280 research outputs found

    Implementation of an Autonomous Impulse Response Measurement System

    Get PDF
    Data collection is crucial for researchers, as it can provide important insights for describing phenomena. In acoustics, acoustic phenomena are characterized by Room Impulse Responses (RIRs) occurring when sound propagates in a room. Room impulse responses are needed in vast quantities for various reasons, including the prediction of acoustical parameters and the rendering of virtual acoustical spaces. Recently, mobile robots navigating within indoor spaces have become increasingly used to acquire information about its environment. However, little research has attempted to utilize robots for the collection of room acoustic data. This thesis presents an adaptable automated system to measure room impulse responses in multi-room environments, using mobile and stationary measurement platforms. The system, known as Autonomous Impulse Response Measurement System (AIRMS), is divided into two stages: data collection and post-processing. To automate data collection, a mobile robotic platform was developed to perform acoustic measurements within a room. The robot was equipped with spatial microphones, multiple loudspeakers and an indoor localization system, which reported real time location of the robot. Additionally, stationary platforms were installed in specific locations inside and outside the room. The mobile and stationary platforms wirelessly communicated with one another to perform the acoustical tests systematically. Since a major requirement of the system is adaptability, researchers can define the elements of the system according to their needs, including the mounted equipment and the number of platforms. Post-processing included extraction of sine sweeps and the calculation of impulse responses. Extraction of the sine sweeps refers to the process of framing every acoustical test signal from the raw recordings. These signals are then processed to calculate the room impulse responses. The automatically collected information was complemented with manually produced data, which included rendering of a 3D model of the room, a panoramic picture. The performance of the system was tested under two conditions: a single-room and a multiroom setting. Room impulse responses were calculated for each of the test conditions, representing typical characteristics of the signals and showing the effects of proximity from sources and receivers, as well as the presence of boundaries. This prototype produces RIR measurements in a fast and reliable manner. Although some shortcomings were noted in the compact loudspeakers used to produce the sine sweeps and the accuracy of the indoor localization system, the proposed autonomous measurement system yielded reasonable results. Future work could expand the amount of impulse response measurements in order to further refine the artificial intelligence algorithms

    Anchor Self-Calibrating Schemes for UWB based Indoor Localization

    Get PDF
    Traditional indoor localization techniques that use Received Signal Strength or Inertial Measurement Units for dead-reckoning suffer from signal attenuation and sensor drift, resulting in inaccurate position estimates. Newly available Ultra-Wideband radio modules can measure distances at a centimeter-level accuracy while mitigating the effects of multipath propagation due to their very fine time resolution. Known locations of fixed anchor nodes are required to determine the position of tag nodes within an indoor environment. For a large system consisting of several anchor nodes spanning a wide area, physically mapping out the locations of each anchor node is a tedious task and thus makes the scalability of such systems difficult. Hence it is important to develop indoor localization systems wherein the anchors can self-calibrate by determining their relative positions in Euclidean 3D space with respect to each other. In this thesis, we propose two novel anchor self-calibrating algorithms - Triangle Reconstruction Algorithm (TRA) and Channel Impulse Response Positioning (CIRPos) that improve upon existing range-based implementations and solve existing problems such as flip ambiguity and node localization success rate. The localization accuracy and scalability of the self-calibrating anchor schemes are tested in a simulated environment based on the ranging accuracy of the Ultra-Wideband modules

    Bidirectional UWB Localization: A Review on an Elastic Positioning Scheme for GNSS-deprived Zones

    Full text link
    A bidirectional Ultra-Wideband (UWB) localization scheme is one of the three widely deployed design integration processes ordinarily destined for time-based UWB positioning systems. The key property of the bidirectional UWB localization is its ability to serve both the navigation and tracking assignments on-demand within a single localization scheme. Conventionally, the perspective of navigation and tracking in wireless localization systems is viewed distinctly as an individual system because different methodologies were required for the implementation process. The ability to flexibly or elastically combine two unique positioning perspectives (i.e., navigation and tracking) within a single scheme is a paradigm shift in the way location-based services are observed. Thus, this article addresses and pinpoints the potential of a bidirectional UWB localization scheme. Regarding this, the complete system model of the bidirectional UWB localization scheme was comprehensively described based on modular processes in this article. The demonstrative evaluation results based on two system integration processes as well as a SWOT (strengths, weaknesses, opportunities, and threats) analysis of the scheme were also discussed. Moreover, we argued that the presented bidirectional scheme can also be used as a prospective topology for the realization of precise location estimation processes in 5G/6G wireless mobile networks, as well as Wi-Fi fine-time measurement-based positioning systems in this article.Comment: 30 pages, 12 figure

    Ultra-wideband Based Indoor Localization of Mobile Nodes in ToA and TDoA Configurations

    Get PDF
    Zandian R. Ultra-wideband Based Indoor Localization of Mobile Nodes in ToA and TDoA Configurations. Bielefeld: Universität Bielefeld; 2019.This thesis discusses the utilization of ultra-wideband (UWB) technology in indoor localization scenarios and proposes system setup and evaluates different localization algorithms in order to improve the localization accuracy and stability of such systems in non-ideal conditions of the indoor environment. Recent developments and advances of technology in the areas of ubiquitous Internet, robotics and internet of things (IoT) have resulted in emerging new application areas in daily life in which localization systems are vital. The significant demand for a robust and accurate localization system that is applicable in indoor areas lacking satellites link, can be sensed. The UWB technology offers accurate localization systems with an accuracy of below 10 cm and covering the range of up to a few hundred meters thanks to their dedicated large bandwidth, modulation technique and signal power. In this thesis, the technology behind the UWB systems is discussed in detail. In terms of localization topologies, different scenarios with the focus on time-based methods are introduced. The main focus of this thesis is on the differential time of arrival localization systems (TDoA) with unilateral constellation that is suitable for robotic localization and navigation applications. A new approach for synchronization of TDoA topology is proposed and influence of clock inaccuracies in such systems are thoroughly evaluated. For localization engine, two groups of static and dynamic iterative algorithms are introduced. Among the possible dynamic methods, extended Kalman filter (EKF), H∞ and unscented Kalman filter (UKF) are discussed and meticulously evaluated. In order to tackle the non-line of sight (NLOS) problem of such systems, for detection stage several solutions which are based on parametric machine learning methods are proposed. Furthermore, for mitigation phase two solutions namely adjustment of measurement variance and innovation term are suggested. Practical results prove the efficiency and high reliability of the proposed algorithms with positive NLOS condition detection rate of more than 87%. In practical trials, the localization system is evaluated in indoor and outdoor arenas in both line of sight and non-line of sight conditions. The results show that the proposed detection and mitigation methods can be successfully applied for both small and large-scale arenas with the higher performance of the localization filters in terms of accuracy in large-scale scenarios

    Models and Algorithms for Ultra-Wideband Localization in Single- and Multi-Robot Systems

    Get PDF
    Location is a piece of information that empowers almost any type of application. In contrast to the outdoors, where global navigation satellite systems provide geo-spatial positioning, there are still millions of square meters of indoor space that are unaccounted for by location sensing technology. Moreover, predictions show that people’s activities are likely to shift more and more towards urban and indoor environments– the United Nations predict that by 2020, over 80% of the world’s population will live in cities. Meanwhile, indoor localization is a problem that is not simply solved: people, indoor furnishings, walls and building structures—in the eyes of a positioning sensor, these are all obstacles that create a very challenging environment. Many sensory modalities have difficulty in overcoming such harsh conditions when used alone. For this reason, and also because we aim for a portable, miniaturizable, cost-effective solution, with centimeter-level accuracy, we choose to solve the indoor localization problem with a hybrid approach that consists of two complementary components: ultra-wideband localization, and collaborative localization. In pursuit of the final, hybrid product, our research leads us to ask what benefits collaborative localization can provide to ultra-wideband localization—and vice versa. The road down this path includes diving into these orthogonal sub-domains of indoor localization to produce two independent localization solutions, before finally combining them to conclude our work. As for all systems that can be quantitatively examined, we recognize that the quality of our final product is defined by the rigor of our evaluation process. Thus, a core element of our work is the experimental setup, which we design in a modular fashion, and which we complexify incrementally according to the various stages of our studies. With the goal of implementing an evaluation system that is systematic, repeatable, and controllable, our approach is centered around the mobile robot. We harness this platform to emulate mobile targets, and track it in real-time with a highly reliable ground truth positioning system. Furthermore, we take advantage of the miniature size of our mobile platform, and include multiple entities to form a multi-robot system. This augmented setup then allows us to use the same experimental rigor to evaluate our collaborative localization strategies. Finally, we exploit the consistency of our experiments to perform cross-comparisons of the various results throughout the presented work. Ultra-wideband counts among the most interesting technologies for absolute indoor localization known to date. Owing to its fine delay resolution and its ability to penetrate through various materials, ultra-wideband provides a potentially high ranging accuracy, even in cluttered, non-line-of-sight environments. However, despite its desirable traits, the resolution of non-line-of-sight signals remains a hard problem. In other words, if a non-line-of-sight signal is not recognized as such, it leads to significant errors in the position estimate. Our work improves upon state-of-the-art by addressing the peculiarities of ultra-wideband signal propagation with models that capture the spatiality as well as the multimodal nature of the error statistics. Simultaneously, we take care to develop an underlying error model that is compact and that can be calibrated by means of efficient algorithms. In order to facilitate the usage of our multimodal error model, we use a localization algorithm that is based on particle filters. Our collaborative localization strategy distinguishes itself from prior work by emphasizing cost-efficiency, full decentralization, and scalability. The localization method is based on relative positioning and uses two quantities: relative range and relative bearing. We develop a relative robot detection model that integrates these measurements, and is embedded in our particle filter based localization framework. In addition to the robot detection model, we consider an algorithmic component, namely a reciprocal particle sampling routine, which is designed to facilitate the convergence of a robot’s position estimate. Finally, in order to reduce the complexity of our collaborative localization algorithm, and in order to reduce the amount of positioning data to be communicated between the robots, we develop a particle clustering method, which is used in conjunction with our robot detection model. The final stage of our research investigates the combined roles of collaborative localization and ultra-wideband localization. Numerous experiments are able to validate our overall localization strategy, and show that the performance can be significantly improved when using two complementary sensory modalities. Since the fusion of ultra-wideband positioning sensors with exteroceptive sensors has hardly been considered so far, our studies present pioneering work in this domain. Several insights indicate that collaboration—even if through noisy sensors—is a useful tool to reduce localization errors. In particular, we show that our collaboration strategy can provide the means to minimize the localization error, given that the collaborative design parameters are optimally tuned. Our final results show median localization errors below 10 cm in cluttered environments
    • …
    corecore