Pub Date : 2017-06-27DOI: 10.1109/ICL-GNSS.2017.8376251
Mohammad Amin Alizadeh Khameneh, A. Jensen, M. Horemuz, J. Andersson
The establishment of leveling benchmarks for performing geodetic measurements, for instance in construction works, is usually costly and laborious due to a mass of field works in transferring the height from nearby known benchmarks. In this study, a real-time updated free station (RUFRIS) method is investigated to be used as an alternative approach for the traditional leveling. The coordinates of a RUFRIS station are determined by establishing a total station on the point, and performing a free-station by observing some points with both Real-Time Kinematic (RTK) GNSS and total station distance and direction observations. The study is conducted based on data from the East Link project in Sweden, where a 150 km long high-speed railway is to be constructed. The results show a standard deviation of 7 mm between the RUFRIS and leveling heights in this project and imply the feasibility of replacing the traditional leveling methods with RUFRIS in projects with low accessibility to benchmarks.
{"title":"Investigation of the RUFRIS method with GNSS and total station for leveling","authors":"Mohammad Amin Alizadeh Khameneh, A. Jensen, M. Horemuz, J. Andersson","doi":"10.1109/ICL-GNSS.2017.8376251","DOIUrl":"https://doi.org/10.1109/ICL-GNSS.2017.8376251","url":null,"abstract":"The establishment of leveling benchmarks for performing geodetic measurements, for instance in construction works, is usually costly and laborious due to a mass of field works in transferring the height from nearby known benchmarks. In this study, a real-time updated free station (RUFRIS) method is investigated to be used as an alternative approach for the traditional leveling. The coordinates of a RUFRIS station are determined by establishing a total station on the point, and performing a free-station by observing some points with both Real-Time Kinematic (RTK) GNSS and total station distance and direction observations. The study is conducted based on data from the East Link project in Sweden, where a 150 km long high-speed railway is to be constructed. The results show a standard deviation of 7 mm between the RUFRIS and leveling heights in this project and imply the feasibility of replacing the traditional leveling methods with RUFRIS in projects with low accessibility to benchmarks.","PeriodicalId":330366,"journal":{"name":"2017 International Conference on Localization and GNSS (ICL-GNSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129855140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-27DOI: 10.1109/ICL-GNSS.2017.8376257
Pekka Peltola, C. Hill, T. Moore
Seamless navigation requires that the mobile device is capable of offering a position solution both indoors and outdoors. Novel seamless navigation system design was implemented and tested to achieve this aim. The design consists of general navigation system framework blocks and of the necessary interface agreements between the blocks. This approach enables plug-and-play style design of modules. The implementation used four preselected key technologies. Microstrain 3DM-GX4-45 foot-mounted inertial measurement unit sensor data was fused together with the u-blox GNSS receiver positions outdoors. Context sensitive inference engine enabled the fusion of position updates indoors from the De-cawave TREK1000 Ultra WideBand ranging kit and from the 6 Kontakt.io/Raspberry Pi anchor-based Bluetooth low energy fingeprinting system. Novel dual-mode filter design uses a particle filter and the pentagon buffer enhanced Kalman filter in the position solution derivation. Depending on the map and the walls in the environment and on the quality of position updates, the implemented control logic employs the most fit filter for the current context. Computational power is now focussed, when particle filter is needed. The novel pentagon buffer enhanced Kalman filter is 10 times faster, allowing power saving when situation is not too critical. Moreover, the buffer provides position updates by interacting with the map and helps to correct the position solution. The navigation system is seamless according to the tests conducted around and within the Nottingham Geospatial building. No user input is needed for smooth transition from outdoors to indoors and vice versa. The system achieves an accuracy of 2.35 m outdoors and 1.4 m indoors (95% of error). Inertial system availability was continuous, while GNSS was available outdoors and BLE and UWB indoors.
{"title":"Adaptive real-time dual-mode filter design for seamless pedestrian navigation","authors":"Pekka Peltola, C. Hill, T. Moore","doi":"10.1109/ICL-GNSS.2017.8376257","DOIUrl":"https://doi.org/10.1109/ICL-GNSS.2017.8376257","url":null,"abstract":"Seamless navigation requires that the mobile device is capable of offering a position solution both indoors and outdoors. Novel seamless navigation system design was implemented and tested to achieve this aim. The design consists of general navigation system framework blocks and of the necessary interface agreements between the blocks. This approach enables plug-and-play style design of modules. The implementation used four preselected key technologies. Microstrain 3DM-GX4-45 foot-mounted inertial measurement unit sensor data was fused together with the u-blox GNSS receiver positions outdoors. Context sensitive inference engine enabled the fusion of position updates indoors from the De-cawave TREK1000 Ultra WideBand ranging kit and from the 6 Kontakt.io/Raspberry Pi anchor-based Bluetooth low energy fingeprinting system. Novel dual-mode filter design uses a particle filter and the pentagon buffer enhanced Kalman filter in the position solution derivation. Depending on the map and the walls in the environment and on the quality of position updates, the implemented control logic employs the most fit filter for the current context. Computational power is now focussed, when particle filter is needed. The novel pentagon buffer enhanced Kalman filter is 10 times faster, allowing power saving when situation is not too critical. Moreover, the buffer provides position updates by interacting with the map and helps to correct the position solution. The navigation system is seamless according to the tests conducted around and within the Nottingham Geospatial building. No user input is needed for smooth transition from outdoors to indoors and vice versa. The system achieves an accuracy of 2.35 m outdoors and 1.4 m indoors (95% of error). Inertial system availability was continuous, while GNSS was available outdoors and BLE and UWB indoors.","PeriodicalId":330366,"journal":{"name":"2017 International Conference on Localization and GNSS (ICL-GNSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130438708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-27DOI: 10.1109/ICL-GNSS.2017.8376237
Yichen Du, T. Arslan
Magnetic field-based location fingerprinting techniques are emerging technologies used in indoor navigation that take advantage of magnetic field anomalies. k Nearest Neighbours (kNN) is one of the general matching algorithms that is widely used in fingerprint-based indoor positioning systems to estimate the location of users. However, the standard kNN algorithm always visits all the data in a database in order to take the appropriate nearest k neighbours into account while calculating the estimated location. One of the key disadvantages associated with kNN is the fact that computational complexity is quite large. In order to deal with this issue and improve the precision of this method, this paper proposes the use of a new method called Segmentation-based kNN algorithm. This approach conducts suitable selection and partitioning on the target positioning area before calculating the kNN. We have calculated the accuracy rate of the proposed algorithm and compared it with standard kNN algorithm, and the results show that the proposed algorithm performs better than the kNN algorithm with an improvement of 9.24% in average accuracy.
{"title":"A segmentation-based matching algorithm for magnetic field indoor positioning","authors":"Yichen Du, T. Arslan","doi":"10.1109/ICL-GNSS.2017.8376237","DOIUrl":"https://doi.org/10.1109/ICL-GNSS.2017.8376237","url":null,"abstract":"Magnetic field-based location fingerprinting techniques are emerging technologies used in indoor navigation that take advantage of magnetic field anomalies. k Nearest Neighbours (kNN) is one of the general matching algorithms that is widely used in fingerprint-based indoor positioning systems to estimate the location of users. However, the standard kNN algorithm always visits all the data in a database in order to take the appropriate nearest k neighbours into account while calculating the estimated location. One of the key disadvantages associated with kNN is the fact that computational complexity is quite large. In order to deal with this issue and improve the precision of this method, this paper proposes the use of a new method called Segmentation-based kNN algorithm. This approach conducts suitable selection and partitioning on the target positioning area before calculating the kNN. We have calculated the accuracy rate of the proposed algorithm and compared it with standard kNN algorithm, and the results show that the proposed algorithm performs better than the kNN algorithm with an improvement of 9.24% in average accuracy.","PeriodicalId":330366,"journal":{"name":"2017 International Conference on Localization and GNSS (ICL-GNSS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125457993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-27DOI: 10.1109/ICL-GNSS.2017.8376258
M. Mäkelä, S. Ali-Löytty, Philipp Müller, R. Piché
Global Navigation Satellite Systems (GNSS) enable positioning almost everywhere in the world under open-sky conditions. However, the GNSS-based position is not exact due to various error sources. Errors in range measurements caused by the ionospheric delay are the largest error source in positioning with consumer grade receivers. In this paper we propose an approach for predicting the ionospheric delay for such receivers. Our method models the ionosphere by a predefined set of trigonometric basis functions, whose coefficient are predicted using a Kalman filter (KF). For the KF we introduce a new, Klobuchar-based state model, which outperforms a standard, random walk state model in our tests with real-world data for various filter update intervals. Our tests show, furthermore, that European Geostationary Navigation Overlay Service (EGNOS) transmissions can be parameterized and predicted without significant information loss, which reduces the amount of data that has to be transmitted to the receiver.
{"title":"Parametrization and prediction of EGNOS GIVD values","authors":"M. Mäkelä, S. Ali-Löytty, Philipp Müller, R. Piché","doi":"10.1109/ICL-GNSS.2017.8376258","DOIUrl":"https://doi.org/10.1109/ICL-GNSS.2017.8376258","url":null,"abstract":"Global Navigation Satellite Systems (GNSS) enable positioning almost everywhere in the world under open-sky conditions. However, the GNSS-based position is not exact due to various error sources. Errors in range measurements caused by the ionospheric delay are the largest error source in positioning with consumer grade receivers. In this paper we propose an approach for predicting the ionospheric delay for such receivers. Our method models the ionosphere by a predefined set of trigonometric basis functions, whose coefficient are predicted using a Kalman filter (KF). For the KF we introduce a new, Klobuchar-based state model, which outperforms a standard, random walk state model in our tests with real-world data for various filter update intervals. Our tests show, furthermore, that European Geostationary Navigation Overlay Service (EGNOS) transmissions can be parameterized and predicted without significant information loss, which reduces the amount of data that has to be transmitted to the receiver.","PeriodicalId":330366,"journal":{"name":"2017 International Conference on Localization and GNSS (ICL-GNSS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131233815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-27DOI: 10.1109/ICL-GNSS.2017.8376252
Shuran Zheng, Jinling Wang
High definition (HD) map is a critical part of highly automated driving (HAD) technology and shows potential for high precision vehicle localization when GNSS signals are not available. The current study of using HD map for localiz ation is mostly based on Simultaneous Localization and Mapping (SLAM) technique, which requires high computing power, huge storage space, and quick data transmission ability. Therefore, a study of a new HD map based vehicle localization method which requires less computation is necessary. Geometry is one key component that affects the quality of localization, including accuracy, reliability, and separability. Analysing the geometry can provide reference for designing a localization system to meet the quality requirement of HAD, but is rarely studied. This paper aims to design a high precision and reliable localization system using HD map as a sensor, and the influence of geometry is also explored. Geometric strength is evaluated under different scenarios considering three factors, including feature distribution type, feature number, and distance between vehicle and feature. The results show Minimum Detectable Bias (MDB) and Minimal Separable Bias (MSB) are mostly affected by feature number and distance between vehicle and feature. Randomly distribution, more detected features and close distance between the host vehicle and the features may all contribute to good quality of vehicle position estimation.
高清晰度(HD)地图是高度自动驾驶(HAD)技术的重要组成部分，在没有GNSS信号的情况下，显示出高精度车辆定位的潜力。目前利用高清地图进行定位的研究多是基于SLAM (Simultaneous Localization and Mapping)技术，该技术要求计算能力高、存储空间大、数据传输速度快。因此，研究一种计算量较小的基于高清地图的车辆定位方法是十分必要的。几何是影响定位质量的一个关键因素，包括精度、可靠性和可分离性。分析其几何形状可以为设计满足HAD质量要求的定位系统提供参考，但研究较少。本文旨在设计一种以高清地图为传感器的高精度、可靠的定位系统，并探讨了几何形状对定位系统的影响。考虑特征分布类型、特征数量和车辆与特征之间的距离三个因素，对不同场景下的几何强度进行评估。结果表明，最小可检测偏差(MDB)和最小可分离偏差(MSB)主要受特征数量和车辆与特征之间距离的影响。随机分布、检测到的特征较多以及主车辆与特征之间的距离较近都有助于提高车辆位置估计的质量。
{"title":"High definition map-based vehicle localization for highly automated driving: Geometric analysis","authors":"Shuran Zheng, Jinling Wang","doi":"10.1109/ICL-GNSS.2017.8376252","DOIUrl":"https://doi.org/10.1109/ICL-GNSS.2017.8376252","url":null,"abstract":"High definition (HD) map is a critical part of highly automated driving (HAD) technology and shows potential for high precision vehicle localization when GNSS signals are not available. The current study of using HD map for localiz ation is mostly based on Simultaneous Localization and Mapping (SLAM) technique, which requires high computing power, huge storage space, and quick data transmission ability. Therefore, a study of a new HD map based vehicle localization method which requires less computation is necessary. Geometry is one key component that affects the quality of localization, including accuracy, reliability, and separability. Analysing the geometry can provide reference for designing a localization system to meet the quality requirement of HAD, but is rarely studied. This paper aims to design a high precision and reliable localization system using HD map as a sensor, and the influence of geometry is also explored. Geometric strength is evaluated under different scenarios considering three factors, including feature distribution type, feature number, and distance between vehicle and feature. The results show Minimum Detectable Bias (MDB) and Minimal Separable Bias (MSB) are mostly affected by feature number and distance between vehicle and feature. Randomly distribution, more detected features and close distance between the host vehicle and the features may all contribute to good quality of vehicle position estimation.","PeriodicalId":330366,"journal":{"name":"2017 International Conference on Localization and GNSS (ICL-GNSS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116501900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-27DOI: 10.1109/ICL-GNSS.2017.8376248
Xiaoyue Hou, T. Arslan
This paper presents a technique for indoor localization using the Monte Carlo localization (MCL) algorithm. The MCL was upgraded from Markov localization, with both belonging to the family of probabilistic approaches. Throughout the last decade, laser rangefinders and gyroscopes have been applied to MCL-based robotic localization systems with remarkable success. However, the utilization of MCL-based indoor localization for mobile devices, by applying Bluetooth low energy (BLE) sensors, is still being researched. In this paper, we present a technique that utilizes MCL that exploits two sensors, namely, the accelerometer and compass, with commonly deployed BLE beacons to localize people with mobile devices indoors. Experimental results illustrate that, by applying MCL with BLE beacons using an accelerometer and compass, the error of the calculated coordinates for the user position is less than 1 m in line of-sight (LOS) environments, while in a complex non-LOS environment, the average error is 3 m. Meanwhile, the proposed MCL system does not demand a high deployment density of BLE beacons compared with triangulation and trilateration-based indoor positioning algorithms.
{"title":"Monte Carlo localization algorithm for indoor positioning using Bluetooth low energy devices","authors":"Xiaoyue Hou, T. Arslan","doi":"10.1109/ICL-GNSS.2017.8376248","DOIUrl":"https://doi.org/10.1109/ICL-GNSS.2017.8376248","url":null,"abstract":"This paper presents a technique for indoor localization using the Monte Carlo localization (MCL) algorithm. The MCL was upgraded from Markov localization, with both belonging to the family of probabilistic approaches. Throughout the last decade, laser rangefinders and gyroscopes have been applied to MCL-based robotic localization systems with remarkable success. However, the utilization of MCL-based indoor localization for mobile devices, by applying Bluetooth low energy (BLE) sensors, is still being researched. In this paper, we present a technique that utilizes MCL that exploits two sensors, namely, the accelerometer and compass, with commonly deployed BLE beacons to localize people with mobile devices indoors. Experimental results illustrate that, by applying MCL with BLE beacons using an accelerometer and compass, the error of the calculated coordinates for the user position is less than 1 m in line of-sight (LOS) environments, while in a complex non-LOS environment, the average error is 3 m. Meanwhile, the proposed MCL system does not demand a high deployment density of BLE beacons compared with triangulation and trilateration-based indoor positioning algorithms.","PeriodicalId":330366,"journal":{"name":"2017 International Conference on Localization and GNSS (ICL-GNSS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121448457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-27DOI: 10.1109/ICL-GNSS.2017.8376250
W. Darwish, Wenbin Li, Shengjun Tang, Wu Chen
The conventional approach to register two or more RGB-D frames produced from low cost depth sensors, such as KINECT and Structure Sensor, applies SIFT matched points between color images along with corresponding depth from the depth images. This is known as RGB-D SLAM. This method depends on ICP concept to refine the sensor pose after estimating it from SIFT depth points. In this research, we propose a new registration method and a new description function to add line feature matching in RGB-D frame registration. The qualitative and quantitative assessments of the proposed procedure show a significant improvement in 3D model quality and precision with the proposed new registration method.
{"title":"Coarse to fine global RGB-D frames registration for precise indoor 3D model reconstruction","authors":"W. Darwish, Wenbin Li, Shengjun Tang, Wu Chen","doi":"10.1109/ICL-GNSS.2017.8376250","DOIUrl":"https://doi.org/10.1109/ICL-GNSS.2017.8376250","url":null,"abstract":"The conventional approach to register two or more RGB-D frames produced from low cost depth sensors, such as KINECT and Structure Sensor, applies SIFT matched points between color images along with corresponding depth from the depth images. This is known as RGB-D SLAM. This method depends on ICP concept to refine the sensor pose after estimating it from SIFT depth points. In this research, we propose a new registration method and a new description function to add line feature matching in RGB-D frame registration. The qualitative and quantitative assessments of the proposed procedure show a significant improvement in 3D model quality and precision with the proposed new registration method.","PeriodicalId":330366,"journal":{"name":"2017 International Conference on Localization and GNSS (ICL-GNSS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125626018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-27DOI: 10.1109/ICL-GNSS.2017.8376238
V. Eerola
This paper presents methods for optimizing matched filter based GNSS acquisition units. Matched filters become computationally complex when the reference signal length is increased. They have a fixed length in time, which also limits the achievable integration time. In this paper, we show how to design an optimal adder for a large number of single-bit values, and how this can be utilized to build matched filters for multi-bit numbers and multiple inputs. By altering the incoming sample rates, it is possible to adapt the matched filter for changing signal frequency. Employing coherent and non-coherent integration after the matched filter allows enhancing the sensitivity of signal detection with modest implementation size increase.
{"title":"Optimizing matched filters for GNSS receivers","authors":"V. Eerola","doi":"10.1109/ICL-GNSS.2017.8376238","DOIUrl":"https://doi.org/10.1109/ICL-GNSS.2017.8376238","url":null,"abstract":"This paper presents methods for optimizing matched filter based GNSS acquisition units. Matched filters become computationally complex when the reference signal length is increased. They have a fixed length in time, which also limits the achievable integration time. In this paper, we show how to design an optimal adder for a large number of single-bit values, and how this can be utilized to build matched filters for multi-bit numbers and multiple inputs. By altering the incoming sample rates, it is possible to adapt the matched filter for changing signal frequency. Employing coherent and non-coherent integration after the matched filter allows enhancing the sensitivity of signal detection with modest implementation size increase.","PeriodicalId":330366,"journal":{"name":"2017 International Conference on Localization and GNSS (ICL-GNSS)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115614502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-27DOI: 10.1109/ICL-GNSS.2017.8376255
Andrea Viel, P. Gallo, A. Montanari, Donatella Gubiani, A. D. Torre, Federico Pittino, C. Marshall
Besides being a fundamental infrastructure for communication, cellular networks are exploited for positioning through signal fingerprinting. Maintaining the fingerprint database consistent and up-to-date is a challenging task in many fingerprint positioning systems, e.g., in those populated by a crowd-sourcing effort. To this end, detecting and tracking the changes in the configurations of cellular networks over time is recognized as a relevant problem. In this paper, we show that to cope with this problem we can successfully exploit information provided by Timing Advance (TA). As a by-product, we prove that TA can improve the fingerprint candidate selection phase, reducing the number of fingerprints to provide as input to positioning algorithms. The effectiveness of the proposed improvements has been tested on a fingerprint positioning system with a large fingerprint dataset collected over a period of 2 years.
{"title":"Dealing with network changes in cellular fingerprint positioning systems","authors":"Andrea Viel, P. Gallo, A. Montanari, Donatella Gubiani, A. D. Torre, Federico Pittino, C. Marshall","doi":"10.1109/ICL-GNSS.2017.8376255","DOIUrl":"https://doi.org/10.1109/ICL-GNSS.2017.8376255","url":null,"abstract":"Besides being a fundamental infrastructure for communication, cellular networks are exploited for positioning through signal fingerprinting. Maintaining the fingerprint database consistent and up-to-date is a challenging task in many fingerprint positioning systems, e.g., in those populated by a crowd-sourcing effort. To this end, detecting and tracking the changes in the configurations of cellular networks over time is recognized as a relevant problem. In this paper, we show that to cope with this problem we can successfully exploit information provided by Timing Advance (TA). As a by-product, we prove that TA can improve the fingerprint candidate selection phase, reducing the number of fingerprints to provide as input to positioning algorithms. The effectiveness of the proposed improvements has been tested on a fingerprint positioning system with a large fingerprint dataset collected over a period of 2 years.","PeriodicalId":330366,"journal":{"name":"2017 International Conference on Localization and GNSS (ICL-GNSS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116021867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-27DOI: 10.1109/ICL-GNSS.2017.8376241
I. Hakala, Jari Luomala
Most localization algorithms in wireless sensor networks rely on a few reference nodes with known locations to estimate the locations of unknown nodes. The locations of reference nodes can be either manually configured or, more practically, obtained by means of some satellite-based positioning system(s). However, satellite-based locations may be inaccurate and imprecise, which results in reduced location accuracy of localization algorithms. This paper proposes a peer-to-peer cooperative GNSS-based localization algorithm for stationary reference nodes to improve their relative location accuracy and precision. The algorithm applies simple statistical methods and GNSS-based information from multiple reference nodes within a WSN in a peer-to-peer fashion to achieve the improvement. The results of the experiments indicate that both location precision and relative location accuracy are clearly increased due to the algorithm.
{"title":"Peer-to-peer cooperative GNSS-based localization for stationary reference nodes in wireless sensor networks","authors":"I. Hakala, Jari Luomala","doi":"10.1109/ICL-GNSS.2017.8376241","DOIUrl":"https://doi.org/10.1109/ICL-GNSS.2017.8376241","url":null,"abstract":"Most localization algorithms in wireless sensor networks rely on a few reference nodes with known locations to estimate the locations of unknown nodes. The locations of reference nodes can be either manually configured or, more practically, obtained by means of some satellite-based positioning system(s). However, satellite-based locations may be inaccurate and imprecise, which results in reduced location accuracy of localization algorithms. This paper proposes a peer-to-peer cooperative GNSS-based localization algorithm for stationary reference nodes to improve their relative location accuracy and precision. The algorithm applies simple statistical methods and GNSS-based information from multiple reference nodes within a WSN in a peer-to-peer fashion to achieve the improvement. The results of the experiments indicate that both location precision and relative location accuracy are clearly increased due to the algorithm.","PeriodicalId":330366,"journal":{"name":"2017 International Conference on Localization and GNSS (ICL-GNSS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121517693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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