UWB Anchor Self-Localization Algorithm Based on PDOA and TOF

Combining ultra-wideband (UWB) and low-cost inertial measurement unit (IMU) by a tight coupling fashion is an effective way to provide an integrated indoor localization system with high accuracy and continuity. However, the performance of such an integrated system may be poor in the presence of undesirable geometry between an UWB target node and a number of UWB anchors. In addition, some parameters in the state vector of Kalman filter (used for data fusion) are unobservable from UWB ranges. These problems have not been discussed in detail in the literature related to UWB localization. In this paper, we analyze the performance of integrated localization under different geometries between the UWB node and anchors based on Fisher information matrix. Also, the parameter observability with respect to UWB ranges is analyzed.

In this article, a resilient tightly coupled ultra-wideband (UWB) visual–inertial indoor localization system (R-UVIS) is developed to obtain accurate and robust localization performance in complex scenes, even in the case when sensors fail. More specifically, three schemes are designed for the proposed system. First, we introduce the line and image patch features to improve the precision and robustness of the visual features. Besides, we propose accurate loop closure and relocalization methods based on multifeatures to improve the performance of the localization system. Second, we introduce the UWB sensor into the system to suppress the localization drifts in the complex scenes and provide a fixed reference frame. Third, we propose a resilient multisensor fusion method based on an optimization framework to fuse the UWB, visual and inertial measurements in a tightly coupled manner. This data fusion approach improves the robustness of the system, making the localization system seamlessly switch among different localization modes depending on the specific scenes. In addition, an initialization process along with three sensors is also designed for the whole system. We conduct extensive experiments on the public dataset and in real-world scenarios to evaluate the proposed R-UVIS. The experimental results show that the proposed R-UVIS can provide the accurate and robust localization results in a fixed coordinate system for the complicated indoor scenes, even in the case when visual tracking fails or the UWB anchors are unavailable.

High-accuracy localization systems for non-line-of-sight (NLOS) environments have been a popular focus of research on indoor positioning. In particular, the use of ultra-wideband (UWB) technology for ranging is emerging as an effective method of indoor localization due to its high accuracy. To improve the overall performance of UWB systems, combining UWB technology with an inertial navigation system (INS) is a commonly used indoor positioning approach. However, UWB measurements are affected by outliers under the NLOS propagation of radio signals. In addition, INS tend to suffer from error accumulation without constraints imposed by observations from other sensors. To address these problems, an adaptive robust extended Kalman filter algorithm based on tightly coupled UWB/INS/floor map integration is proposed, with which the noise in the UWB observations can be adaptively adjusted. The integrated system performs NLOS identification based on a map line segment matching algorithm; then, abnormal observations are abandoned, or their weights are reduced to prevent the error accumulation of the INS from being incorrectly constrained. Finally, an evaluation based on experimental data shows the significant enhancement achieved with the proposed algorithm based on the integration of UWB technology, an INS and a floor map, which can achieve a 2-D positioning accuracy that corresponds to a root mean square error of 0.27 m. Thus, substantial improvements can be achieved for long-term operation in NLOS environments using the proposed integration scheme.

This paper introduces an indoor integrated navigation system for the auto-control of unmanned aerial vehicle (UAV) and robot that fuses inertial sensors and ultra wide band (UWB) networks. In this paper, we concentration on the problem of selecting the optimized UWB anchor node to obtain the distance measurement between the selected anchor node and the tag node for this indoor integrated navigation system. Taking the navigation accuracy and system power consumption into account according to the requirements and features of UWB system, we propose and analyze the performance of four kinds of anchor node selection algorithms, named alternation algorithm, powerfirst algorithm, accuracy-first algorithm and the optimized algorithm considering power and accuracy. In the fourth algorithm, we introduce dynamic local dilution of precision (DOP) values which reflect the impact of the geometric distribution of anchor nodes to guarantee precision and define a cost function of DOP values and UWB communication distance for a complementary consideration of power consumption. To fuse the inertial sensor data and the selected UWB distance measurement, an extended Kalman filter (EKF) is applied in this integrated system to correct the state errors. Simulations and experiments with low-cost Micro-electromechanical inertial sensors and Decawave UWB transceivers have been done to evaluate the performance of these four anchor node selection algorithms. The simulation and experiments results verify that the fourth algorithm proposed in this paper can achieve a good balance in system multiple demands of navigation accuracy and system power consumption.

Traffic safety analysis has traditionally relied on historical road collision data. However, this approach has many limitations due to well-known challenges with the availability and quality of collision data. Moreover, collecting sufficient crash data to develop statistical models for traffic safety analysis is only possible after the societal damage due to collisions has been sustained. Those problems are more likely when studying pedestrian safety. To address these constraints, researchers utilize traffic conflict indicators to identify the severity of conflicts and develop strategies to enhance road safety. This study evaluates Ultra-Wideband (UWB) technology for estimating the post-encroachment time (PET) indicator, a commonly used measure in pedestrian safety. Indoor experiments were conducted to explore potential multipath issues commonly encountered in wireless-based localization systems. The time-division multiple access (TDMA) scheme was utilized by assigning 20 ms time slots for stable communication between a tag and an anchor. To address the different clocks in UWB anchors and tags, the master–slave technique was employed for time synchronization between the devices. The experiments also examined the storage of UWB measurements using a cloud-based global clock for time synchronization. The study found that the mean absolute error (MAE) in PET is 4.92 s under interference conditions and 0.148 s with the TDMA technique between the ground truth and the UWB measurements. The findings offer valuable insights for future studies aimed at enhancing UWB accuracy.

With the increasing commercial interest in proximity services, various wireless localization techniques have gained prominence. Among them, Ultra-wideband (UWB) stands out as a promising solution due to its centimeter-level localization accuracy. One application of UWB is the gate system that enables users to pass through gates without tapping for payment, such as NFC. In this paper, we propose a new paradigm for the gate system called NewGate, which optimizes localization and transaction algorithms over UWB. We implemented all components of NewGate, including mobile devices, UWB anchors, and UWB modules in the gate. Using commercial devices such as Samsung Galaxy S23+ and Ultra, we evaluated the performance of NewGate. NewGate achieved a localization accuracy of 8.3 cm and 15.1 cm in static and dynamic scenarios, respectively. The transaction latency through UWB was measured to be 959.6 ms, enabling seamless gate entry without stopping. Moreover, we conducted an experiment at Seoul Oksu subway station to demonstrate the practicality and feasibility of NewGate. Finally, the large-scale simulations revealed the traffic throughput performance and scalability of NewGate, showing a 32% improvement in time taken for 120 people to pass the gate compared to traditional NFC methods.

Indoor positioning is usually based on individual technologies that provide estimates of the trajectory of the person, or measures the ranges or angles between the user and known positions. Each technique has its advantages and problems, and a common way to overcome the drawbacks of single-technology solutions is to fuse the information from several system, but due to their non linear measurements, there is no optimal linear solution. We propose the use of a particle filter to fuse foot mounted inertial measurements with any additional Radio Frequency (RF) measurement. The information fusion is achieved propagating the position of the particles using the relative step displacements obtained from foot mounted Pedestrian Dead Reckoning (PDR), and updating the weights of the particles according to the RF measurements. In our experiments the inertial unit was located in the foot of the user, and the RF system consisted in a Radio Frequency Identification (RFID) receiver in the waist, and an Ultra Wide band (UWB) tag in the chest, but the scheme can be used in any sensor configuration. As the UWB measurements have a significant amount of outliers due to non line of sight conditions generated by the position of the tag in the body, received reflections, etc., we propose a new outlier rejection algorithm based on the compatibility of groups of measurements. The fusion was tested evaluating the inclusion of each of the RF systems and varying the number RFID tags used. The proposed method is able to locate a person with less than 2 m of error (for 90 % of the obtained estimations) in the studied trajectory. This particle filter scheme offers robust indoor positioning with 100 % availability and smooth trajectory estimation thanks to the PDR and limited error due to the RF measurements.

Ultra-wideband (UWB) is a radio technology that is usually used for indoor localization. To complete the 3D localization task in a room, more than three UWB anchors should be installed without occlusion, then a UWB tag can be localized. An Inertial Measurement Unit (IMU) is usually fused to provide smoother results. However, the requirements for the installation of UWB anchors limit their application in a floor with multiple rooms and corridors. In this paper, we propose a localization method that needs fewer anchors in this environment. In addition to the range information from UWB anchors, non-holonomic constraints from the carrier and reference trajectory from the special environmental structure are added as observations of the error-state Extended Kalman Filter (EKF) system. The proposed method can complete the localization task in the corridor-like areas using a single UWB anchor, so fewer UWB anchors are needed in a floor with multiple rooms and corridors. The real-world experiment video is available at https://youtu.be/DT-02Id_94A.

The performance of ultra-wideband (UWB) indoor positioning systems based on time of arrival (TOA) techniques is generally affected by the density of reference points (RPs), as well as undetected direct path (UDP) conditions. For a fixed number of reference points (RPs), the performance of some indoor positioning algorithms tends to degrade as the size of the area is increased, i.e. the RP density is decreased. In this paper, we evaluate the effects of RP density on the performance of different positioning algorithms in the presence of empirical distance measurement error (DME) models derived from UWB measurements in typical indoor environments. We then present functional relationships between RP density and positioning mean-square error (MSE) for these algorithms. These relationships can be used for more effective indoor positioning system design and deployment. Finally, we investigate the effects of bandwidth with respect to improving the performance of these algorithms

In this paper, the problem of indoor localization in wireless networks is addressed relying on a swarm-based approach. We assume to know the positions of a few number of sensor nodes, denoted as anchor nodes (ANs), and we aim at finding the position of a target node (TN) on the basis of the estimated distances between each AN and the considered TN. Since ultra wide band (UWB) technology is particularly suited for localization purposes (owing to its remarkable time resolution), we consider a network composed of UWB devices. More precisely, we carry out an experimental investigation using the PulsOn 410 ranging and communication modules (RCMs) produced by time domain. Using four of them as ANs and one of them as TN, various topologies are considered in order to evaluate the accuracy of the proposed swarm-based localization approach, which relies on the pairwise (AN-TN) distances estimated by the RCMs. Then, we investigate how the accuracy of the proposed localization algorithm changes if we apply to the distance estimates a recently proposed stochastic correction, which is designed to reduce the distance estimation error. Our experimental results show that a good accuracy is obtained in all the considered scenarios, especially when applying the proposed swarm-based localization algorithm to the stochastically corrected distances. The obtained results are satisfying also in terms of software execution time, making the proposed approach applicable to real-time dynamic localization problems.

Ultra-wideband (UWB) positioning is widely used in indoor positioning due to its low power consumption and high accuracy. However, owing to UWB clock drift and poor communication conditions, it will cause errors or even loss of UWB signals, which will eventually affect position calculation. Therefore, based on the TDOA algorithm, this paper designs and implements a LSTM-WKNN hybrid positioning model based on UWB. This model uses the LSTM model to predict the TDOA data at the future time, and uses the predicted data to correct the actual UWB measurement data. After modified, the TDOA value is transferred to the WKNN model, then the positioning coordinates are solved. The experimental results show that the average positioning error of this model is within 20cm, and it has high positioning accuracy.

A quickly growing location-based services area has led to increased demand for indoor positioning and localization. Undoubtedly, Wi-Fi fingerprint-based localization is one of the promising indoor localization techniques, yet the variation of received signal strength is a major problem for accurate localization. Magnetic field-based localization has emerged as a new player and proved a potential indoor localization technology. However, one of its major limitations is degradation in localization accuracy when various smartphones are used. The localization performance is different from various smartphones even with the same localization technique. This research leverages the use of a deep neural network-based ensemble classifier to perform indoor localization with heterogeneous devices. The chief aim is to devise an approach that can achieve a similar localization accuracy using various smartphones. Features extracted from magnetic data of Galaxy S8 are fed into neural networks (NNs) for training. The experiments are performed with Galaxy S8, LG G6, LG G7, and Galaxy A8 smartphones to investigate the impact of device dependence on localization accuracy. Results demonstrate that NNs can play a significant role in mitigating the impact of device heterogeneity and increasing indoor localization accuracy. The proposed approach is able to achieve a localization accuracy of 2.64 m at 50% on four different devices. The mean error is 2.23 m, 2.52 m, 2.59 m, and 2.78 m for Galaxy S8, LG G6, LG G7, and Galaxy A8, respectively. Experiments on a publicly available magnetic dataset of Sony Xperia M2 using the proposed approach show a mean error of 2.84 m with a standard deviation of 2.24 m, while the error at 50% is 2.33 m. Furthermore, the impact of devices on various attitudes on the localization accuracy is investigated.

An indoor positioning system (IPS) is a technology employed to locate objects and people within a building scenario using signal processing or other sensory information. Ultra Wide Band (UWB) is a versatile wireless technology that can be employed as an IPS and has shown very good performances. UWB can be used in many scenarios and its effectiveness in through wall detection along with its excellent resolution for person localization is one of the best applications of Impulse Radio (IR) UWB. The main objective of this work is to propose a concept for intelligent radar systems employing UWB augmented by machine learning approaches to not only localize but understand the location of a person or target within a building. Although suitably developed UWB is excellent for obtaining localizing data it does not automatically understand what that location effectively means or where it is thus further methods are required to create meaningful data for end user appreciation. Learning from the huge amount of UWB signal data through Multi Class Support Vector Machine (MC-SVM) architecture enables a truly evolving scheme to both localize targets and identify them in a useful way. Statistical analysis of the experimental results supports the proposed algorithm.

<p>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.</p>

In this paper, we present the results of the measurements and modeling of the propagation channel for ultra wide band (UWB) communications in large indoor environments. The selected environment is a sports center of large dimensions ($51 \times 26$ m, 7.4m height) without intermediate obstacles so that there is always direct path (DP) between TX and RX and UWB technology can be used for critical communications of sensors. On this environment, narrow band and ultra wide band measurements have been made with a high resolution pulse channel sounder (8ns) at the nominal frequency of 3.9 GHz. Narrow band measurements have been used to model path loss for the DP, while for UWB measurements, the power delay profile have been used to obtain the statistics of the multipath of the channel. Combining both models has been possible to obtain a detailed characterization of the environment. The results constitute a complete characterization of this type of indoor environments for UWB signals and can be easily extrapolated to other environments with similar characteristics, allowing the use of UWB technology for applications of critical communications for sensors and other devices.