Ultra-wideband Indoor Research Articles

Scheduling UWB Ranging and Backbone Communications in a Pure Wireless Indoor Positioning System

In this paper, we present and evaluate an ultra-wideband (UWB) indoor processing architecture that allows the performing of simultaneous localizations of mobile tags. This architecture relies on a network of low-power fixed anchors that provide forward-ranging measurements to a localization engine responsible for performing trilateration. The communications within this network are orchestrated by UWB-TSCH, an adaptation to the ultra-wideband (UWB) wireless technology of the time-slotted channel-hopping (TSCH) mode of IEEE 802.15.4. As a result of global synchronization, the architecture allows deterministic channel access and low power consumption. Moreover, it makes it possible to communicate concurrently over multiple frequency channels or using orthogonal preamble codes. To schedule communications in such a network, we designed a dedicated centralized scheduler inspired from the traffic aware scheduling algorithm (TASA). By organizing the anchors in multiple cells, the scheduler is able to perform simultaneous localizations and transmissions as long as the corresponding anchors are sufficiently far away to not interfere with each other. In our indoor positioning system (IPS), this is combined with dynamic registration of mobile tags to anchors, easing mobility, as no rescheduling is required. This approach makes our ultra-wideband (UWB) indoor positioning system (IPS) more scalable and reduces deployment costs since it does not require separate networks to perform ranging measurements and to forward them to the localization engine. We further improved our scheduling algorithm with support for multiple sinks and in-network data aggregation. We show, through simulations over large networks containing hundreds of cells, that high positioning rates can be achieved. Notably, we were able to fully schedule a 400-cell/400-tag network in less than 11 s in the worst case, and to create compact schedules which were up to 11 times shorter than otherwise with the use of aggregation, while also bounding queue sizes on anchors to support realistic use situations.

Identification and mitigation of non-line-of-sight path effect using repeater forhybrid ultra-wideband positioning and networking system

At least two decades ago, various applications have been proposed for the implementation of ultra-wideband (UWB) technology, but only a few of them are being realised such as radar detection, home networking, and indoor positioning. Although UWB positioning offers precise locality tracking, the accuracy of the estimation is greatly affected by the non-line-of-sight (NLOS) path effect. In this paper, we propose a hybrid indoor UWB positioning and networking system that utilises the existing repeater of the data network to eliminate the NLOS paths. A switching algorithm is written to identify the existence of NLOS paths based on received signal strength (RSS) and unique channel characteristics such as mean excess delay (MED) and root mean square (RMS). From the simulation results, the NLOS paths have been successfully identified under the NLOS environment. Hence, a higher probability of accuracy across the entire tested area can be achieved for the UWB positioning system by mitigating the NLOS path effect using the proposed algorithm. Besides that, the transmission of the line-of-sight (LOS) signal attains a data rate of around 15 times higher than the NLOS signal at a bit-error-rate (BER) of 10$^{-5}$ in the indoor networking system. In this case, the minimum-bit-error-rate (MBER) receiver is again shown to outperform the minimum-mean-square error (MMSE) and the performance gain drops around 5 dB to 6 dB at a BER of 10$^{-5}$ when the repeaters increase to a maximum number of 10 units. In conclusion, the proposed method is capable of mitigating the NLOS path effect on both indoor positioning and data networking systems.

Dual‐metasurface‐based wideband antenna

A dual metasurface-based wideband antenna is proposed herein. The antenna is fed through the stepped aperture of a coplanar waveguide. One layer of the metasurface is printed directly above the aperture, where the other layer is printed on the dielectric plate and placed above the antenna. The antenna bandwidth is significantly improved by the high-order modes excited by the double-layer metasurface. The proposed antenna is simple and compact. The − 10 dB relative impedance bandwidth of the proposed antenna is approximately 95.6% (4.8–13.6 GHz), and the main radiation direction deviates from the normal direction by 30°. The proposed antenna has an ultra-wideband (UWB), which facilitates short-range indoor UWB positioning.

Channel-Quality-Evaluation-Based Anchor Node Selection for UWB Indoor Positioning

Ultra-wideband (UWB) is a widely used technology in wireless indoor positioning. However, Non-line-of-sight (NLOS) and complex multipath fading introduce positioning errors to the UWB system. In order to alleviate the influence of non-line-of-sight (NLOS) and multipath fading, a channel-quality-evaluation-based method is proposed in this paper. In the proposed method, the qualities of the channel between unknown nodes and anchor nodes are evaluated by a weighted equation related to the channel impulse response (CIR) characteristics. Anchor nodes with higher quality are selected adaptively for positioning. The experiments showed that this method can reduce the root-mean-squared error (RMSE) of the positioning results by 40.4% on average and 95.78% in some strongly degraded cases.

Harris hawks optimization algorithm and BP neural network for ultra-wideband indoor positioning.

Traditional back propagation neural networks (BPNNs) for ultrawideband (UWB) indoor localization can effectively improve localization accuracy, although there is high likelihood of becoming trapped in nearby minima. To solve this problem, the random weights and thresholds of the BPNN are optimized using the Harris Hawks optimization algorithm (HHO) to obtain the optimal global solution to enhance the UWB indoor positioning accuracy and NLOS resistance. The results show that the predicted trajectory of the HHO and BPNN hybrid algorithm (HHO-BP) matches the actual position in the two-dimensional localization scenario with four base stations; the optimized average positioning error is effectively reduced in both indoor LOS and NLOS environments. In the LOS environment, the total mean error of the traditional BPNN algorithm is 6.52 cm, which is 26.99% better than the UWB measurement error; in the NLOS environment, the total mean error of the conventional BPNN is 14.82 cm, which is 50.08% better than the UWB measurement error. The HHO-BP algorithm is further optimized on this basis, and the total mean error in the LOS environment is 4.50 cm, which is 22.57% better than the conventional BPNN algorithm; in the NLOS environment, the total mean error is 9.56 cm, which is 17.54% better than the conventional BPNN algorithm. The experimental findings suggest that the approach has greater calibration accuracy and stability than BPNN, making it a viable choice for scenarios requiring high positional precision.

Individualization of Intensity Thresholds on External Workload Demands in Women's Basketball by K-Means Clustering: Differences Based on the Competitive Level.

In previous studies found in the literature speed (SP), acceleration (ACC), deceleration (DEC), and impact (IMP) zones have been created according to arbitrary thresholds without considering the specific workload profile of the players (e.g., sex, competitive level, sport discipline). The use of statistical methods based on raw data could be considered as an alternative to be able to individualize these thresholds. The study purposes were to: (a) individualize SP, ACC, DEC, and IMP zones in two female professional basketball teams; (b) characterize the external workload profile of 5 vs. 5 during training sessions; and (c) compare the external workload according to the competitive level (first vs. second division). Two basketball teams were recorded during a 15-day preseason microcycle using inertial devices with ultra-wideband indoor tracking technology and microsensors. The zones of external workload variables (speed, acceleration, deceleration, impacts) were categorized through k-means clusters. Competitive level differences were analyzed with Mann–Whitney’s U test and with Cohen’s d effect size. Five zones were categorized in speed (<2.31, 2.31–5.33, 5.34–9.32, 9.33–13.12, 13.13–17.08 km/h), acceleration (<0.50, 0.50–1.60, 1.61–2.87, 2.88–4.25, 4.26–6.71 m/s2), deceleration (<0.37, 0.37–1.13, 1.14–2.07, 2.08–3.23, 3.24–4.77 m/s2), and impacts (<1, 1–2.99, 3–4.99, 5–6.99, 7–10 g). The women’s basketball players covered 60–51 m/min, performed 27–25 ACC-DEC/min, and experienced 134–120 IMP/min. Differences were found between the first and second division teams, with higher values in SP, ACC, DEC, and IMP in the first division team (p < 0.03; d = 0.21–0.56). In conclusion, k-means clustering can be considered as an optimal tool to categorize intensity zones in team sports. The individualization of external workload demands according to the competitive level is fundamental for designing training plans that optimize sports performance and reduce injury risk in sport.

A Novel Ultra-Wideband Double Difference Indoor Positioning Method with Additional Baseline Constraint

Ultra-wideband (UWB) technology is suitable for indoor positioning owing to its high resolution and penetration. However, the current UWB positioning methods not only fail to fully analyze errors, but do not have the ability to eliminate gross and large random errors. In this article, the errors of UWB indoor positioning are analyzed comprehensively, and the basic function model is given. An indoor positioning method based on a double difference UWB with ranging observations is proposed and realized. In the proposed method, two UWB rover stations and a common base station are introduced, and the known baseline length between two rovers is used as the constraint condition for quality control. The observations and coordinate estimations are constrained by the prior and posteriori, respectively, and the weight of ranging observations with large residuals is reduced. Two groups of static experiments are designed. After adopting the proposed method, the plane error of one rover is 3.4 cm and 2.1 cm, and plane error of another rover is 3.3 cm and 2.0 cm, respectively. The positioning precision is improved by more than 80% compared with the traditional method. In the dynamic experiment, the coordinates of the starting and ending point obtained by the proposed method are basically consistent with the truth value, and the positioning results are close to the reference trajectory. The experimental results show that the proposed method can eliminate systematic and large random errors and improve the positioning precision effectively.

Self-calibration and Collaborative Localization for UWB Positioning Systems

Ultra-Wideband (UWB) is a Radio Frequency technology that is currently used for accurate indoor localization. However, the cost of deploying such a system is large, mainly due to the need for manually measuring the exact location of the installed infrastructure devices (“anchor nodes”). Self-calibration of UWB reduces deployment costs, because it allows for automatic updating of the coordinates of fixed nodes when they are installed or moved. Additionally, installation costs can also be reduced by using collaborative localization approaches where mobile nodes act as anchors. This article surveys the most significant research that has been done on self-calibration and collaborative localization. First, we find that often these terms are improperly used, leading to confusion for the readers. Furthermore, we find that in most of the cases, UWB-specific characteristics are not exploited, so crucial opportunities to improve performance are lost. Our classification and analysis provide the basis for further research on self-calibration and collaborative localization in the deployment of UWB indoor localization systems. Finally, we identify several research tracks that are open for investigation and can lead to better performance, e.g., machine learning and optimized physical settings.

Exploratory Factor Analysis of Match Period and Sex-Related Differences of External and Internal Workload Demands in Youth Basketball Players

Aims and Scope: The purpose of this study was to compare external and internal workload according to sex and match periods in youth basketball players during a congested-fixture tournament using an exploratory factor analysis method as a data reduction technique through Principal Component Analysis (PCA). Methods: Sixty-four youth basketball players (32 women and 32 men) participated in an official congested-fixture tournament of three consecutive matches. An ultra-wideband (UWB) indoor and inertial measurement unit (IMU) was used to quantify external and internal workload. PCA identified six to seven principal components that explained more than 60% and 70% of the variance for men and women, respectively. Significant differences between sexes in the external load variable as AccMax, DecMax, AccAvg, DecAvg, Acc2–4 /min, Dec2–4 /min, Sprint/min, SpeedMax, JumpsTotal, JumpsAvg, Takeoff3–5g /min, and Takeoff5–8g /min; and internal load variables as Player Load, Power Met, and Heart Rate bpm. TD/min, AccAvg /min, DecAvg /min, Acc2–4 /min, and Dec2–4 /min decreased significantly throughout the match. Result: The results indicated that acceleration and deceleration greater explained men locomotion while jumping actions had higher significance in women. Monitoring youth basketball competition is useful for understanding physical and physiological individual and collective demands. Conclusion: Statistical data reduction techniques are also sensitive to detect potential sex-related differences.

A New Modeling Methodology for Multipath Parameter Estimation in Ultrawideband Channels

A new modeling methodology for estimation of the parameters of the underlying multipath structure in an ultrawideband (UWB) indoor propagation channel is proposed in this article based on measurement campaign data available for a typical modern laboratory building. Issues inherent in generic multipath parameter estimation procedures such as limitations of curve fitting techniques, ambiguity in identifying cluster regions, and arbitrariness in estimating cluster and ray parameters accentuate a lack of transparency in the whole parameter estimation process and prevent the performance evaluation of different UWB physical layer proposals. A ramification of such techniques is that they are often too complex and intractable. The proposed approach, in this article, addresses these issues and offers a simple, tractable, easy to implement, and accurate solution based on a rationale proposing that the true behavior of the multipath structure is dependent on the structure of the considered environment itself and not on any additional reflectors. The results obtained from the proposed model are compared against the IEEE 802.15.4a CM1 and CM2 channel models. The proposed model establishes a normative to compare and evaluate performances between different UWB propagation models in realistic environments.

UWB Indoor Localization Algorithm Using Firefly of Multistage Optimization on Particle Filter

With the increasing application potential of indoor personnel positioning, ultra‐wideband (UWB) positioning technology has attracted more and more attentions of scholars. In practice, an indoor positioning process often involves multipath and Non‐Line‐Of‐Sight (NLOS) problems, and a particle filtering (PF) algorithm has been widely used in the indoor positioning research field because of its outstanding performance in nonlinear and non‐Gaussian estimations. Aiming at mitigating the accuracy decreasing caused by the particle degradation and impoverishment in traditional Sequential Monte Carlo (SMC) positioning, we propose a method to integrate the firefly and particle algorithm for multistage optimization. The proposed algorithm not only enhances the searching ability of particles of initialization but also makes the particles propagate out of the local optimal condition in the sequential estimations. In addition, to prevent particles from falling into the oscillatory situation and find the global optimization faster, a decreasing function is designed to improve the reliability of the particle propagation. Real indoor experiments are carried out, and results demonstrate that the positioning accuracy can be improved up to 36%, and the number of needed particles is significantly reduced.

Research on Indoor Wireless Positioning Precision Optimization Based on UWB

The ultra-wide band (UWB) indoor positioning precision often has large deviations due to environmental influences. To reduce noise error and improve the UWB indoor positioning precision, this paper divides the indoor positioning into static positioning and mobile positioning, and proposes different optimization algorithms for the two positioning modes. An improved self-organizing feature mapping neural network clustering algorithm is used for static positioning. After training, the layout of the neural network is established, and each weight vector is located at the center of the input vector cluster. Experiments indicate that the fully trained neural network can effectively filter noise, and reduce the mean square error significantly within 3.0×10−3. The positioning precision is 32.39% and 17.24% higher than those of the K-mean filtering algorithm and the Kalman filtering algorithm. For mobile positioning, the optimized neural network clustering algorithm is integrated with the unscented Kalman filter (UKF) to smooth the positioning data and reduce non-line-of-sight (NLOS) error. Experiments prove that this method can effectively reduce the errors caused by the NLOS state change, and estimate the distance with a high precision for ultra-wide band positioning and tracking.

Static LiDAR Assisted UWB Anchor Nodes Localization

The paper presents an indoor mapping and UWB (ultra-wideband) anchor localization method. The method, unlike most of the solutions described it the literature uses a static LiDAR (Light Detection and Ranging) mounted on a tripod rather than a robotic platform. It can be used at an any place, where employing a robot would be difficult (e.g. private homes), but since it requires manual LiDAR placement it would be most efficient in spaces of moderate areas. The proposed concept consists in mapping the environment of system installation while performing ranging measurements with deployed UWB anchor nodes. The SLAM (Simultaneous Location and Mapping) algorithm used for map integration and device localization relies only on LiDAR results. The matching is performed in two steps by finding an initial match based on corresponding landmarks extracted from the scans (intersections of the detected wall lines) and refining the results using an Iterative Closest Point algorithm. The anchors are localized based on the ranging results and SLAM-derived device locations using a Least-Squares based optimizer. The experiments have shown that the algorithm allows to construct a comprehensive map of the environment and localize the anchors with a root mean square error of 0.34 m, which is at similar level to analogous methods described in other works. The impact of anchor localization error on the systems performance was not significant. In both static and dynamic scenarios the difference in median errors obtained using reference and mapped anchors’ locations was about 0.05 m.

LOS/NLOS Identification for Indoor UWB Positioning Based on Morlet Wavelet Transform and Convolutional Neural Networks

In indoor ultra-wideband (UWB) positioning systems, positioning accuracy can be improved by determining the conditions of line-of-sight (LOS) and non-line-of-sight (NLOS) propagation and taking appropriate measures. The existing methods, such as support vector machine (SVM), decision tree (DT), k-Nearest Neighbor (KNN), identify LOS/NLOS mainly using time-domain characteristics. However, using only time-domain characteristics cannot achieve satisfactory performance. In this letter, we propose a LOS/NLOS identification method based on Morlet wave transform and convolutional neural networks (MWT-CNN). MWT-CNN is capable of identifying LOS/NLOS in the time-frequency domain. Our simulations are based on the 802.15.4a UWB model and an open-source dataset. The simulation results show that MWT-CNN achieves an accuracy of 100% in the office scenario, 99.89% in the industrial scenario, 96.10% in the residential scenario, and 98.84% in a real experimental scenario. Further simulation results show that MWT-CNN is more suitable to be deployed in static scenarios.

Planar Sectoral Antenna for IR-UWB Localization With Minimal Range Estimation Biasing

A planar sectoral antenna is presented, optimized for highly precise impulse-radio ultrawideband (IR-UWB) indoor localization with minimal range estimation biasing. By judiciously combining two planar inverted-F antenna elements into one footprint, a large half-power beamwidth (HPBW) is obtained in the [3.2448-4.7424] GHz band, thereby covering channels 1-4 of the IEEE 802.15.4a-2011 standard. Through system-level optimization, the system fidelity factor (SFF) is maximized for a minimal value of at least 90$\text{\%}$ within its entire HPBW, while minimizing the orientation-specific range estimation biasing down to 10 mm. To validate the antenna performance, measurements have been performed in both the frequency and time domain, showing a HPBW larger than 120$^\circ$ in the complete frequency band of operation and an SFF larger than 90$\text{\%}$ and range biasing lower than 4 mm within the antenna's HPBW.

Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation.

The growth of location-based services (LBS) has increased rapidly in last years, mainly due to the possibility to exploit low-cost sensors installed in portable devices, such as smartphones and tablets. This work aims to show a low-cost multi-sensor platform developed by the authors in which an ultra-wideband (UWB) indoor positioning system is added to a classical global navigation satellite systems–inertial navigation system (GNSS-INS) integration, in order to acquire different synchronized data for further data fusion analysis in order to exploit seamless positioning. The data fusion is based on an extended Kalman filter (EKF) and on a geo-fencing approach which allows the navigation solution to be provided continuously. In particular, the proposed algorithm aims to solve a navigation task of a pedestrian user moving from an outdoor space to an indoor environment. The methodology and the system setup is presented with more details in the paper. The data acquired and the real-time positioning estimation are analysed in depth and compared with ground truth measurements. Particular attention is given to the UWB positioning system and its behaviour with respect to the environment. The proposed data fusion algorithm provides an overall horizontal and 3D accuracy of 35 cm and 45 cm, respectively, obtained considering 5 different measurement campaigns.

An ultrawideband indoor positioning system with distributed ranging between tags

The aim of the work is to develop methodologies based on ultra-wideband positioning to create systems for the mutual positioning of moving objects. For this purpose, positioning errors in ultra-wideband radio navigation in the presence and absence of a direct signal propagation line between the radio tag of a mobile object and a base station are shown in the article. In presence of difficulties, pseudorange measurements acquire a displacement error. The construction of positioning systems using the ranges between radio tags (interpersonal channel) is proposed in the article. Construction of schemes based on radio channel models of copper mines shows that there is no error in the displacement of the RFID tag range, which is defined as the sum of the ranges by the intermediate RFID tag. With a low speed of movement of an intermediate RFID tag and a high frequency of range measurements, the intermediate marker can be represented as a fixed base station. The broadcast format of RFID signals will allow avoiding collisions on the air.

Tracking group housed sows with an ultra-wideband indoor positioning system: A feasibility study

Automated tracking of indoor housed farm animals is gaining an increasing interest in farming practice and research for monitoring animal behaviour and health. Several positioning solutions have become commercially available for different animal species, but the insight on how these systems perform in pig facilities is still limited. In this study the feasibility of tracking sows in a barn using an ultra-wideband (UWB) based indoor positioning system was investigated. The system consisted of 7 base stations and multiple tracker tags, and the determination of tags' positions was based on time of arrival (ToA). For locating stationary tags the system achieved 0.37 m accuracy at 1 m height and 0.50 m accuracy at 0.3 m height. While tracking moving tags at 1 m height 0.38 m accuracy was achieved. Three filters were tested to further improve the positioning performance. The median filter had the highest improvement on locating stationary and moving tags. The bilateral filter had a balanced capability of uncovering complex moving trajectories. The double exponential moving average filter was more suited when real-time updates were required. The overall results showed that tracking group housed sows inside a pig barn was feasible using the UWB positioning system. • A UWB positioning system achieved <0.5 m positioning accuracy using in a sow barn. • The positioning accuracy was dependent on the location and height of tracker tags. • Filters were more effective for improving tracking precision than accuracy. • The choice of filters depends on the motion characteristics and the application.

Review on Ultra Wide Band Indoor Localization

Ultra-Wide Band (UWB) is an important means of indoor positioning. Carrying out the research of UWB is of great significance to the development of indoor positioning technology. This article gives a review of the application of UWB in indoor positioning. The motivation and development status of UWB are introduced. UWB localization algorithms such as received signal strength indication, time of arrival, time difference of arrival are analyzed one by one. In this paper, the derivations of the algorithm are summarized. Several technical difficulties of UWB technology development and future development of UWB are presented. This paper provides researchers with a clear insight into the UWB indoor positioning system so that they can further develop other advanced techniques.

Performance Analysis of Parameter Estimator Based on Closed-Form Newton Method for Ultrawideband Positioning

Ultrawideband (UWB) is considered as a promising technology in indoor positioning due to its accurate time resolution and good penetration through objects. Since the functional model of UWB positioning is nonlinear, iterative algorithms are often considered for solving the nonlinear problem. With a rough initial value, we can obtain the optimal solution by the way of continuous iteration. As an iterative descent method of high efficiency, the Gauss–Newton method is widely used to estimate the position. However, in the UWB indoor positioning, since the location information of the user is rather limited, it is not easy to get a good initial value for iteration. Besides, the positioning system is prone to become ill-posed. These factors make the Gauss–Newton method not easy to converge to a global optimal solution or even diverge, especially under ill-conditioned positioning configuration. Furthermore, the linearization of the positioning functional model results in biased least-squares estimation. The Gauss–Newton method only includes the first-order Taylor expansion of distance equations. The bias comes from neglected higher order terms. In this study, the closed-form Newton method which considers the high order partial derivatives is introduced and proposed. The simulation and measurement experiments are implemented to analyze the performance of the closed-form Newton method in UWB positioning. Both the initial value factor and geometric configuration factor are discussed. Experimental results are given to demonstrate that the closed-form Newton method can converge to the global optimal solution with better convergence and higher efficiency regardless of whether the initial iteration value is reasonable or not. Meanwhile, the closed-form Newton method can improve the accuracy of positioning results, particularly when the anchors are not uniformly and symmetrically distributed, or the ranging error is relatively large. The study shows that closed-form of the Newton method has better convergence and positioning performance than the Gauss–Newton method, especially under ill-conditioned positioning configuration or relatively low measurement precision, even though it adds a little computational cost.