Precise indoor localization systems: Alternative methods for sub-sampling UWB pulses and associated error sources

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 localization knows many applications, such as industry 4.0, warehouses, healthcare, drones, etc., where high accuracy becomes more critical than ever. Recent advances in ultra-wideband localization systems allow high accuracies for multiple active users in line-of-sight environments, while they still introduce errors above 300 mm in non-line-of-sight environments due to multi-path effects. Current work tries to improve the localization accuracy of ultra-wideband through offline error correction approaches using popular machine learning techniques. However, these techniques are still limited to simple environments with few multi-path effects and focus on offline correction. With the upcoming demand for high accuracy and low latency indoor localization systems, there is a need to deploy (online) efficient error correction techniques with fast response times in dynamic and complex environments. To address this, we propose (i) a novel semi-supervised autoencoder-based machine learning approach for improving ranging accuracy of ultra-wideband localization beyond the limitations of current improvements while aiming for performance improvements and a small memory footprint and (ii) an edge inference architecture for online UWB ranging error correction. As such, this paper allows the design of accurate localization systems by using machine learning for low-cost edge devices. Compared to a deep neural network (as state-of-the-art, with a baseline error of 75 mm) the proposed autoencoder achieves a 29% higher accuracy. The proposed approach leverages robust and accurate ultra-wideband localization, which reduces the errors from 214 mm without correction to 58 mm with correction. Validation of edge inference using the proposed autoencoder on a NVIDIA Jetson Nano demonstrates significant uplink bandwidth savings and allows up to 20 rapidly ranging anchors per edge GPU.

Ultra-Wideband (UWB) communication system provides high accurate localization due to the high multi-path resolution capacity and large bandwidth of a UWB signal. Time Difference of Arrival(TDOA) technique provides very good accuracy, which is suitable for applying to UWB communication and localization systems. Estimating the target location from TDOA measurements is not an easy task since it is dealing with a set of nonlinear equations relating to the target location and the TDOA measurements. All kinds of method is by exhaustive search, which is computationally Intensive and suffers from the initialization and convergence problems. The paper develops an linear solution that estimate the target location, which does have convergence problems. Simulation results indicate that the proposed solution calculates the position of the target precisely.

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 study presents a bespoke hardware platform for indoor navigation, featuring a quadrotor equipped with an FZ3 card incorporating the AMD (formerly Xilinx) Zynq UltraScale+ ZU3EG MPSoC as the onboard computer. A core component of this platform is a field programmable gate array (FPGA) module specifically designed to efficiently compute Delaunay triangulations, enabling enhanced spatial awareness and real-time surface reconstruction. The onboard computer communicates with the flight controller, inertial measurement unit (IMU), ultra-wideband (UWB) localisation system, stereo camera, light detection and ranging (LiDAR) and ultrasonic sensors via robotic operating system (ROS) 2. The primary objective is to develop a cost-effective, modular unmanned aerial vehicle (UAV) system that can be adapted for a range of indoor navigation applications. The modular design supports different onboard computer platforms and sensor configurations, allowing researchers to easily customise the system for various experiments. By providing a practical framework for precise indoor navigation, this platform addresses the limitations of simulated, simplified laboratory setups, accelerating prototyping and supporting the deployment of UAVs in complex real-world environments. This work explores the UAV’s hardware architecture, the implementation of the Delaunay triangulation core on the FPGA system-on-chip (SoC), the ROS 2-based communication system and includes a detailed mass analysis and power estimation.

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.

In the post-epidemic era, with the resumption of production, orders for large-scale and small-batch production exponentially increase, new challenges to the productivity of the traditional production line have appeared, such as the redundant information, complex production process, which lead the traditional monitoring system cannot satisfy the requirements of rapid information interaction between management and executive layer in the workshop. According to the healthy control during epidemic, physical contact between workers should be reduced. Therefore, Ultra Wide Band(UWB) indoor localization and Augmented Reality(AR) technology are introduced, based on localization information and mobile device camera attitude estimation, an "actively push" mobile monitoring system can be built, which makes the workshop managers timely and completely monitor the manufacturing resources, non-contact production was also ensured. Finally, taking one equipment in a workshop as an example, the virtual visualization models are constructed in Unity software, and the effectiveness of the mobile monitoring system is realized based on UWB indoor localization technology and AR technology.

Narrow-band interference (NBI) from the coexisting narrow-band services affects the performance of ultra wideband (UWB) systems considerably due to the high power of these narrow-band signals with respect to the UWB signals. Specifically, IEEE 802.11a systems which operate around 5 GHz and overlap the band of UWB signals may interfere with UWB systems significantly. In this paper, we suggest a novel NBI suppression technique based on singular value decomposition (SVD) algorithm in time hopping UWB (TH-UWB) systems. SVD is used to approximate the interference which then is subtracted from the received signals. The algorithm precision and closed-form bit error rate (BER) expression are derived in the wireless multipath channel. Comparing with the conventional suppression methods such as a notch filter and a RAKE receiver, the proposed method is simple and robust and especially suitable for UWB systems.

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.

The demand and growth of indoor positioning has increased rapidly in the past few years for a diverse range of applications. Various innovative techniques and technologies have been introduced but precise and reliable indoor positioning still remains a challenging task due to dependence on a large number of factors and limitations of the technologies. Positioning technologies based on radio frequency (RF) have many advantages over the technologies utilizing ultrasonic, optical and infrared devices. Both narrowband and wideband RF systems have been implemented for short range indoor positioning/real-time locating systems. Ultra wideband (UWB) technology has emerged as a viable candidate for precise indoor positioning due its unique characteristics. This article presents a comparison of UWB and narrowband RF technologies in terms of modulation, throughput, transmission time, energy efficiency, multipath resolving capability and interference. Secondly, methods for measurement of the positioning parameters are discussed based on a generalized measurement model and, in addition, widely used position estimation algorithms are surveyed. Finally, the article provides practical UWB positioning systems and state-of-the-art implementations. We believe that the review presented in this article provides a structured overview and comparison of the positioning methods, algorithms and implementations in the field of precise UWB indoor positioning, and will be helpful for practitioners as well as for researchers to keep abreast of the recent developments in the field.

This paper presents of study of ultra-wide band (UWB) indoor positioning considering different type of obstacles that can affect the localization accuracy. In the actual warehouse, a variety of obstacles including metal, board, worker and other obstacles will have NLOS (non-line-of-sight) impact on the positioning of the logistics package, which influence the measurement of the distance between the logistics package and the anchor, thereby affecting positioning accuracy. A new developed method attempts to improve the accuracy of UWB indoor positioning, through and improved positioning algorithm and filtering algorithm. In this project, simulate the warehouse environment in the laboratory, several simulation proves that the used Kalman filter algorithm and Markov algorithm can effectively reduce the error of NLOS. Experimental validation is carried out considering a mobile tag mounted on a robot platform.

Ultrawideband (UWB) radio is a promising solution for low and high data rate wireless communications over short ranges. Space-time (ST) coding techniques are known to be simple and practical ways to increase both the spectral efficiency and the capacity in wireless communications. So far, few contributions have looked over multiple-inputs multiple- outputs (MIMO) UWB systems. In this paper we introduce two new families of ST codes suitable for BPM UWB modulations. The codes are characterized by full-rate, full-diversity and low complexity at the receiver side. The probability of error resulting from the use of these space-time codes in a typical MIMO UWB channel is analytically derived. Simulations support analysis for various numbers of transmit and receive antennas in order to enlight that the two methods can effectively enlarge the transmission range of UWB devices.

Ultra-wideband (UWB) time of flight (ToF) ranging is nowadays one of the most attractive technologies to implement indoor localization solutions with reliable dm-level accuracy. UWB systems are generally resistant to multipath interference. However, non-line-of-sight (NLOS) components with small relative delays may introduce errors in the estimated distances well exceeding the typically achievable accuracy. We present an empirical accuracy assessment of UWB ranging using a commercial UWB system. A particular focus is on the magnitude and spatial patterns of multipath errors. A large dataset comprising distances between 0.2 and 100 m was collected in a geodetic metrology lab and outdoors for this purpose. We derived ground truth of the distances with superior accuracy using a laser tracker and a total station. The results show that multipath errors are highly repeatable if the geometrical configuration does not change, but can degrade the accuracy of the distance measurements locally by almost an order of magnitude. While typical biases were on the order of a few cm, peak values exceeding 60 cm were found, and the standard deviation, typically about 3 cm or less, increased up to almost 15 cm in certain locations. We have used a part of the large dataset to train an artificial neural network (ANN) for error prediction. The ANN uses the measured ranges and additional signal features, also provided by the ranging nodes, as inputs. For error mitigation, the ANN output was subtracted from the original range measurements. This improved the ranging accuracy over the whole test data by practically eliminating the bias on average and reducing the overall standard deviation by a factor of 3. The results indicate that the relation between the output of the UWB node and the range errors can be modeled to a practically useful degree even without in-depth knowledge of the specific UWB node’s measurement process and characteristics. The proposed approach can help to significantly improve the UWB ranging performance in presence of multipath and thus also the accuracy of a corresponding localization system.

To solve the coexistence of ultra wideband (UWB) system with other existed narrowband systems operating within the UWB bandwidth, we propose an adaptive non-linear chirp UWB waveform based on the atan function in this paper. With the utilization of cognitive radio (CR) technology and the good time-frequency relationship of the chirp function, the flexible multiple narrowband interferences (NBIs) suppression can be achieved. Simulation results demonstrate the superior multiple NBIs suppression ability of the proposed scheme in direct sequence bipolar pulse amplitude modulation (DS-BPAM) UWB system, which is also compared with the linear chirp pulse based UWB system.

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.