IEEE Journal of Indoor and Seamless Positioning and Navigation最新文献

{"title":"Evaluation of Sparse Acoustic Array Geometries for the Application in Indoor Localization","authors":"Georg K.J. Fischer;Niklas Thiedecke;Thomas Schaechtle;Andrea Gabbrielli;Fabian Höflinger;Alexander Stolz;Stefan J. Rupitsch","doi":"10.1109/JISPIN.2024.3476011","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3476011","url":null,"abstract":"Angle-of-arrival (AoA) estimation technology, with its potential advantages, emerges as an intriguing choice for indoor localization. Notably, it holds the promise of reducing installation costs. In contrast to time-of-flight (ToF)/time-difference-of-arrival (TDoA) based systems, AoA-based approaches require a reduced number of nodes for effective localization. This characteristic establishes a tradeoff between installation costs and the complexity of hardware and software. Moreover, the appeal of acoustic localization is further heightened by its capacity to provide cost-effective hardware solutions while maintaining a high degree of accuracy. Consequently, acoustic AoA estimation technology stands out as a feasible and compelling option in the field of indoor localization. Sparse arrays additionally have the ability to estimate the direction-of-arrival (DoA) of more sources than available sensors by placing sensors in a specific geometry. In this contribution, we introduce a measurement platform designed to evaluate various sparse array geometries experimentally. The acoustic microphone array comprises 64 microphones arranged in an 8×8 grid, following an uniform rectangular array (URA) configuration, with a grid spacing of 8.255 mm. This configuration achieves a spatial Nyquist frequency of approximately 20.8 kHz in the acoustic domain at room temperature. Notably, the array exhibits a mean spherical error of 1.26\u0000<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula>\u0000 when excluding higher elevation angles. The platform allows for masking sensors to simulate sparse array configurations. We assess four array geometries through simulations and experimental data, identifying the open-box and nested array geometries as robust candidates. In addition, we demonstrate the array's capability to concurrently estimate the directions of three emitting sources using experimental data, employing waveforms consisting of orthogonal codes.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"263-274"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10707198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Indoor Drone 3-D Tracking Using Reflected Light From Floor Surfaces","authors":"Yusei Onishi;Hiroki Watanabe;Masanari Nakamura;Hiromichi Hashizume;Masanori Sugimoto","doi":"10.1109/JISPIN.2024.3453775","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3453775","url":null,"abstract":"Because of the drone's penetration into our society, the demand for their indoor positioning has increased. However, its standard technology has not been established yet. This article describes an indoor 3-D tracking method for drones, using the drone's built-in camera to capture light reflected from the floor. Using a captured image and video data captured during the drone's flight, the proposed method can estimate the drone's position and trajectory. A drone's built-in camera is usually unable to capture light directly from ceiling light sources because of its limited field of view and gimbal angles. To address this problem, the proposed method captures the light indirectly, as the reflections from the floor of ceiling light-emitting diodes (LEDs), in the video stream acquired by its rolling-shutter camera. The 3-D position is estimated by calculating the received signal strength of each individual LED for a single video frame during the flight and fitting this data to a model generated by simulation images. In an indoor environment without external lights, we captured the reflected light from floor surfaces using the drone's camera under gimbal control and analyzed the captured images offline. Experimental results gave an absolute error of 0.34 m at the 90th percentile for 3-D positioning when hovering and using a single-frame image. For a linear flight path, the error was 0.31 m. The computation time for 3-D position estimation was 1.12 s. We also discussed limitations related to real-time and real-world applications, together with approaches to addressing these limitations.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"251-262"},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10664003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Employing an Embedded Renderer as Recognition Tool for Odometry, Map-Building, Navigation, and Localization on Active Sensing Robotics","authors":"Park Kunbum;Tsuchiya Takeshi","doi":"10.1109/JISPIN.2024.3433671","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3433671","url":null,"abstract":"This study proposes a method that employs a renderer as a tool for environmental recognition. In the proposed system, features are extracted from sensors and cameras; the renderer represents scenes in a 3-D space to suit the purpose of the applications, and the applications resample the scenes to achieve their purpose after manipulating the renderer. As an example, this study presents implementation mechanisms of environmental recognition—odometry, map-building, navigation, and localization of automotive indoor robots. This method has a higher computational cost than typical feature-based methods; however, the algorithms are considerably intuitive. Although commercial rendering engines cannot be used as they are, a lightweight rendering engine dedicated to recognition can operate in embedded systems to enable real-time recognition. In addition, this study presents an experiment that corresponds to the simulation of moving robots indoors. In conclusion, this study proposes a change from the perspective of adopting a renderer–a well-established software technology that has been thoroughly investigated and can manipulate space–as an essential tool in the recognition framework.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"275-291"},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10609476","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization","authors":"Ilayda Yaman;Guoda Tian;Erik Tegler;Jens Gulin;Nikhil Challa;Fredrik Tufvesson;Ove Edfors;Kalle Åström;Steffen Malkowsky;Liang Liu","doi":"10.1109/JISPIN.2024.3429110","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3429110","url":null,"abstract":"In this article, we present a unique comparative analysis, and evaluation of vision-, radio-, and audio-based localization algorithms. We create the first baseline for the aforementioned sensors using the recently published Lund University Vision, Radio, and Audio dataset, where all the sensors are synchronized and measured in the same environment. Some of the challenges of using each specific sensor for indoor localization tasks are highlighted. Each sensor is paired with a current state-of-the-art localization algorithm and evaluated for different aspects: localization accuracy, reliability and sensitivity to environment changes, calibration requirements, and potential system complexity. Specifically, the evaluation covers the Oriented FAST and Rotated BRIEF simultaneous localization and mapping (SLAM) algorithm for vision-based localization with an RGB-D camera, a machine learning algorithm for radio-based localization with massive multiple-input multiple-output (MIMO) technology, and the StructureFromSound2 algorithm for audio-based localization with distributed microphones. The results can serve as a guideline and basis for further development of robust and high-precision multisensory localization systems, e.g., through sensor fusion, and context- and environment-aware adaptations.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"240-250"},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10599608","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Velocity-Based Channel Charting With Spatial Distribution Map Matching","authors":"Maximilian Stahlke;George Yammine;Tobias Feigl;Bjoern M. Eskofier;Christopher Mutschler","doi":"10.1109/JISPIN.2024.3424768","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3424768","url":null,"abstract":"Radio fingerprinting (FP) technologies improve localization performance in challenging non-line-of-sight environments. However, FP is expensive as its life cycle management requires recording reference signals for initial training and when the environment changes. Instead, novel channel charting technologies are significantly cheaper. Because they implicitly assign relative coordinates to radio signals, they require few reference coordinates for localization. However, even channel charting still requires data acquisition and reference signals, and its localization is slightly less accurate than FP. In this article, we propose a novel channel charting framework that does not require references and dramatically reduces life-cycle management. With velocity information, e.g., pedestrian dead reckoning or odometry, we model relative charts. And with topological map information, e.g., building floor plans, we transform them into real coordinates. In a large-scale study, we acquired two realistic datasets using 5G and single-input and multiple-output distributed radio systems with noisy velocities and coarse map information. Our experiments show that we achieve the localization accuracy of FP but without reference information.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"230-239"},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10591331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating Multipath Component Delays With Transformer Models","authors":"Jonathan Ott;Maximilian Stahlke;Tobias Feigl;Christopher Mutschler","doi":"10.1109/JISPIN.2024.3422908","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3422908","url":null,"abstract":"Multipath in radio propagation provides essential environmental information that is exploited for positioning or channel-simultaneous localization and mapping. This enables accurate and robust localization that requires less infrastructure than traditional methods. A key factor is the reliable and accurate extraction of multipath components (MPCs). However, limited bandwidth and signal fading make it difficult to detect and determine the parameters of the individual signal components. In this article, we propose multipath delay estimation based on a transformer neural network. In contrast to the state of the art, we implicitly estimate the number of MPCs and achieve subsample accuracy without using computationally intensive super-resolution techniques. Our approach outperforms known methods in detection performance and accuracy at different bandwidths. Our ablation study shows exceptional results on simulated and real datasets and generalizes to unknown radio environments.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"219-229"},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10584252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UWB Positioning Integrity Estimation Using Ranging Residuals and ML Augmented Filtering","authors":"Mihkel Tommingas;Muhammad Mahtab Alam;Ivo Müürsepp;Sander Ulp","doi":"10.1109/JISPIN.2024.3418296","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3418296","url":null,"abstract":"This article investigates the use of ultrawideband (UWB) ranging residuals for coordinate integrity estimation and their use in a filtering scheme. Typically, UWB system accuracy is improved using channel statistics (CSs) to detect and mitigate non-line-of-sight effects between UWB sensors and the object to be located, potentially improving the end coordinate solution. However, in practice, when considering UWB system with a high positioning update rate, this is not a feasible approach, as gathering and processing CS data takes too much time. In contrast to this approach, this article proposes a set of features based on UWB ranging residuals that could be used as an alternative in integrity assessment. By using machine learning (ML), the most important features were extracted from the initial set, and then, used to train and validate a model for UWB coordinate error prediction. Finally, the prediction was applied in an adaptive Kalman filtering scheme as an input for measurement uncertainty. Model testing was done using UWB measurement test dataset gathered at an industrial site. The overall results showed significant improvement in 2-D and 3-D positioning metrics of ML-augmented filtering when compared to non-ML filtering. On average, the end coordinates in the test set had ca. 10 cm smaller mean location error and ca. 40 cm smaller dispersion in 2-D positioning. In addition, the presence of outliers was reduced significantly as the maximum error offset decreased by several meters. Although ML augmented filtering is computationally slower than non-ML filtering (e.g., ordinary and extended Kalman filter), it is still faster than using CS for UWB integrity estimation. The results show that using the proposed residual features in an ML model provides a feasible approach to predict UWB positioning integrity and use it as a measure of uncertainty in a coordinate filtering scheme.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"205-218"},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10568925","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Wi-Fi RSS-RTT Indoor Positioning Model Based on Dynamic Model Switching Algorithm","authors":"Xu Feng;Khuong An Nguyen;Zhiyuan Luo","doi":"10.1109/JISPIN.2024.3385356","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3385356","url":null,"abstract":"The advances in Wi-Fi technology have encouraged the development of numerous indoor positioning systems. However, their performance varies significantly across different indoor environments, making it challenging to identify the most suitable system for all scenarios. To address this challenge, we propose an algorithm that dynamically selects the most optimal Wi-Fi positioning model for each location. Our algorithm employs a machine learning weighted model selection algorithm trained on raw Wi-Fi received signal strength (RSS), raw Wi-Fi round-trip time (RTT) data, statistical RSS and RTT measures, and access point line-of-sight information. We tested our algorithm in four complex indoor environments, and compared its performance to traditional Wi-Fi indoor positioning models and state-of-the-art stacking models, demonstrating an improvement of up to 1.8 m on average.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"151-165"},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10493073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140813902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ubiquitous UWB Ranging Error Mitigation With Application to Infrastructure-Free Cooperative Positioning","authors":"Maija Mäkelä;Martta-Kaisa Olkkonen;Martti Kirkko-Jaakkola;Toni Hammarberg;Tuomo Malkamäki;Jesperi Rantanen;Sanna Kaasalainen","doi":"10.1109/JISPIN.2024.3384909","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3384909","url":null,"abstract":"Ultra wideband (UWB) signals are a promising choice for indoor positioning applications, since they are able to penetrate walls to a certain extent. Nevertheless, signal reflections and non-line-of-sight propagation cause bias in the measured range. This ranging error can be corrected with machine learning (ML) methods, such as convolutional neural networks (CNNs). However, these ML models often generalize poorly between different environments. In this work we present an instance-based transfer learning (TL) approach, that enables generalizing a CNN-based ranging error mitigation approach to a new situation with only a few unlabeled training samples. The performance of the UWB error correction approach is demonstrated in a real-life infrastructure-free cooperative positioning setting.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"143-150"},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10490099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140641622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-Localization Method Using a Single Acoustic Ranging Sensor Based on Impulse Response and Doppler Effect","authors":"Atsushi Tsuchiya;Naoto Wakatsuki;Tadashi Ebihara;Keiichi Zempo;Koichi Mizutani","doi":"10.1109/JISPIN.2024.3403519","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3403519","url":null,"abstract":"This study aims to realize self-position estimation for indoor robots using only a single acoustic channel. When a single omnidirectional transmitter/receiver is used as an object detection sensor, detected objects are identified on concentric circles with the transmitter/receiver as the center point. Self-position estimation method using this sensor cannot use the directional information of the detected object. This fact makes it impossible to specify the robot's turning angle using environmental information. In this article, we propose a self-position estimation method using a single omnidirectional transmitter/receiver that can consider the direction of the reflected object by estimating the direction of the reflected wave from the Doppler effect generated during the robot's movement. The self-position estimation was implemented by using echo images of the direction of arrival of sound waves estimated from the Doppler effect and the distance of arrival of sound waves estimated from the impulse response and matching them with a previously generated map image. The accuracy of the proposed method was evaluated by simulation and experiment. In the simulation, an average position estimation error of 0.042 m was achieved; in the experiment, it was 0.051 m. Furthermore, experimental and simulation results show that using the Doppler effect contributes to self-position estimation accuracy.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"193-204"},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10535727","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141474966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}