Edge Inference for UWB Ranging Error Correction Using Autoencoders

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.

A millimeter precision indoor ultra-wideband (UWB) localization system has been developed for high accuracy indoor localization applications including surgical navigation. We use two competing sub-sampling techniques in our system: the first technique exploits a small frequency shift of the transmitter clock and the receiver clock; the second technique uses a delay line chip at the receiver side. We discuss the limitations of these sub-sampling systems in the context of achieving sub-mm precision performance for biomedical applications requiring high accuracy indoor localization.

Conference lecture rooms are considered as an indoor environment for research with indoor localization that faces different challenges. In this paper, we design an indoor localization system in such a complex environment focus on machine learning algorithms with a large scale data. In this system, users can get information where a specified people locate in. Our system has two main sections: system design and algorithm approach. We use Bluetooth Low Energy devices to achieve communication and collect the information consist of people's name and corresponding RSSI data. To illustrate the advantages of machine learning algorithms using in this situation, a traditional algorithm will be proposed as a comparison. We also design and implement a Nine-Rectangle-Grid localization system in a practical laboratory environment. Experiments on real-world environment and simulations show our system can live up to locate people with low cost, high accuracy and short computing time.

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.

Various machine learning techniques on indoor localization using radio signals are being rapidly developed to achieve a sub-meter accuracy under noisy and complex environments. A fingerprint database using channel state information (CSI) extracted from a radio packet based on an orthogonal frequency diversity multiplexing (OFDM) channel can provide enough information to localize a transmitter device with a neural network (NN) based machine learning technique. In this article, we concern about the more practical use of the localization system using machine learning. We introduce a novel design of a signal preprocessing method for NN fingerprinting. To deal with the real building environment with corridors where certain signals cannot arrive at the receiver, our preprocessing with nonnegative matrix factorization (NMF) recovers multiview CSI of the original signal and complete the sparse CSI matrix, which enables robust and practical localization. The recovered CSI is then applied to variational inference-based machine learning that finds informative corridor views among multiview CSI. Our proposed system significantly outperforms other existing machine learning-based systems and shows a localization accuracy of 89 cm, while it still maintains the reliable accuracy even with 30% sparse network. It is the first time to consider how to design a practical localization system in an empirical building environment.

Understanding the indoor whereabouts of individuals and objects is important, especially for those who fall within the 71% of visually impaired individuals with a school education, students in 450 special education units and many other areas and aspects in Sri Lanka. Researchers have declared that, there isn’t any particularly good localization system, and the performance should be evaluated considering the approach and application. The most well-known indoor positioning (IP) technologies that have been historically deployed are Bluetooth, Wi-Fi, RFID (radio frequency identification), IR (Infrared), and UV (ultraviolet) whereas received signal strength (RSSI), fingerprinting, and triangulation methods have been used as common IP techniques. The combination of both IP technologies and techniques creates an IP system, and the integration of machine learning and IoT with the structured system essentially delivers an accurate and more advanced system. This paper contains a detailed, analytical review of a developed indoor positioning system derived from the existing indoor localization techniques, localization technologies, localization systems, algorithms, and performance matrixes. This also provides a comprehensive comparison between numerous existing systems to justify the proposed solution. This project has been developed to achieve better accuracy through low-cost deployment as an effective system to fill the gap in the scarcity of positioning systems in the world. This paper presents a descriptive introduction and problem definition, a critical discussion of results, machine learning models, benefits of the project, and future works. As later justified, ESP32 microcontroller and BLE beacons are utilized with RSSI fingerprinting method to develop this IP system and, as a part of the project, two data visualization methods have been introduced here using NodeRED dashboard and LC display. Overall, this project was developed with an effective combination of RSSI fingerprinting, IoT protocols, machine learning, and data interpretation methods. KEYWORDS: Indoor positioning, RSSI, Fingerprinting, Machine Learning, MQTT, ESP32, BLE beacons, NodeRED

In specific ultra-wideband (UWB) localization scenarios, conventional non-line-of-sight (NLOS) identification algorithm cannot detect other not-direct-path conditions. We proposed an adaptive not-direct-path identification method based on machine learning and artificial neural networks. Compared to the artificial division of not-direct-path data sets, we verified the advantages of data set partitioning by the method. The performance of NLOS recognition using different methods in different scenes is also analyzed, and the average identification accuracy in different scenarios can reach 92% or more.

Radio channel state information (CSI) measured with many receivers is a good resource for localizing a transmitter device with machine learning with a discriminative model. However, CSI localization is nontrivial when the radio map is complicated, such as in building corridors. This article introduces a view-selective deep learning (VSDL) system for indoor localization using CSI of WiFi. The multiview training with CSI obtained from multiple groups of access points (APs) generates latent features on a supervised variational deep network. This information is then applied to an additional network for dominant view classification to minimize the regression loss of localization. As noninformative latent features from multiple views are rejected, we can achieve a localization accuracy of 1.28 m, which outperforms by 30% the best known accuracy in practical applications in a complex building environment. To the best of our knowledge, this is the first approach to apply variational inference and to construct a practical system for radio localization. Furthermore, our work investigates a methodology for supervised learning with multiview data where informative and noninformative views coexist.

In this paper, we propose a new joint time-of-arrival (TOA) estimation and non-line-of-sight (NLOS) identification approach for ultra-wideband (UWB) localization systems. We use an energy-based TOA estimation (EBE) algorithm. The NLOS detection is based on the ratio between the first path power (FPP) and the total signal power (TSP) of the received signal folded version (RSFV) of the EBE algorithm. The proposed approach is evaluated by Monte Carlo simulation on the CM1 and CM2 channel models of the standard IEEE802.15.4a. The simulation demonstrates that the correct identification of channel models with the proposed approach gives better results than those based on joint likelihood ratio.

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.

Accurate indoor localization estimation has important social and commercial values, such as indoor location services and pedestrian retention times. Acoustic-based methods can achieve high localization accuracies in specific scenarios with special equipment; however, it is a challenge to obtain accurate localization with general equipment in indoor environments. To solve this problem, we propose a novel fusion CHAN and the improved pedestrian dead reckoning (PDR) indoor localization system (CHAN-IPDR-ILS). In this system, we propose a step length estimation method that adds the previous two steps for extracting more accurate information to estimate the current step length. The maximum influence factor is set for the previous two steps to ensure the importance of the current step length. We also propose a heading direction correction method to mitigate the errors in sensor data. Finally, pedestrian localization is achieved using a motion model with acoustic estimation and dynamic improved PDR estimation. In the fusion localization, the threshold and confidence level of the distance between estimation base-acoustic and improved PDR estimation are set to mitigate accidental and cumulative errors. The experiments were performed at trial sites with different users, devices, and scenarios, and experimental results demonstrate that the proposed method can achieve a higher accuracy compared with the state-of-the-art methods. The proposed fusion localization system manages equipment heterogeneity and provides generality and flexibility with different devices and scenarios at a low cost.

The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.

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 time-of-flight of a signal captures the time it takes to propagate from a transmitter to a receiver. Time-of-flight is perhaps the most intuitive method for localization using wireless signals. If one can accurately measure the time-of-flight from a transmitter, one can compute the transmitter's distance simply by multiplying the time-of-flight by the speed of light. Today, GPS, the most widely used outdoor localization system, localizes a device using the time-of-flight of radio signals from satellites. However, applying the same concept to indoor localization has proven difficult. Systems for localization in indoor spaces are expected to deliver high accuracy (e.g., a meter or less) using consumer-oriented technologies (e.g., Wi-Fi on one's cellphone). Unfortunately, past work could not measure time-of-flight at such an accuracy on Wi-Fi devices. As a result, over the years, research on accurate indoor positioning has moved towards more complex alternatives such as employing large multi-antenna arrays to compute the angle-of-arrival of the signal. These new techniques have delivered highly accurate indoor localization systems. Despite these advances, time-of-flight based localization has some of the basic desirable features that state-of-the-art indoor localization systems lack. In particular, measuring time-of-flight does not require more than a single antenna on the receiver. In fact, by measuring time-of-flight of a signal to just two antennas, a receiver can intersect the corresponding distances to locate its source. Thus, a receiver can locate a wireless transmitter with no support from the surrounding infrastructure. This is quite unlike current indoor localization systems, which require multiple access points at known locations, to find the distance between a pair of mobile devices. Furthermore, each of these access points need to have many antennas -- far beyond what is supported in commercial Wi-Fi devices.

The relevance of the research is due to the growing need to automate the processes of text analysis and correction, in particular for Ukrainian-language content, which is characterized by a wealth of morphological and syntactic structure. Due to the wide range of errors that can occur in texts, from spelling to contextual, there is an urgent need to create systems that can accurately identify errors and offer their correct corrections. The specificity of the Ukrainian language, including its grammatical complexity and multifacetedness, requires the adaptation of machine learning models to local features. The purpose of the research is to develop a mathematical model of a decision support system for identifying and correcting errors in Ukrainian-language texts. The task includes both the formalization and mathematical description of the text processing process, and the construction of a model with an orientation to the tasks of classification and text generation. Special attention is paid to the effective consideration of structural features specific to the Ukrainian language in order to increase the accuracy and productivity of the system. The research method is based on the construction of a mathematical model of error correction, which is presented as a context-aware text generation problem. The study used statistical methods and machine learning approaches. Special attention is paid to the formation of a training sample, which combines texts with real and artificial errors to ensure a balanced learning process. The correction module includes generation mechanisms based on contextual models capable of predicting the correct correction for erroneous tokens. Approaches to text vectorization are mathe- matically substantiated, taking into account the peculiarities of the morphology and syntax of the Ukrainian language. The constructed model is a universal basis for creating intelligent systems for automatic editing of Ukrainian-language text. As a result of the research, approaches to building an error correction model in Ukrainian-language texts are formulated and mathematically substantiated. The main result was the creation of an integrated system that uses contextual information to ensure high accuracy of error recognition and correction. The applied mathematical methods include probabilistic approaches and vector representation of tokens, which allows adapting the system to the peculiarities of the Ukrainian language with its high morphological and syntactic complexity. The formed basis of the model creates opportunities for scaling and further use in practical tasks, such as automatic text editing or improving the quality of content in the Ukrainian-speaking environment.