scholarly journals Accurate Localization in Acoustic Underwater Localization Systems

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 762
Author(s):  
Gianni Cario ◽  
Alessandro Casavola ◽  
Gianfranco Gagliardi ◽  
Marco Lupia ◽  
Umberto Severino
Keyword(s):  
Statistical Analysis ◽  
Measurement Unit ◽  
Design Parameters ◽  
Final Position ◽  
Localization System ◽  
Sources Of Error ◽  
Accurate Localization ◽  
Hardware System ◽  
Underwater Localization ◽  
Localization Systems

In underwater localization systems several sources of error may impact in different ways the accuracy of the final position estimates. Through simulations and statistical analysis it is possible to identify and characterize such sources of error and their relative importance. This is especially of use when an accurate localization system has to be designed within required accuracy prescriptions. This approach allows one to also investigate how much these sources of error influence the final position estimates achieved by an Extended Kalman Filter (EKF). This paper presents the results of experiments designed in a virtual environment used to simulate real acoustic underwater localization systems. The paper intends to analyze the main parameters that significantly influence the position estimates achieved by a Short Baseline (SBL) system. Specifically, the results of this analysis are presented for a proprietary localization system constituted by a surface platform equipped with four acoustic transducers used for the localization of an underwater target. The simulator here presented has the purpose of simulating the hardware system and modifying some of its design parameters, such as the base-line length and the errors on the GPS and Inertial Measurement Unit (IMU) units, in order to understand which parameters have to modify for improving the accuracy of the entire positioning system. It is shown that statistical analysis techniques can be of help in determining the best values of these parameters that permit to improve the performance of a real hardware system.

scholarly journals Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5084 ◽  
Author(s):  
Alwin Poulose ◽  
Dong Seog Han
Keyword(s):  
Hybrid System ◽  
Indoor Localization ◽  
Simultaneous Localization And Mapping ◽  
Maximum Error ◽  
Measurement Unit ◽  
Localization Error ◽  
Positioning Systems ◽  
Localization System ◽  
Localization And Mapping ◽  
Localization Systems

Smartphone camera or inertial measurement unit (IMU) sensor-based systems can be independently used to provide accurate indoor positioning results. However, the accuracy of an IMU-based localization system depends on the magnitude of sensor errors that are caused by external electromagnetic noise or sensor drifts. Smartphone camera based positioning systems depend on the experimental floor map and the camera poses. The challenge in smartphone camera-based localization is that accuracy depends on the rapidness of changes in the user’s direction. In order to minimize the positioning errors in both the smartphone camera and IMU-based localization systems, we propose hybrid systems that combine both the camera-based and IMU sensor-based approaches for indoor localization. In this paper, an indoor experiment scenario is designed to analyse the performance of the IMU-based localization system, smartphone camera-based localization system and the proposed hybrid indoor localization system. The experiment results demonstrate the effectiveness of the proposed hybrid system and the results show that the proposed hybrid system exhibits significant position accuracy when compared to the IMU and smartphone camera-based localization systems. The performance of the proposed hybrid system is analysed in terms of average localization error and probability distributions of localization errors. The experiment results show that the proposed oriented fast rotated binary robust independent elementary features (BRIEF)-simultaneous localization and mapping (ORB-SLAM) with the IMU sensor hybrid system shows a mean localization error of 0.1398 m and the proposed simultaneous localization and mapping by fusion of keypoints and squared planar markers (UcoSLAM) with IMU sensor-based hybrid system has a 0.0690 m mean localization error and are compared with the individual localization systems in terms of mean error, maximum error, minimum error and standard deviation of error.

scholarly journals The Influence of Design Parameters on Straight Bevel Gear Dynamics

2015 ◽  
Vol 5 (2) ◽  
pp. 114
Author(s):  
Chiu-Fan Hsieh ◽  
You-Qing Zhu
Keyword(s):  
Statistical Analysis ◽  
The Other ◽  
Design Parameters ◽  
Bevel Gears ◽  
Straight Bevel Gear ◽  
Pressure Angle ◽  
Stress Fluctuation ◽  
Shaft Angle ◽  
Statistical Results ◽  
Component Force

<p class="1Body">This study analyzes the influence of design parameters on the dynamics of straight bevel gears by constructing a model that allows variation in the shaft angle, pressure angle, and backlash. According to the statistical analysis, the order of influence of these parameters on weight is shaft angle &gt; pressure angle &gt; backlash. When the shaft angle is 90°, the statistical results show the drive is stable and the stress fluctuation level is low. The pressure angle, on the other hand, can affect the gear’s dynamic property by influencing the driving component force on the gear and the component force on the shaft. The results for the shaft and pressure angles are used to determine the appropriate backlash. Overall, the analysis not only provides designers with an important reference but explains the dominance in the market of gear designs with a 90° shaft angle and a 20° pressure angle.</p>

scholarly journals Barcode based localization system in indoor environment

2014 ◽  
Vol 13 ◽  
pp. 31-36
Author(s):  
Ľubica Ilkovičová ◽  
Ján Erdélyi ◽  
Alojz Kopáčik
Keyword(s):  
Indoor Environment ◽  
Operating Principle ◽  
Quick Response ◽  
Qr Codes ◽  
Localization System ◽  
Accurate Localization ◽  
Intelligent Buildings

Nowadays, in the era of intelligent buildings, there is a need to create indoornavigation systems, what is steadily a challenge. QR (Quick Response) codesprovide accurate localization also in indoor environment, where other navigationtechniques (e.g. GPS) are not available. The paper deals with the issues of posi-tioning using QR codes, solved at the Department of Surveying, Faculty of CivilEngineering SUT in Bratislava. Operating principle of QR codes, description ofthe application for positioning in indoor environment based on OS Android forsmartphones are described.

scholarly journals DSCP: Depthwise Separable Convolution-Based Passive Indoor Localization Using CSI Fingerprint

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chong Han ◽  
Wenjing Xun ◽  
Lijuan Sun ◽  
Zhaoxiao Lin ◽  
Jian Guo
Keyword(s):  
Indoor Localization ◽  
Wireless Sensing ◽  
Training Phase ◽  
Training Time ◽  
Localization Accuracy ◽  
Localization System ◽  
Complex Models ◽  
Indoor Scenarios ◽  
Trained Network ◽  
Localization Systems

Wi-Fi-based indoor localization has received extensive attention in wireless sensing. However, most Wi-Fi-based indoor localization systems have complex models and high localization delays, which limit the universality of these localization methods. To solve these problems, a depthwise separable convolution-based passive indoor localization system (DSCP) is proposed. DSCP is a lightweight fingerprint-based localization system that includes an offline training phase and an online localization phase. In the offline training phase, the indoor scenario is first divided into different areas to set training locations for collecting CSI. Then, the amplitude differences of these CSI subcarriers are extracted to construct location fingerprints, thereby training the convolutional neural network (CNN). In the online localization phase, CSI data are first collected at the test locations, and then, the location fingerprint is extracted and finally fed to the trained network to obtain the predicted location. The experimental results show that DSCP has a short training time and a low localization delay. DSCP achieves a high localization accuracy, above 97%, and a small median localization distance error of 0.69 m in typical indoor scenarios.

scholarly journals End-to-End Sequential Indoor Localization Using Smartphone Inertial Sensors and WiFi

2021 ◽  
Author(s):  
Mukhamet Nurpeiissov ◽  
Askat Kuzdeuov ◽  
Aslan Assylkhanov, ◽  
Yerbolat Khassanov ◽  
Hüseyin Atakan Varol
Keyword(s):  
Indoor Localization ◽  
Inertial Sensors ◽  
Sampling Rate ◽  
Reference Points ◽  
Measurement Unit ◽  
Localization System ◽  
Practical Applications ◽  
External Data ◽  
End To End ◽  
Commercial Off The Shelf

This paper addresses sequential indoor localization using WiFi and Inertial Measurement Unit (IMU) modules commonly found in commercial off-the-shelf smartphones. Specifically, we developed an end-to-end neural network-based localization system integrating WiFi received signal strength indicator (RSSI) and IMU data without external data fusion models. The developed system leverages the advantages of WiFi and IMU modules to locate finer-level sequential positions of a user at 150 Hz sampling rate. Additionally, to demonstrate the efficacy of the proposed approach, we created the IMUWiFine dataset comprising IMU and WiFi RSSI readings sequentially collected at fine-level reference points. The dataset contains 120 trajectories covering an aggregate distance of over 14 kilometers. We conducted extensive experiments using deep learning models and achieved a mean error distance of 1.1 meters on an unseen evaluation set, which makes our approach suitable for many practical applications requiring meter-level accuracy. To enable experiment and result reproducibility, we made the developed localization system and IMUWiFine dataset publicly available in our GitHub repository.<br>

Reducing Tracking Error in RFID Real-Time Localization Systems Using Kalman Filters

2014 ◽  
Vol 5 (3) ◽  
pp. 1-24
Author(s):  
Benjamin Sanda ◽  
Ikhlas Abdel-Qader ◽  
Abiola Akanmu
Keyword(s):  
Real Time ◽  
Radio Frequency Identification ◽  
Kalman Filters ◽  
Tracking Error ◽  
Time Of Arrival ◽  
Indoor Environments ◽  
Real World Data ◽  
Localization Accuracy ◽  
Accurate Localization ◽  
Localization Systems

The use of Radio Frequency Identification (RFID) has become widespread in industry as a means to quickly and wirelessly identify and track packages and equipment. Now there is a commercial interest in using RFID to provide real-time localization. Efforts to use RFID technology in this way experience localization errors due to noise and multipath effects inherent to these environments. This paper presents the use of both linear Kalman filters and non-linear Unscented Kalman filters to reduce the error rate inherent to real-time RFID localization systems and provide more accurate localization results in indoor environments. A commercial RFID localization system designed for use by the construction industry is used in this work, and a filtering model based on 3rd order motion is developed. The filtering model is tested with real-world data and shown to provide an increase in localization accuracy when applied to both raw time of arrival measurements as well as final localization results.

scholarly journals Lane-level localization system using surround-view cameras adaptive to different driving conditions

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142092163
Author(s):  
Tianyi Li ◽  
Yuhan Qian ◽  
Arnaud de La Fortelle ◽  
Ching-Yao Chan ◽  
Chunxiang Wang
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Lateral Position ◽  
Measurement Unit ◽  
Map Matching ◽  
Matching Model ◽  
Localization System ◽  
Perceptual Model ◽  
Road Map ◽  
Driving Conditions

This article presents a lane-level localization system adaptive to different driving conditions, such as occlusions, complicated road structures, and lane-changing maneuvers. The system uses surround-view cameras, other low-cost sensors, and a lane-level road map which suits for mass deployment. A map-matching localizer is proposed to estimate the probabilistic lateral position. It consists of a sub-map extraction module, a perceptual model, and a matching model. A probabilistic lateral road feature is devised as a sub-map without limitations of road structures. The perceptual model is a deep learning network that processes raw images from surround-view cameras to extract a local probabilistic lateral road feature. Unlike conventional deep-learning-based methods, the perceptual model is trained by auto-generated labels from the lane-level map to reduce manual effort. The matching model computes the correlation between the sub-map and the local probabilistic lateral road feature to output the probabilistic lateral estimation. A particle-filter-based framework is developed to fuse the output of map-matching localizer with the measurements from wheel speed sensors and an inertial measurement unit. Experimental results demonstrate that the proposed system provides the localization results with submeter accuracy in different driving conditions.

Dynamic License Plate Localization for Vehicles on Multi-lane Using Single Camera

2013 ◽  
Vol 300-301 ◽  
pp. 740-745
Author(s):  
Hung Li Tseng ◽  
Chao Nan Hung ◽  
Chiu Ching Tuan ◽  
You Ru Wen ◽  
Wen Tzeng Huang ◽  
...  
Keyword(s):  
High Resolution ◽  
Recognition System ◽  
License Plate ◽  
Single Camera ◽  
License Plate Recognition ◽  
Hardware Cost ◽  
Localization System ◽  
Parking Management ◽  
Toll Collection ◽  
Localization Systems

LPR (License Plate Recognition) System has been widely used in highway toll collection, parking management, various traffic regulations enforcement and other systems. Currently, most of the existing LPL (license plate localization) systems are with single camera that is limited to recognizing vehicles in one lane. In this paper we design a license plate localization system that simultaneously recognizes license plates of vehicles on multi-lane by using single high-resolution camera. Our approach significantly reduces the hardware cost of LPR system without sacrificing the accuracy of recognition. And our success rate is about 94%.

scholarly journals AGV Localization System Based on Ultra-Wideband and Vision Guidance

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 448 ◽  
Author(s):  
Xiaohao Hu ◽  
Zai Luo ◽  
Wensong Jiang
Keyword(s):  
Monocular Vision ◽  
Ultra Wideband ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Localization Method ◽  
Localization System ◽  
Indoor Location ◽  
Accurate Localization ◽  
Vision Localization ◽  
Automated Storage

Aiming at the problems of low localization accuracy and complicated localization methods of the automatic guided vehicle (AGV) in the current automatic storage and transportation process, a combined localization method based on the ultra-wideband (UWB) and the visual guidance is proposed. Both the UWB localization method and the monocular vision localization method are applied to the indoor location of the AGV. According to the corner points of an ArUco code fixed on the AGV body, the monocular vision localization method can solve the pose information of the AGV by the PnP algorithm in real-time. As an auxiliary localization method, the UWB localization method is called to locate the AGV coordinates. The distance from the tag on the AGV body to the surrounding anchors is measured by the time of flight (TOF) ranging algorithm, and the actual coordinates of the AGV are calculated by the trilateral centroid localization algorithm. Then, the localization data of the UWB is corrected by the mean compensation method to obtain a consistent and accurate localization trajectory. The experiment result shows that this localization system has an error of 15mm, which meets the needs of AGV location in the process of automated storage and transportation.

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