Pub Date : 2010-12-03DOI: 10.1109/UPINLBS.2010.5654313
Tim Schwartz, Christoph Stahl, Christian A. Müller, Valentin Dimitrov, Hao Ji
We implemented a positioning engine for mobile phones that can be trained by the users to recognize places as personal landmarks by their wireless communication fingerprint. Our always-best-positioned approach integrates heterogeneous sensor data, such as Bluetooth (BT) device addresses, WLAN MACs, GSM cell ids and GPS coordinates, if available. As an alternative to measuring the signal strength of wireless access points, our positioning engine measures the relative frequency of their appearance and disappearance over time, which closely correlates to their distance. The user can add new places as symbolic names to a hierarchical location model at any time using their mobile phone. For each place, the wireless sensor fingerprint can be trained by the user to define a landmark. Once landmarks have been trained, the positioning engine continuously matches the current sensor profile against the database of learned fingerprints and chooses the most likely place. In case that no BT or WLAN APs are visible, the hierarchical data model can at least derive a higher-level description of the current region based on GSM or GPS as fallback strategy in the sense of being always best positioned. We evaluated the positioning accuracy in our university's lab environment in terms of hits and misses and investigated the effect of various time window sizes for the frequency measurement of the fingerprint. The symbolic location model can be applied for example to adapt the mobile device to different contexts, e.g. automatically mute the ringtone in meeting rooms, trigger location-dependent rules and events, or disclose the current location to friends. (Abstract)
{"title":"UbiSpot - A user trained always best positioned engine for mobile phones","authors":"Tim Schwartz, Christoph Stahl, Christian A. Müller, Valentin Dimitrov, Hao Ji","doi":"10.1109/UPINLBS.2010.5654313","DOIUrl":"https://doi.org/10.1109/UPINLBS.2010.5654313","url":null,"abstract":"We implemented a positioning engine for mobile phones that can be trained by the users to recognize places as personal landmarks by their wireless communication fingerprint. Our always-best-positioned approach integrates heterogeneous sensor data, such as Bluetooth (BT) device addresses, WLAN MACs, GSM cell ids and GPS coordinates, if available. As an alternative to measuring the signal strength of wireless access points, our positioning engine measures the relative frequency of their appearance and disappearance over time, which closely correlates to their distance. The user can add new places as symbolic names to a hierarchical location model at any time using their mobile phone. For each place, the wireless sensor fingerprint can be trained by the user to define a landmark. Once landmarks have been trained, the positioning engine continuously matches the current sensor profile against the database of learned fingerprints and chooses the most likely place. In case that no BT or WLAN APs are visible, the hierarchical data model can at least derive a higher-level description of the current region based on GSM or GPS as fallback strategy in the sense of being always best positioned. We evaluated the positioning accuracy in our university's lab environment in terms of hits and misses and investigated the effect of various time window sizes for the frequency measurement of the fingerprint. The symbolic location model can be applied for example to adapt the mobile device to different contexts, e.g. automatically mute the ringtone in meeting rooms, trigger location-dependent rules and events, or disclose the current location to friends. (Abstract)","PeriodicalId":373653,"journal":{"name":"2010 Ubiquitous Positioning Indoor Navigation and Location Based Service","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115113362","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 : 2010-12-03DOI: 10.1109/UPINLBS.2010.5653577
Xun Li, Jinling Wang, Aire Olesk, Nathan Knight, W. Ding
In this paper, we propose a method of vision-based positioning with the use of single camera and newly defined 3D maps for indoor localization and navigation purposes. Our work here is to address the accuracy and reliability concerns of an indoor navigation system. The main contribution will be the adoption of photogrammetric 6DOF pose estimation method to improve the positioning accuracy. DOPs are introduced to evaluate positioning precision within vision-based domain. Quality control strategies are also applied to detect outliers in the observation and strengthen system reliability. Besides, only natural landmarks are required in the proposed method to provide absolute position and orientation information.
{"title":"Indoor positioning within a single camera and 3D maps","authors":"Xun Li, Jinling Wang, Aire Olesk, Nathan Knight, W. Ding","doi":"10.1109/UPINLBS.2010.5653577","DOIUrl":"https://doi.org/10.1109/UPINLBS.2010.5653577","url":null,"abstract":"In this paper, we propose a method of vision-based positioning with the use of single camera and newly defined 3D maps for indoor localization and navigation purposes. Our work here is to address the accuracy and reliability concerns of an indoor navigation system. The main contribution will be the adoption of photogrammetric 6DOF pose estimation method to improve the positioning accuracy. DOPs are introduced to evaluate positioning precision within vision-based domain. Quality control strategies are also applied to detect outliers in the observation and strengthen system reliability. Besides, only natural landmarks are required in the proposed method to provide absolute position and orientation information.","PeriodicalId":373653,"journal":{"name":"2010 Ubiquitous Positioning Indoor Navigation and Location Based Service","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115072916","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 : 2010-12-03DOI: 10.1109/UPINLBS.2010.5654316
Vinski Bräysy, Jarkko Hurme, Hannu Teppo, T. Korpela, Mikko Karjalainen
In this paper, we provide a study of applying location aware wireless sensor network system for tracking movements and gathering sensor data from sports team players. A set of system design requirements are developed that cover the design of the sensor network system, the hardware design of the mobile nodes, the design of the position algorithm, the design of communication protocols, and the design of testbed for testing positioning accuracy. Based on the field experiments, we propose a system architecture that fulfills these requirements. The architecture proposed implements e.g. hybrid location technologies for better accuracy and data aggregation for energy efficiency.
{"title":"Movement tracking of sports team players with wireless sensor network","authors":"Vinski Bräysy, Jarkko Hurme, Hannu Teppo, T. Korpela, Mikko Karjalainen","doi":"10.1109/UPINLBS.2010.5654316","DOIUrl":"https://doi.org/10.1109/UPINLBS.2010.5654316","url":null,"abstract":"In this paper, we provide a study of applying location aware wireless sensor network system for tracking movements and gathering sensor data from sports team players. A set of system design requirements are developed that cover the design of the sensor network system, the hardware design of the mobile nodes, the design of the position algorithm, the design of communication protocols, and the design of testbed for testing positioning accuracy. Based on the field experiments, we propose a system architecture that fulfills these requirements. The architecture proposed implements e.g. hybrid location technologies for better accuracy and data aggregation for energy efficiency.","PeriodicalId":373653,"journal":{"name":"2010 Ubiquitous Positioning Indoor Navigation and Location Based Service","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121876904","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 : 2010-12-03DOI: 10.1109/UPINLBS.2010.5653672
L. Ruotsalainen, H. Kuusniemi, Ruizhi Chen
Pedestrian navigation is often needed in surroundings where GPS signals are degraded or not available at all, like indoors and urban canyons. Aiding from other location sensors is then needed. Self-contained sensors with pedestrian dead reckoning algorithms offer location information that doesn't depend on any infrastructure and isn't restricted to certain areas. The problem with those sensors is, however, that they suffer from drifting in time and are prone to errors. Integrating an independent drift-free sensor like a camera and correcting the heading with information derived from it will provide a seamless navigation system. In this paper we will give an overview of the systems developed for pedestrian visual-aided navigation for GPS degraded or denied environments. We will also introduce methods and algorithms for retrieving position and motion information from images as well as for integrating this information with measurements obtained from other location sensors.
{"title":"Overview of methods for visual-aided pedestrian navigation","authors":"L. Ruotsalainen, H. Kuusniemi, Ruizhi Chen","doi":"10.1109/UPINLBS.2010.5653672","DOIUrl":"https://doi.org/10.1109/UPINLBS.2010.5653672","url":null,"abstract":"Pedestrian navigation is often needed in surroundings where GPS signals are degraded or not available at all, like indoors and urban canyons. Aiding from other location sensors is then needed. Self-contained sensors with pedestrian dead reckoning algorithms offer location information that doesn't depend on any infrastructure and isn't restricted to certain areas. The problem with those sensors is, however, that they suffer from drifting in time and are prone to errors. Integrating an independent drift-free sensor like a camera and correcting the heading with information derived from it will provide a seamless navigation system. In this paper we will give an overview of the systems developed for pedestrian visual-aided navigation for GPS degraded or denied environments. We will also introduce methods and algorithms for retrieving position and motion information from images as well as for integrating this information with measurements obtained from other location sensors.","PeriodicalId":373653,"journal":{"name":"2010 Ubiquitous Positioning Indoor Navigation and Location Based Service","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129773028","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 : 2010-12-03DOI: 10.1109/UPINLBS.2010.5654348
Matthias Brugger, T. Christ, Ferdinand Kemeth, Sandor Nagy, Matthias Schäfer, M. Pietrzyk
Indoor localization with the accuracy being better than few meters may be a quite challenging task when the system costs are an issue. This paper presents a frequency modulation continuous wave (FMCW) technology-based indoor localization system that has been built in the EU FP7 Confidence project. The main objective of the Confidence project is the development and integration of innovative technologies to build a care system for the detection of abnormal events, e.g. falls or unexpected behaviors that may be related to health problems of elderly people. In this paper, details on the hardware, firmware, and software parts of the developed prototype are provided. The localization algorithm based on the Kalman filter with the Gaussian averaging is presented. The localization performance is evaluated in the laboratory setting. Our first results indicate that in the case of localization in two dimensions the mean absolute position error is about 1 meter.
{"title":"The FMCW technology-based indoor localization system","authors":"Matthias Brugger, T. Christ, Ferdinand Kemeth, Sandor Nagy, Matthias Schäfer, M. Pietrzyk","doi":"10.1109/UPINLBS.2010.5654348","DOIUrl":"https://doi.org/10.1109/UPINLBS.2010.5654348","url":null,"abstract":"Indoor localization with the accuracy being better than few meters may be a quite challenging task when the system costs are an issue. This paper presents a frequency modulation continuous wave (FMCW) technology-based indoor localization system that has been built in the EU FP7 Confidence project. The main objective of the Confidence project is the development and integration of innovative technologies to build a care system for the detection of abnormal events, e.g. falls or unexpected behaviors that may be related to health problems of elderly people. In this paper, details on the hardware, firmware, and software parts of the developed prototype are provided. The localization algorithm based on the Kalman filter with the Gaussian averaging is presented. The localization performance is evaluated in the laboratory setting. Our first results indicate that in the case of localization in two dimensions the mean absolute position error is about 1 meter.","PeriodicalId":373653,"journal":{"name":"2010 Ubiquitous Positioning Indoor Navigation and Location Based Service","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114554917","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 : 2010-12-03DOI: 10.1109/UPINLBS.2010.5653986
K. Abdulrahim, C. Hide, T. Moore, C. Hill
Heading drift error remains a problem in a standalone navigation system that uses only low cost MEMS IMU due to yaw error unobservability. This paper therefore proposes a shoe mounted IMU approach, integrated with ZUPT and building heading information in Kalman filter environment to reduce heading drift for pedestrian navigation application. There were no additional sensors used except MEMS IMU that contains accelerometers and gyros. Two trials; represented by regular and irregular walking trials, were undertaken in a typical public building. The results were then compared with HSGPS solution and IMU+ZUPT solution. Based on these trials, return position error of 0.1% from total distance travelled was achieved using a low cost MEMS IMU only.
{"title":"Aiding MEMS IMU with building heading for indoor pedestrian navigation","authors":"K. Abdulrahim, C. Hide, T. Moore, C. Hill","doi":"10.1109/UPINLBS.2010.5653986","DOIUrl":"https://doi.org/10.1109/UPINLBS.2010.5653986","url":null,"abstract":"Heading drift error remains a problem in a standalone navigation system that uses only low cost MEMS IMU due to yaw error unobservability. This paper therefore proposes a shoe mounted IMU approach, integrated with ZUPT and building heading information in Kalman filter environment to reduce heading drift for pedestrian navigation application. There were no additional sensors used except MEMS IMU that contains accelerometers and gyros. Two trials; represented by regular and irregular walking trials, were undertaken in a typical public building. The results were then compared with HSGPS solution and IMU+ZUPT solution. Based on these trials, return position error of 0.1% from total distance travelled was achieved using a low cost MEMS IMU only.","PeriodicalId":373653,"journal":{"name":"2010 Ubiquitous Positioning Indoor Navigation and Location Based Service","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131659850","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 : 2010-12-03DOI: 10.1109/UPINLBS.2010.5654054
L. Deambrogio, C. Palestini, F. Bastia, Giulio Gabelli, G. Corazza, J. Samson
This paper introduces the concept of peer-to-peer cooperation as an aiding technique in GNSS positioning and addresses the impact of the presence of professional receivers in the network. The impact of their higher accuracy is evaluated for the cases of both GNSS-data only and hybrid data exchange. Kalman Filters are considered as the data fusion engine.
{"title":"Impact of high-end receivers in a peer-to-peer cooperative localization system","authors":"L. Deambrogio, C. Palestini, F. Bastia, Giulio Gabelli, G. Corazza, J. Samson","doi":"10.1109/UPINLBS.2010.5654054","DOIUrl":"https://doi.org/10.1109/UPINLBS.2010.5654054","url":null,"abstract":"This paper introduces the concept of peer-to-peer cooperation as an aiding technique in GNSS positioning and addresses the impact of the presence of professional receivers in the network. The impact of their higher accuracy is evaluated for the cases of both GNSS-data only and hybrid data exchange. Kalman Filters are considered as the data fusion engine.","PeriodicalId":373653,"journal":{"name":"2010 Ubiquitous Positioning Indoor Navigation and Location Based Service","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131878480","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 : 2010-12-03DOI: 10.1109/UPINLBS.2010.5653658
C. Hide, T. Botterill, M. Andreotti
Low cost MEMS sensors typically result in large position errors after very short periods of time unless they are frequently corrected by measurements from other systems. One form of measurements comes from the computer vision community where successive frames from a camera approximately looking at the ground can be used to compute the translation between frames. These measurements can be used to control the drift of an Inertial Measurement Unit (IMU) when measurements from other systems such as GPS are not available. This configuration of sensors is preferable since they are already available on some smartphones. This paper demonstrates that computer vision measurements can significantly reduce the drift of IMU-only positioning with a view for pedestrian navigation indoors. Issues such as computational requirements and operation in low light areas are also discussed.
{"title":"Low cost vision-aided IMU for pedestrian navigation","authors":"C. Hide, T. Botterill, M. Andreotti","doi":"10.1109/UPINLBS.2010.5653658","DOIUrl":"https://doi.org/10.1109/UPINLBS.2010.5653658","url":null,"abstract":"Low cost MEMS sensors typically result in large position errors after very short periods of time unless they are frequently corrected by measurements from other systems. One form of measurements comes from the computer vision community where successive frames from a camera approximately looking at the ground can be used to compute the translation between frames. These measurements can be used to control the drift of an Inertial Measurement Unit (IMU) when measurements from other systems such as GPS are not available. This configuration of sensors is preferable since they are already available on some smartphones. This paper demonstrates that computer vision measurements can significantly reduce the drift of IMU-only positioning with a view for pedestrian navigation indoors. Issues such as computational requirements and operation in low light areas are also discussed.","PeriodicalId":373653,"journal":{"name":"2010 Ubiquitous Positioning Indoor Navigation and Location Based Service","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124513223","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 : 2010-12-03DOI: 10.1109/UPINLBS.2010.5654321
S. Wibowo, M. Klepal
Pattern matching localisation based on Received Signal Strength Indication (RSSI) is widely implemented in the Wireless Local Area Network (WLAN). This implementation is commonly with a filtering method to achieve better location estimation accuracy. A Kalman filter (KF) is an optimal filter, if requirements on linear/Gaussian state space model are met. Otherwise, a particle filter (PF) should be used to deal with nonlinear/non-Gaussian state space model. However, in the real situation especially in the localisation field, the state space model may be linear/Gaussian and nonlinear/non-Gaussian. Therefore, there should be a filtering method that can accommodate both linear/Gaussian and nonlinear/non-Gaussian state space model such as Rao Blackwellized particle filter (RBPF). RBPF implemented in the pattern matching localisation system is described and its performance is compared against KF and PF. Those three filtering methods are evaluated in the test bed. To the best of our knowledge, implementing RBPF and performance comparison against KF and PF in the pattern matching indoor localisation in WLAN environment have never been published before.
{"title":"Rao-Blackwellized particle filter for pattern matching indoor localisation","authors":"S. Wibowo, M. Klepal","doi":"10.1109/UPINLBS.2010.5654321","DOIUrl":"https://doi.org/10.1109/UPINLBS.2010.5654321","url":null,"abstract":"Pattern matching localisation based on Received Signal Strength Indication (RSSI) is widely implemented in the Wireless Local Area Network (WLAN). This implementation is commonly with a filtering method to achieve better location estimation accuracy. A Kalman filter (KF) is an optimal filter, if requirements on linear/Gaussian state space model are met. Otherwise, a particle filter (PF) should be used to deal with nonlinear/non-Gaussian state space model. However, in the real situation especially in the localisation field, the state space model may be linear/Gaussian and nonlinear/non-Gaussian. Therefore, there should be a filtering method that can accommodate both linear/Gaussian and nonlinear/non-Gaussian state space model such as Rao Blackwellized particle filter (RBPF). RBPF implemented in the pattern matching localisation system is described and its performance is compared against KF and PF. Those three filtering methods are evaluated in the test bed. To the best of our knowledge, implementing RBPF and performance comparison against KF and PF in the pattern matching indoor localisation in WLAN environment have never been published before.","PeriodicalId":373653,"journal":{"name":"2010 Ubiquitous Positioning Indoor Navigation and Location Based Service","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124542082","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 : 2010-12-03DOI: 10.1109/UPINLBS.2010.5654301
B. Wei, Wu Chen, X. Ding
Wireless Sensor Networks (WSNs) are increasingly being used for various information collection in the future. For many applications, a large number of sensors in an active wireless network are more effective and preferable to a few expensive wireless sensors. Location discovery of sensor nodes after they have been deployed is known as a challenge, especially for moving sensors. Algorithms based on radio signal strength indication are considered as the lowest cost method to solve localization problem. But in practice, the accuracy of signal strength indication algorithm varies with the average distances between wireless nodes - the nearer, the better. This paper describes a new integrated dynamic localization algorithm, based on Received Signal Strength Indication (RSSI) and Multidimensional Scaling (MDS) algorithm. In this concept, signals with RSSI values among beacon nodes could help to generate a real time 2D map of the network by self iterations. Unknown nodes use the map to determine their locations in this region. A software simulator is created to test the performance of the algorithm, and practical experiments also show the development of localization accuracy and efficiency.
{"title":"Advanced MDS based localization algorithm for location based services in Wireless Sensor Network","authors":"B. Wei, Wu Chen, X. Ding","doi":"10.1109/UPINLBS.2010.5654301","DOIUrl":"https://doi.org/10.1109/UPINLBS.2010.5654301","url":null,"abstract":"Wireless Sensor Networks (WSNs) are increasingly being used for various information collection in the future. For many applications, a large number of sensors in an active wireless network are more effective and preferable to a few expensive wireless sensors. Location discovery of sensor nodes after they have been deployed is known as a challenge, especially for moving sensors. Algorithms based on radio signal strength indication are considered as the lowest cost method to solve localization problem. But in practice, the accuracy of signal strength indication algorithm varies with the average distances between wireless nodes - the nearer, the better. This paper describes a new integrated dynamic localization algorithm, based on Received Signal Strength Indication (RSSI) and Multidimensional Scaling (MDS) algorithm. In this concept, signals with RSSI values among beacon nodes could help to generate a real time 2D map of the network by self iterations. Unknown nodes use the map to determine their locations in this region. A software simulator is created to test the performance of the algorithm, and practical experiments also show the development of localization accuracy and efficiency.","PeriodicalId":373653,"journal":{"name":"2010 Ubiquitous Positioning Indoor Navigation and Location Based Service","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116460593","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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