the minimum requirement for anchor numbers or constellations in trilateration. At the same time, location accuracy under insufficient anchor coverage will be improved by exploring the opportunity of diverse data types rather than deploying more anchor nodes. To enable this highly scalable and accurate design, we leverage low-coupling hybrid ranging using our low-cost anchor nodes with centimeter-level relative distance estimation. Activity patterns extracted in users’ smartphones are utilized for displacement compensation and direction estimation. The system also scales to finer location resolution when anchor access is improved. We introduce robust delay-constraint semidefinite programming in location estimation to realize optimized system scalability and resolution flexibility. We conduct extensive experiments in various scenarios. Compared with existing approaches, opportunistic sensing could improve the location accuracy and scalability, as well as robustness, under various anchor accessibilities.

Adaptive Sampling of RF Fingerprints for Fine-Grained Indoor Localization

Indoor localization is a supporting technology for a broadening range of pervasive wireless applications. One promis- ing approach is to locate users with radio frequency fingerprints. However, its wide adoption in real-world systems is challenged by the time- and manpower-consuming site survey process, which builds a fingerprint database a priori for localization. To address this problem, we visualize the 3-D RF fingerprint data as a function of locations (x-y) and indices of access points (fingerprint), as a tensor and use tensor algebraic methods for an adaptive tubal-sampling of this fingerprint space. In particular using a recently proposed tensor algebraic framework in [1] we capture the complexity of the fingerprint space as a low-dimensional tensor-column space. In this formulation the proposed scheme exploits adaptivity to identify reference points which are highly informative for learning this low-dimensional space. Further, under certain incoherency conditions we prove that the proposed scheme achieves bounded recovery error and near-optimal sampling complexity. In contrast to several existing work that rely on random sampling, this paper shows that adaptivity in sampling can lead to significant improvements in localization accuracy. The approach is validated on both data generated by the ray-tracing indoor model which accounts for the floor plan and the impact of walls and the real world data. Simulation results show that, while maintaining the same localization accuracy of existing approaches, the amount of samples can be cut down by 71% for the high SNR case and 55% for the low SNR case.

Guoguo: Enabling Fine-Grained Smartphone Localization via Acoustic Anchors

Modern smartphones and location-based services and apps are poised to transform our daily life. However, current smartphone-based localization solutions are limited mainly to outdoor, mostly missing practical, robust and accurate indoor location solutions. Despite significant efforts on indoor localization in both academia and industry in the past two decades, highly accurate and practical smartphone-based indoor localization remains an open problem. To enable indoor location-based services (ILBS), e.g., step-by-step navigation for the Blind and visually impaired, there are several stringent requirements: highly accurate (foot-level); no additional hardware components or extensions on users’ smartphones; scalable to massive concurrent users. Current GPS, Radio RSS (e.g. Wi-Fi, Bluetooth, ZigBee), or Fingerprinting based solutions can only achieve meter-level or room-level accuracy. In this paper, we propose a practical and accurate solution that fills the long-lasting gap of smartphone-based fine-grained indoor localization. Specifically, we design and implement an indoor localization ecosystem Guoguo. Guoguo consists of an anchor network with a coordination protocol to transmit modulated localization beacons using high-band acoustic signals, a realtime processing app in a smartphone, and a backend server for indoor contexts and location-based services. We further propose approaches to improve its coverage, accuracy, and location update rate with low-power consumption. Our prototype shows centimeter-level localization accuracy in several typical indoor environments. Such precise indoor localization is expected to have high impact in the future ILBS and our daily activities.

Fine-Grained Indoor Localization Using Single Access Point With Multiple Antennas

We propose a single access point-based fine-grained indoor localization system in multipath environments using 802.11ac Wi-Fi signals. The physical layer of 802.11ac supports orthogonal frequency-division multiplexing modulation (OFDM) scheme, more antennas, and multiuser multiple-input multiple-output. Angle-of-arrival (AOA) of the paths can be obtained using the phase difference among all the antennas. Similarly, time of arrival (TOA) information is estimated from the phase difference among all the subcarriers of the OFDM signal. After obtaining the AOA and TOA information of the dominant paths, an auto paring approach is proposed to identify the line-of-sight (LOS) path. Mobile device can be localized using the estimated AOA and TOA information of the LOS path. Performance analysis of the proposed method is provided. Numerical simulations are carried out to evaluate the performance. Based on the simulation results, meter or submeter level accuracy is obtained for the proposed method.