We present a hybrid signaling approach to position energy-neutral devices. Our method combines the best of two worlds: instant RF signals for both communication and energy transfer, and slower propagating acoustic waves for accurate distance measurements. We introduce advanced energy loading of an 'E-buffer' through beamformed RF signals. The scientific contribution of our approach is twofold, advancing both the positioning performance and the energy harvesting efficiency. On the one hand, we overcome current distance limitations in RF backscattering-based indoor localization. On the other hand, it enhances the energy harvesting by using the calculated position and beamforming a higher amount of directed RF energy into the mobile node. We provide a functional architecture for both sides of the system and present a proof-of-concept. Practical measurements in representative use cases show an update rate of 10 positions per hour within the regulatory constraints in the 868MHz band for distances up to 4.5 m. An energy and power model are drawn up to provide insights into the performance trade-offs.
The received signal strength (RSS) based technique is extensively utilized for localization in the indoor environments. Since the RSS values of neighboring locations may be similar, the localization accuracy of the RSS based technique is limited. To tackle this problem, in this paper, we propose to utilize reconfigurable intelligent surface (RIS) for the RSS based multi-user localization. As the RIS is able to customize the radio channels by adjusting the phase shifts of the signals reflected at the surface, the localization accuracy in the RIS aided scheme can be improved by choosing the proper phase shifts with significant differences of RSS values among adjacent locations. However, it is challenging to select the optimal phase shifts because the decision function for location estimation and the phase shifts are coupled. To tackle this challenge, we formulate the optimization problem for the RIS-aided localization, derive the optimal decision function, and design the phase shift optimization (PSO) algorithm to solve the formulated problem efficiently. Analysis of the proposed RIS aided technique is provided, and the effectiveness is validated through simulation.
We propose an indoor localization algorithm for visible light systems by considering effects of non-line-of-sight (NLOS) propagation. The proposed algorithm, named database assisted nonlinear least squares (DA-NLS), utilizes ideas from both the classical NLS algorithm and the fingerprinting algorithm to achieve accurate and robust localization performance in NLOS environments. In particular, a database is used to learn NLOS effects, and then an NLS algorithm is employed to estimate the position. The performance of the proposed algorithm is compared against that of the fingerprinting and NLS algorithms.
In this paper, we propose a novel visible light communication (VLC) assisted Perspective-fourLine algorithm (V-P4L) for practical indoor localization. The basic idea of V-P4L is to joint VLC and computer vision to achieve high accuracy regardless of LED height differences. In particular, we first exploit the space-domain information to estimate the orientation and coordinate of a single rectangular LED luminaire in the camera coordinate system based on the plane and solid geometry. Then, based on the time-domain information transmitted by VLC and the estimated luminaire information, V-P4L can estimate the position and pose of the camera by the single-view geometry theory and the linear least square (LLS) method. To further mitigate the effect of height differences among LEDs on localization accuracy, we then propose a correction algorithm of V-P4L based on the LLS method and a simple optimization method. Due to the combination of time- and space-domain information, V-P4L only requires a single luminaire for localization without limitation on the correspondences between the features and their projections. Simulation results show that for V-P4L the position error is always less than 15 cm and the orientation error is always less than 3{\deg} using popular indoor luminaires.
A neural network (NN)-based approach for indoor localization via cellular long-term evolution (LTE) signals is proposed. The approach estimates, from the channel impulse response (CIR), the range between an LTE eNodeB and a receiver. A software-defined radio (SDR) extracts the CIR, which is fed to a long short-term memory model (LSTM) recurrent neural network (RNN) to estimate the range. Experimental results are presented comparing the proposed approach against a baseline RNN without LSTM. The results show a receiver navigating for 100 m in an indoor environment, while receiving signals from one LTE eNodeB. The ranging root-mean squared error (RMSE) and ranging maximum error along the receiver's trajectory were reduced from 13.11 m and 55.68 m, respectively, in the baseline RNN to 9.02 m and 27.40 m, respectively, with the proposed RNN-LSTM.
Radio channel state information (CSI) measured with many receivers is a good resource for localizing a transmit device with machine learning with a discriminative model. However, CSI localization is nontrivial when the radio map is complicated, such as in building corridors. This paper introduces a view-selective deep learning (VSDL) system for indoor localization using CSI of WiFi. The multiview training with CSI obtained from multiple groups of access points (APs) generates latent features on supervised variational deep network. This information is then applied to an additional network for dominant view classification to enhance the regression accuracy of localization. As non-informative latent features from multiple views are rejected, we can achieve a localization accuracy of 0.77 m, which outperforms by 30 $\%$ the best known accuracy in practical applications in a real building environment. To the best of our knowledge, this is the first approach to apply variational inference and to construct a scalable system for radio localization. Furthermore, our work investigates a methodology for supervised learning with multiview data where informative and non-informative views coexist.
The existing localization systems for indoor applications basically rely on wireless signal. With the massive deployment of low-cost cameras, the visual image based localization become attractive as well. However, in the existing literature, the hybrid visual and wireless approaches simply combine the above schemes in a straight forward manner, and fail to explore the interactions between them. In this paper, we propose a joint visual and wireless signal feature based approach for high-precision indoor localization system. In this joint scheme, WiFi signals are utilized to compute the coarse area with likelihood probability and visual images are used to fine-tune the localization result. Based on the numerical results, we show that the proposed scheme can achieve 0.62m localization accuracy with near real-time running time.
Recent years have witnessed the rapid proliferation of backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes. Localizing such backscatter tags is crucial for IoT-based smart applications. However, current backscatter localization systems require prior knowledge of the site, either a map or landmarks with known positions, which is laborious for deployment. To empower universal localization service, this paper presents Rover, an indoor localization system that localizes multiple backscatter tags without any start-up cost using a robot equipped with inertial sensors. Rover runs in a joint optimization framework, fusing measurements from backscattered WiFi signals and inertial sensors to simultaneously estimate the locations of both the robot and the connected tags. Our design addresses practical issues including interference among multiple tags, real-time processing, as well as the data marginalization problem in dealing with degenerated motions. We prototype Rover using off-the-shelf WiFi chips and customized backscatter tags. Our experiments show that Rover achieves localization accuracies of 39.3 cm for the robot and 74.6 cm for the tags.
In recent years, the Internet of Things (IoT) has grown to include the tracking of devices through the use of Indoor Positioning Systems (IPS) and Location Based Services (LBS). When designing an IPS, a popular approach involves using wireless networks to calculate the approximate location of the target from devices with predetermined positions. In many smart building applications, LBS are necessary for efficient workspaces to be developed. In this paper, we examine two memoryless positioning techniques, K-Nearest Neighbor (KNN), and Naive Bayes, and compare them with simple trilateration, in terms of accuracy, precision, and complexity. We present a comprehensive analysis between the techniques through the use of three popular IoT wireless technologies: Zigbee, Bluetooth Low Energy (BLE), and WiFi (2.4 GHz band), along with three experimental scenarios to verify results across multiple environments. According to experimental results, KNN is the most accurate localization technique as well as the most precise. The RSSI dataset of all the experiments is available online.
WiFi-based indoor localization has received extensive attentions from both academia and industry. However, the overhead of constructing and maintaining the WiFi fingerprint map remains a bottleneck for the wide-deployment of WiFi-based indoor localization systems. Recently, robots are adopted as the professional surveyor to fingerprint the environment autonomously. But the time and energy cost still limit the coverage of the robot surveyor, thus reduce its scalability. To fill this need, we design an AutonomousWiFi Fingerprinting system, called AuF, which autonomously constructs the fingerprint database with time and energy efficiency. AuF first conduct an automatic initialization process in the target indoor environment, then constructs the WiFi fingerprint database of in two steps: (i) surveying the site without sojourn, (ii) recovering unreliable signals in the database with two methods. We have implemented and evaluated AuF using a Pioneer 3-DX robot, on two sites of our $70$$\times$$90$m$^2$ Department building with different structures and deployments of access points (APs). The results show AuF finishes the fingerprint database construction in 43/51 minutes, and consumes 60/82 Wh on the two floors respectively, which is a 64%/71% and 61%/64% reduction when compared to traditional site survey methods, without degrading the localization accuracy.
Precise indoor localization is an increasingly demanding requirement for various emerging applications, like Virtual/Augmented reality and personalized advertising. Current indoor environments are equipped with pluralities of WiFi access points (APs), whose deployment is expected to be massive in the future enabling highly precise localization approaches. Though the conventional model-based localization schemes have achieved sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is usually significant, especially in the current multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In order to address this issue, model-free localization techniques using deep learning frameworks have been lately proposed, where mainly classification methods were applied. In this paper, instead of classification based mechanism, we propose a logistic regression based scheme with the deep learning framework, combined with Cram\'er-Rao lower bound (CRLB) assisted robust training, which achieves more robust sub-meter level accuracy (0.97m median distance error) in the standard laboratory environment and maintains reasonable online prediction overhead under the single WiFi AP settings.
In this paper, we propose a joint indoor localization and navigation algorithm to enable a swarm of unmanned aerial vehicles (UAVs) to deploy in a specific spatial formation in indoor environments. In the envisioned scenario, we consider a static user acting as a central unit whose main task is to acquire all the UAV measurements carrying position-dependent information and to estimate the UAV positions when there is no existing infrastructure for positioning. Subsequently, the user exploits the estimated positions as inputs for the navigation control with the aim of deploying the UAVs in a desired formation in space (formation shaping). The user plans the trajectory of each UAV in real time, guaranteeing a safe navigation in the presence of obstacles. The proposed algorithm guides the UAVs to their desired final locations with good accuracy.
This paper proposes a soft range limited K nearest neighbours (SRL-KNN) localization fingerprinting algorithm. The conventional KNN determines the neighbours of a user by calculating and ranking the fingerprint distance measured at the unknown user location and the reference locations in the database. Different from that method, SRL-KNN scales the fingerprint distance by a range factor related to the physical distance between the user's previous position and the reference location in the database to reduce the spatial ambiguity in localization. Although utilizing the prior locations, SRL-KNN does not require knowledge of the exact moving speed and direction of the user. Moreover, to take into account of the temporal fluctuations of the received signal strength indicator (RSSI), RSSI histogram is incorporated into the distance calculation. Actual on-site experiments demonstrate that the new algorithm achieves an average localization error of $0.66$ m with $80\%$ of the errors under $0.89$ m, which outperforms conventional KNN algorithms by $45\%$ under the same test environment.
Indoor localization systems based on Visible Light Communication (VLC) have shown promising advantages compared with systems based on other wireless technologies. In these systems, many VLC light-emitting diode (LED) anchors are employed in an indoor space in which location identification messages are sent to user devices in small packets. In normal beacon network models, micro-controller (MCU) or low-end system-on-chip (SoC) are often the coordinators which configure messages for one or many VLC-LED bulbs. In this paper, we discuss about processing overload and implementation cost of the two typical models of VLC beacon network in scenarios of a hundred of VLC-LED anchors are taken into account. Finally, an FPGA-based centralized VLC transmitter and its aided Nios II-based system has been introduced to enhance the performance of the VLC beacon network. Besides, due to the centralized processing, our system model is considered to be more cost-efficient than the dedicated-processor-based models
Indoor localization becomes a raising demand in our daily lives. Due to the massive deployment in the indoor environment nowadays, WiFi systems have been applied to high accurate localization recently. Although the traditional model based localization scheme can achieve sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is significant. To address this issue, the model-free localization approach using deep learning framework has been proposed and the classification based technique is applied. In this paper, instead of using classification based mechanism, we propose to use a logistic regression based scheme under the deep learning framework, which is able to achieve sub-meter level accuracy (97.2cm medium distance error) in the standard laboratory environment and maintain reasonable online prediction overhead under the single WiFi AP settings. We hope the proposed logistic regression based scheme can shed some light on the model-free localization technique and pave the way for the practical deployment of deep learning based WiFi localization systems.
Visible light communication (VLC) based localization is a potential candidate for wide range indoor localization applications. In this paper, we propose a VLC architecture based on orthogonal frequency division multiplexing (OFDM) with multiple functionalities integrated in the same system, i.e., the 3- D receiver location, the control of the room illumination intensity, as well as the data transmission capability. Herein we propose an original methodology for LED power discrimination applying spatial optical OFDM (SO-OFDM) structure for position estimation. The hybrid locator initially makes a first estimate using a weighted angle-of-arrival (WAoA)-based locator which is then used as the starting point of the recursive estimator based on the strength of the received signal (RSS). Hence, the first stage is deployed to increase convergence probability, reducing the root-mean-square error (RMSE) and the number of iterations of the second stage. Also, a performance vs computational complexity comparative analysis is carried out with parameter variations of these estimators. The numerical results indicate a decade improvement in the RMSE for each two decades of decrement of power noise on the receiver photodiode. The best clipping factor is obtained through the analysis of locator accuracy and transmission capacity for each simulated system. Finally, the numerical results also demonstrate effectiveness, robustness, and efficiency of the proposed architecture.
Recently, a camera or an image sensor receiver based optical wireless communications (OWC) techniques have attracted particular interest in areas such as the internet of things, indoor localization, motion capture, and intelligent transportation systems. As a supplementary technique of high-speed OWC based on photo-detectors, communications hinging on image sensors as receivers do not need much modification to the current infrastructure, such that the implementation complexity and cost are quite low. Therefore, in this paper, we present a comprehensive survey of optical camera communication (OCC) techniques, and their use in localization, navigation, and motion capture. This survey is distinguishable from the existing reviews on this topic by covering multiple aspects of OCC and its various applications. The first part of the paper focuses on the standardization, channel characterization, modulation, coding, and synchronization of OCC systems while the second part of the article presents the literature on OCC based localization, navigation, and motion capture. Finally, in the last part of the paper, we present the challenges and future research directions of OCC.
In this paper, we propose an accurate visible light indoor localization system for a smartphone using the existing commercial light-emitting diode (LED) luminaries. The proposed technique, called geometrical optics positioning algorithm (GOPA), uses spatial color code landmarks alongside angle of arrival (AOA)-based geometrical algorithm on smartphones to locate the device, and reserves LED's time-frequency domain modulation to increase the throughput of the visible light network broadcast from the same luminaries infrastructure. GOPA algorithm is developed with practical considerations such as flexible hand gesture and handshake, and it enables both positioning robustness and on-device processing. By introducing the idea of virtual plane, field of view (FOV) issue of AOA-based positioning systems is addressed in GOPA. The embedded accelerometer and front-facing camera of the smartphone are used at the receiver side to measure the smartphone inclination and acquire the image. Experimental results show robust two-dimensional ($2$-D) and three-dimensional ($3$-D) positioning. The experimental mean positioning error for $2$-D positioning is $0.54$ cm, in case one ignoring the tilt. The experimental mean positioning errors for $3$-D positioning are respectively $1.24$ cm, $1.85$ cm, and $6.02$ cm for ideal non-tilted and non-oriented, non-tilted but orientated, and both tilted and orientated scenarios.
Visible light communication (VLC)-based beacon systems, which usually transmit identification (ID) information in small-size data frames are applied widely in indoor localization applications. There is one fact that flicker of LED light should be avoid in any VLC systems. Current flicker mitigation solutions based on run-length limited (RLL) codes suffer from reduced code rates, or are limited to hard-decoding forward error correction (FEC) decoders. Recently, soft-decoding techniques of RLL-codes are proposed to support soft-decoding FEC algorithms, but they contain potentials of high-complexity and time-consuming computations. Fortunately, non-RLL direct current (DC)-balance solutions can overcome the drawbacks of RLL-based algorithms, however, they meet some difficulties in system latency or inferior error-correction performances. Recently, non-RLL flicker mitigation solution based on Polar code has proved to be an optimal approach due to its natural equal probabilities of short runs of 1's and 0's with high error-correction performance. However, we found that this solution can only maintain the DC balance only when the data frame length is sufficiently long. Accordingly, short beacon-based data frames might still be a big challenge for flicker mitigation in such non-RLL cases. In this paper, we introduce a flicker mitigation solution designed for VLC-based beacon systems that combines a simple pre-scrambler with a Polar encoder which has a codeword smaller than the previous work 8 times. We also propose a hardware architecture for the proposed compact non-RLL VLC receiver for the first time. Also, a 3-bit soft-decision filter is introduce to enable soft-decoding of Polar decoder to improve the performance of the receiver.
Feature extraction plays an important role in visual localization. Unreliable features on dynamic objects or repetitive regions will interfere with feature matching and challenge indoor localization greatly. To address the problem, we propose a novel network, RaP-Net, to simultaneously predict region-wise invariability and point-wise reliability, and then extract features by considering both of them. We also introduce a new dataset, named OpenLORIS-Location, to train the proposed network. The dataset contains 1553 images from 93 indoor locations. Various appearance changes between images of the same location are included and can help the model to learn the invariability in typical indoor scenes. Experimental results show that the proposed RaP-Net trained with OpenLORIS-Location dataset achieves excellent performance in the feature matching task and significantly outperforms state-of-the-arts feature algorithms in indoor localization. The RaP-Net code and dataset are available at https://github.com/ivipsourcecode/RaP-Net.