Resilient Indoor Localization System Based on UWB and Visual–Inertial Sensors for Complex Environments

Ultra-wideband (UWB)-based indoor localization systems are characterized by high accuracy, strong anti-interference capabilities, low power consumption, and excellent scalability, making them a prominent technology in indoor positioning. Determining accurate anchor node positions is a prerequisite for achieving high-precision tag localization. However, this process often requires significant manual effort and time and may not be feasible for certain scenarios involving manual measurements. To address this challenge, this paper proposes an accurate and convenient anchor self-localization method. The proposed method requires only TOF and PDOA data from UWB measurements without relying on external sensor data, and can realize single-anchor localization without the need to introduce multiple anchors to construct a coordinate system. Subsequently, the iterative reweighted least squares (IRLS) algorithm is employed to centrally process measurement data from each anchor, yielding optimized position estimates. In simulation experiments, we analyzed the impact of ranging noise and AOA estimation errors on localization accuracy. Furthermore, we conducted validation experiments, and the results demonstrated that the proposed method effectively achieves accurate self-localization of UWB anchor nodes.

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.

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.

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.

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.

Recent advances in the field of ultra wide band (UWB) localization system has brought renewed interest to the problem of indoor localization system, which has a wide range of applications, such as localization in mine, commodity localization in market or factory and other fields. However, in real scenario, the time synchronization and the limited located users problems are widely existed. An indoor localization system based on UWB is proposed in this paper, where a pseudo time synchronization algorithm is proposed to solve time synchronization problem. Moreover, a signal communication mechanism is established, by which located users only receive signals transmitted from reference base stations. In this case, it can locate unlimited number of users simultaneously in theory. The system has high localization accuracy and stability, and achieves the indoor 3D localization, which can be seen from the indoor static localization and the indoor dynamic localization experiments, respectively.

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.

<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>

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.

The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements are not yet complete. Like any other process, localization also has security requirements. The use of ultra-wideband (UWB) indoor localization systems have recently grown quickly in industries, with a reliable, fast, and have high accuracy performances. In particular, time difference-of-arrival (TDOA) is one of the widely used localization models. However, as TDOA measurement errors increase, the accuracy of the localization decreases. The accuracy of the TDOA measurement is influenced the accuracy of the localization and affected by multiple factors such as time synchronization, errors in sensor positions, missing data is caused by network attack. To reduce the influence of a sensor measurement error in localization, this paper proposes an improved localization algorithm for source localization using deep learning to address a TDOA measurements error or missing data in an asynchronous localization called DeepTAL. Unlike the conventional algorithm, DeepTAL can obtain highly accurate localization data in the presence of TDOA measurement errors or missing data. The algorithm starts with TDOA measurements without time synchronization. A network based on Long Short-Term Memory (LSTM) is then applied to achieve a stronger learning and better representation of the determined target state and TDOA prediction. The network can express features more abstractly at higher levels and increase recognition accuracy. After that, the target node is accurately located by TDOAs. We implement the DeepTAL algorithm on an asynchronous localization system with UWB signals. The experiments show that the proposed DeepTAL algorithm is efficient in improving the localization precision as measurement errors or missing data.

Localization system are widespread both outdoor and indoor and their objectives are various and of obvious importance: navigation, object and person localization and more. Indoor environments are particularly complex for position detection and localization; among several systems and the technologies used, the most commonly used is the Ultra Wide Band for its performance in terms of range and resolution. Any localization method strongly depends on a distance measurement and on its accuracy. In this paper after a short introduction on the state of art of indoor localization and on Ultra Wide Band technology, localization estimation technique is detailed in order to support the following sensor measurement characterization. The paper also report several measurements made on an indoor localization system based on UWB technology and extracts a noise model for the distance measurement. An exponential model for noise is extracted from measurements.

High-precision indoor localization and tracking are essential requirements for the safe navigation and task execution of autonomous mobile robots. Despite the growing importance of mobile robots in various areas, achieving precise indoor localization remains challenging due to signal interference, multipath propagation, and complex indoor layouts. In this work, we present the first comprehensive study comparing the accuracy of Bluetooth low energy (BLE), WiFi, and ultra wideband (UWB) technologies for the indoor localization of mobile robots under various circumstances. In the performed experiments, the error margin of the WiFi-based systems reached 608.7 cm, which is not tolerable for most applications. As a commonly used technology in the existing tracking systems, the accuracy of BLE-based systems is at least 44.12% better than that of WiFi-based systems. The error margin of the BLE-based system in tracking static and mobile robots was 191.7 cm and 340.1 cm, respectively. The experiments showed that even with a limited number of UWB anchors, the system provides acceptable accuracy for tracking the mobile robots. Using only four UWB beacons in an environment of about 431 m2 area, the maximum error margin of detected positions by the UWB-based tracking system remained below 13.1 cm and 28.9 cm on average for the static and mobile robots, respectively. This error margin is 88.05% lower than that of the BLE-based system and 93.27% lower than that of the WiFi-based system on average. The high tracking precision, the need for a lower number of anchors, and the decreasing hardware costs point out that UWB will be the dominating technology in indoor tracking systems in the near future.

As a fundamental prerequisite for a variety of location-based services, indoor location information has received increasing attention in recent years. Under the line-of-sight condition, the positioning accuracy of the indoor positioning technology based on ultrawideband (UWB) is acceptable for many applications, but under the non-line-of-sight condition, it degrades dramatically. The positioning accuracy can be significantly improved by the fusion of inertial measurement units and UWB sensors based on the extended Kalman filter (EKF) algorithm. However, when UWB measurements are affected by large non-Gaussian noise, the assumption of the EKF algorithm that observations are subject to Gaussian distribution for noise is invalid. Although the non-Gaussian noise can be handled by the robust EKF algorithm, this algorithm only uses the prior information to judge the reliability of the observations, and the positioning result is not stable when the number of beacons is small. To solve this problem, a method for successive updating of the covariance and posterior state of the observations in iterations based on an iterated extended Kalman filter (IEKF) is proposed. The marginal distribution of the posterior distribution is constructed and iteratively optimized, inhibiting the effect of non-Gaussian noise on UWB under a complex environment. The positioning results of the proposed method, the standard EKF algorithm, and the robust EKF algorithm, using different numbers of beacons, are compared. The results show that the positioning accuracy of the proposed algorithm is the highest under all scenarios. The proposed algorithm shows the smallest decrease in accuracy and presents the most stable positioning when the number of beacons is small, which is a common situation in practical applications.

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

Time-of-Arrival (ToA) based localization has attracted considerable attention for solving the very complex and challenging problem of indoor localization, mainly due to its fine range estimation process. However, ToA-based localization systems are very vulnerable to the blockage of the direct path (DP) and occurrence of undetected direct path (UDP) conditions. Erroneous detection of other multipath components as DP, which corresponds to the true distance between transmitter and receiver, introduces substantial ranging and localization error into ToA-based systems. Therefore, in order to enable robust and accurate ToA-based indoor localization, it is important to identify and mitigate occurrence of DP blockage. In this paper we present two methodologies to identify and mitigate the UDP conditions in indoor environments. We first introduce our identification technique which utilizes the statistics of radio propagation channel metrics along with binary hypothesis testing and then we introduce our novel identification technique which integrates the same statistics into a neural network architecture. We analyze each approach and the effects of neural network parameters on the accuracy of the localization system. We also compare the results of the two approaches in a sample indoor environment using both real-time measurement and ray tracing simulation. The identification metrics are extracted from wideband frequency-domain measurements conducted in a typical office building with a system bandwidth of 500 MHz, centered around 1 GHz. Then we show that with the knowledge of the channel condition, it is possible to improve the localization performance by mitigating those UDP-induced ranging errors. Finally, we compare the standard deviation of localization error of traditional localization system and UDP identification-enhanced localization system with their respective lower bound.