Configurable Multipath-Assisted Indoor Localization Using Active Relay

With the development of Internet of Thing (IoT), positioning is of a significant importance in indoor environments. Thus, recent years, localization technology has advanced greatly aimed at localizing the target by utilizing existing infrastructure such as WiFi. However, in a complex indoor environment, the performance of localization system tend to fluctuates when there is Non-Line-of-Sight (NLoS) propagation. Specially, for the high accuracy localization system using Angle of Arrival (AoA) or Time of Flight (ToF) of the direct signal path, the robustness is a challenge since the propagations of signal are not always in Line-of-Sight (LoS) due to the complicated indoor environment. Environment sensing is of significant importance for improving the accuracy and robustness of high accuracy localization system working in NLoS environment. In this paper, we propose a context-aware algorithm by using the spatial properties of multipath signal to aid indoor localization. Firstly, we investigate the influence of Signal-Noise-Ratio (SNR) on the distribution of signal spatial parameters, i.e., AoA and ToF, by deriving a mathematical model. Then, we creatively employ the method of clustering tendency analysis to construct a LoS/NLoS identification collaborating with non-parametric statistical method. Finally, we deploy a localization system with the proposed context-aware algorithm. The extensive experimental result shows that the proposed algorithm can sense environment well and further improve the accuracy and robustness of localization system.

With the increasing demand for location-based services, indoor localization technology based on wireless sensor networks (WSNs) has recently received significant attention. Bluetooth low energy (BLE) has become a research hotspot among the numerous WSNs due to its ubiquitous deployment, low-power consumption, and low cost. Many previous BLE-based indoor localization technologies can only use received signal strength indication (RSSI) for localization in the 2-D plane and achieve coarse accuracy. However, high-accuracy location information is required in an increasing number of applications, e.g., elderly fall detection for medical applications, which urgently requires a new BLE-based localization technology in 3-D space. To this end, we propose Spotlight, an angle-based fine-grained indoor localization system in 3-D space using BLE sensors. The critical idea of the Spotlight is to measure the angle-of-arrival (AoA) and elevation-of-angle (EoA) of the signal from the target to the BLE receivers and utilize them to achieve accurate 3-D localization. Specifically, we develop an offset elimination algorithm and data stitching algorithm, utilizing the second-order cone (SoC) programming to obtain accurate angle estimates. Then, the simulated annealing algorithm (SAA) is used to search for the target location in 3-D space. Finally, we develop the prototype system with commercial off-the-shelf (COTS) BLE sensors and conduct extensive experiment measurements in real-world scenarios. The results demonstrate that the proposed system can achieve a median localization error of 36 cm in the 2-D plane and 48 cm in the 3-D space.

Indoor localization has recently and significantly attracted the interest of the research community mainly due to the fact that Global Navigation Satellite Systems (GNSSs) typically fail in indoor environments. In the last couple of decades, there have been several works reported in the literature that attempt to tackle the indoor localization problem. However, most of this work is focused solely on two-dimensional (2D) localization, while very few papers consider three dimensions (3D). There is also a noticeable lack of survey papers focusing on 3D indoor localization; hence, in this paper, we aim to carry out a survey and provide a detailed critical review of the current state of the art concerning 3D indoor localization including geometric approaches such as angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), fingerprinting approaches based on Received Signal Strength (RSS), Channel State Information (CSI), Magnetic Field (MF) and Fine Time Measurement (FTM), as well as fusion-based and hybrid-positioning techniques. We provide a variety of technologies, with a focus on wireless technologies that may be utilized for 3D indoor localization such as WiFi, Bluetooth, UWB, mmWave, visible light and sound-based technologies. We critically analyze the advantages and disadvantages of each approach/technology in 3D localization.

In the field of integrated sensing and communication (ISAC), achieving high-precision location information based on channel state information (CSI) has emerged as a research hotspot. Traditional subspace-based parameter estimation methods suffer from performance degradation when some sampling points missing due to heterogeneous interference source. Besides, the majority of methods ignore the influence of outliers on the precision of estimation results when identifying paths. This paper proposes a CSI purification algorithm based on the alternating direction method of multipliers (ADMM), which exploits the sparsity of the CSI matrix in conjunction with low-rank matrix recovery (LRMR) techniques to effectively mitigate interference and enhance path resolution. A parameter estimation method for dynamic outlier removal is adopted, which combines matrix pencil (MP) and density-based spatial clustering of applications with noise (DBSCAN) algorithm to dynamically adjust the input parameters to achieve the removal of outliers. We performed indoor measurements and showed that the CSI purification algorithm was able to reduce the median angle of arrival (AoA) error from 8.39° to 6.24° when half of the sampling points were missing. Besides, the addition of the dynamic outlier removal algorithm improves AoA estimation performance by 4.1%. In terms of localization measurement, the median localization error of the proposed system in the activity room and the meeting room is 0.39 m and 0.41 m, respectively. In the case of missing half of the data, the positioning accuracy of the proposed algorithm is 0.47 m, which is better than that of the mainstream algorithms.

This paper proposes conditioning angle of arrival (AOA) algorithms for pseudo-spectrum fingerprint acquisition based on line of sight (LOS) and non-LOS detection schema for optimizing indoor localization. The proposed approach merges two AOA based methods being that of the MUltiple Signal Classsification (MUSIC) algorithm and virtual MUSIC algorithm into a conditional based localization approach with a uniform circular array (UCA). The paper begins by demonstrating the environmental dependencies of the two AOA approaches based on the Rician <tex>$K$</tex>-factor metric. The <tex>$K$</tex>-factor is then exploited as an algorithm selection metric to arrive at improved localization performance in a realistic indoor environment.

There is an emerging market today for indoor positioning systems capable of working alongside global navigation satellite systems, such as the global positioning system, in indoor environments. Many systems have been proposed in the literature but all of them have fundamental flaws that hold them back from widescale implementation. We review angle-of-arrival (AOA) and angle-difference-of-arrival (ADOA) optical indoor positioning systems which have been proven to be robust, accurate, and easily implementable. We build an AOA/ADOA optical indoor positioning system out of a simple commercial high-speed camera and white light light emitting diodes (LEDs) which operate over a working area of 1 m3, and compare its performance to other indoor positioning methods. The AOA and ADOA systems achieve positioning with low errors of 1.2 and 3.7 cm, respectively.

Indoor localization systems have already wide applications for providing localized information and directions. They can also be very useful in medical applications, as an individual’s indoor movements can be indicative of his/her clinical status. Research for indoor localization systems is ongoing and there are numerous solutions with different hardware or algorithmic approaches, for different use cases. Ease of setup and cost are significant factors when using indoor localization systems for medical applications. This paper proposes an indoor localization system with room-level accuracy, focusing on an easy installation procedure that can be easily followed from non-technical staff, as well as on low cost, so that it can be widely adopted. The proposed system has been mainly designed for behavioural monitoring of adults in an indoor environment. The system operates by tracking a carried device based on the processing of RSS (Received Signal Strength) measurements from Bluetooth Low Energy beacons placed in the area. The proposed system has been evaluated in realistic settings and and achieves an accuracy of 95.31% in room estimation.

In this paper, the indoor visible light positioning algorithm based on the technique Time Difference of Arrival (TDOA) using Hilbert transform is presented. The proposed indoor positioning system (IPS) is designed with the single light emitting diode (LED) ceiling lamp transmitter and the target device with multiple receiver photodiodes (PD). The mathematical model of the proposed system is derived that constitutes the different approaches for estimating the coordinates of the target device. As the phase difference is the key parameter for estimating the location in the TDOA approach, we have employed the Hilbert transform to calculate the phase difference for different PDs. The mean localization error achieved for the indoor environment of 5 m × 5 m × 3 m is in the range of 10−4 m. The mm-level accurate IPS system is designed with relatively lower complexity than the other positioning techniques, which are Received Signal Strength (RSS), Angle of Arrival (AOA), and Time of Arrival (TOA). This is due to the single LED transmitter requirement for the positioning of target device, whereas the other techniques necessitate multiple transmitters. The estimation is done with the different transmitter and receiver positions and the performances are compared. Based on the analysis, the optimum positions for the single LED ceiling lamp are provided.

Indoor localization has been actively researched in recent years due to the increasing demand for location-awareness services. However, to balance localization accuracy and system cost is always a challenge for indoor localization systems. Radio frequency identification (RFID) is a promising technology to achieve both goals, because of its reasonable cost and reliability. In this paper, we propose a novel RFID indoor localization system based on angle of arrival (AoA) and phase of arrival (PoA) methods. This system leverages RFID's two experimental signal diffusion characteristics to estimate AoA. One is that the interrogation zone is constrained in a lobe, and only in this area the tag can be queried. The second is that there exits a stable pattern of received signal strength (RSS) on angle changes. We use the two features to find a general area and to pinpoint the AoA consecutively. This effectively narrows the sampling zone (where signal needs to be sampled), and helps to reduce computational complexity. In addition, we reduce the multipath effect on range estimation by determining the AoA and rotating the reader into the direction of the target. Moreover, we exploit two signals with a slightly different frequency to eliminate the phase ambiguity issue. Our system takes only one reader and achieves mean accuracy of 23 cm. The simplicity and effectiveness of our system make it convenient to be used in practice.

In this article, we propose a new robust, stable, accurate, and efficient time-delay estimation scheme for the angle-of-arrival (AoA) ultrasonic indoor single-source localization systems. The AoA technique employing microphone arrays and ultrasonic signal emitting sources has turned out to be a promising alternative to the conventional distance-based ultrawideband systems for indoor localization. The crux of AoA estimation lies in the derivation of time-delay estimates, which is typically handled by advanced digital signal processing approaches based on cross-correlation and matching algorithms. The calculation of the channel response for each received signal and the conversion from the time to the time-delay domain consent our echo suppression delay estimator (ESDE) to apply echo suppression, in order to overcome the typical challenges introduced by the channel in indoor environments. Moreover, the optimization of the cross-correlation algorithm through oversampling the cross power spectrum and the curve-fitting algorithm allows to ensure subsample precision results. The performances of our developed AoA estimation system are evaluated by carrying out real-world experiments and compared with the state-of-the-art generalized cross correlators. Our ESDE algorithm is shown to outperform several generalized solutions.

&amp;lt;p&amp;gt;The interest in indoor positioning systems has been rising in the last couple of decades. This interest was further accelerated by the availability of signals of opportunity. The introduction of signals of opportunity into the indoor environments has meant that these widely available technologies can be utilised to achieve higher levels of accuracy and precision for positioning in parking garages, airports, underground locations, large office buildings, and shopping centres. Indoor positioning can also be utilised for emergency services or for Building Information Models (BIM) for purposes such as construction sites or hospital building management. In addition to the need for indoor and outdoor positioning, there is a need to ensure seamless transitions in and out of these different environments. For example, building information from BIM could be utilised for navigation of BIM users that considers the architectural barriers (e.g., for wheelchairs, baby strollers, and transport of large equipment within the facility). For this, a positioning system capable of seamlessly transitioning in and out of buildings is needed. This paper proposes a multi-sensor and cooperative positioning method of seamless environment transition and presents the first architecture concept. For this purpose, Global Navigation Satellite System (GNSS) data are planned to be fused with Ultra Wide-Band (UWB), Inertial Measurement Unit (IMU), Wireless-Fidelity (Wi-Fi), visual camera and LiDAR (Light Detection and Ranging) data. All sensors required for such a positioning system, are already available on modern smartphones commonly used as low-cost platforms for pedestrian navigation and localisation. Newer smartphones are also equipped with LiDAR and UWB chips, which are a future signal of opportunity, just like Wi-Fi currently is. The transition to different environments will be determined based on the UWB anchors called identical anchors that will be used to transition from the global coordinate system for the outdoor environment to the local building environment for positioning in indoor environments. The proposed multi-sensor and cooperative positioning system is expected to be able to achieve decimetre-level accuracy.&amp;lt;/p&amp;gt;

As sensor-rich mobile devices became a commodity, more opportunities appeared for the creation of location-aware services. While GPS is a well established solution for outdoor localization, there is still no standard solution for localization indoors. This paper presents a novel accurate indoor positioning mechanism that is meant to run in common smartphones to be a readily and widely available solution. The system is based on multiple gait-model based filtering techniques for accurate movement quantification in combination with an advanced fused positioning mechanism that leverages sequences of opportunistic observations towards an accurate localization process. Magnetic field fluctuations, Wi-Fi readings and movement data are incrementally matched with a feature spot map containing multi-dimensional spatially-related features that characterize the building. A novel and convenient way of mapping the architectural and environmental properties of buildings is also introduced, which avoids the burden normally associated with the process. The system has been evaluated by multiple users in open and crowded spaces where overall median localization errors between 1.11 m and 1.68 m were obtained. While the reported errors are already satisfactory in the context of indoor localization, improvements may be readily achieved through the inclusion of additional reference features. High accuracy performance coupled with an opportunistic and infrastructure-free approach creates a very desirable solution for the indoor localization market doge.

Angle of arrival (AOA) and received signal strength (RSS) measurements have been commonly used in wireless localization due to easy access and simple implementation. In this paper, we investigate source localization using the AOA-only and hybrid RSS/AOA measurements, respectively. In AOA localization, we approximate the angle error using a range-related quantity. Then the optimization problem based on maximum likelihood (ML) is converted to a convex semidefinite programming (SDP) problem. In hybrid AOA/RSS localization, the ML estimator is decomposed into an RSS part and an AOA part. The AOA part follows a similar procedure as in the AOA localization. Taylor series expansion and relaxation are applied in optimizing the RSS part. These two parts are closely related through the range. The proposed methods avoid the nonconvexity in the original ML estimators for both AOA-only and hybrid AOA/RSS localization problems. Numerical examples show good performance of the proposed methods in both AOA and hybrid AOA/RSS localizations. They are close to or better than the LS methods in the literature.

Indoor Positioning System (IPS) has always been a focused area of research due to an increasing demand for IPS in a variety of civilian and defence applications. Bluetooth low energy beacons are best suited for IPS owing to their compatibility with mobile devices such as smart phones. In this paper, a standard bluetooth module is configured to work as a Bluetooth-Beacon (BT-Beacons) and an IPS is implemented using the RSSI values of signals from the BT-Beacons. The position of user in indoors is computed using the principle of trilateration. The proposed system achieved good positioning accuracy and is economical. Further, the proposed system can be improved to assist localization and navigation in indoor environment such as huge buildings.

: The operator of the Army Analytical Photogrammetric Positioning System (APPS) must perform a critical step in the coordinate determination procedure by the subjective comparison of dissimilar images. He must correlate reconnaissance mission imagery with aerotriangulated photographic stereoscopic pairs, known as data base imagery, and visually identify the image position on the data base of a target detected on the mission image. Results obtained from 40 image interpreters performing this task using vertical, oblique, and panoramic mission imagery are given. Two levels of target position difficulty were established: (1) A-points located at terrain or man-made features mutually identifiable on both mission and data base imagery; (2) B-points remote from terrain or man-made features mutually identifiable on mission and data base imagery. Transfer of A-points for vertical, oblique, and panoramic photographs was accomplished with good accuracy--median location error was 19 meters or less. Performance was significantly better when A-points in panoramic missions were located in the near-vertical half of the image in contrast to the near-horizon half. Transfer of B-points was more difficult. For vertical missions, median location error was about 20 meters. For oblique or panoramic imagery, location errors were markedly greater when the B-points were located in the horizon half of the image as compared to performance when the points were in the near-vertical half of the image.