Pub Date : 2012-03-15DOI: 10.1109/WPNC.2012.6268749
Stephan Adler, Stefan Pfeiffer, Heiko Will, Thomas Hillebrandt, J. Schiller
Several ways to estimate the position of a Wireless-Sensor-Network (WSN) node have been discussed in the past years. Unlike in outdoor solutions where the Global Positioning System (GPS) could be used in most applications, a general solution for indoor usage has not been found. The few existing indoor localization solutions on the market are highly specialized and rely on infrastructure or on very expensive special designed hardware. Both - infrastructure and expensive hardware - do not fit well into most scenarios where WSNs are commonly used because of the adhoc characteristics and the large amount of nodes of such installations. In this paper we present a solution to get a precise estimation of the distance between two nodes without the needs for special purpose chips or a redesign of already existent nodes. We use radio runtime measurement to calculate the distance between nodes and present algorithms to refine the measurements. A comparison with a professional solution which is available on the market is also presented.
{"title":"Measuring the distance between wireless sensor nodes with standard hardware","authors":"Stephan Adler, Stefan Pfeiffer, Heiko Will, Thomas Hillebrandt, J. Schiller","doi":"10.1109/WPNC.2012.6268749","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268749","url":null,"abstract":"Several ways to estimate the position of a Wireless-Sensor-Network (WSN) node have been discussed in the past years. Unlike in outdoor solutions where the Global Positioning System (GPS) could be used in most applications, a general solution for indoor usage has not been found. The few existing indoor localization solutions on the market are highly specialized and rely on infrastructure or on very expensive special designed hardware. Both - infrastructure and expensive hardware - do not fit well into most scenarios where WSNs are commonly used because of the adhoc characteristics and the large amount of nodes of such installations. In this paper we present a solution to get a precise estimation of the distance between two nodes without the needs for special purpose chips or a redesign of already existent nodes. We use radio runtime measurement to calculate the distance between nodes and present algorithms to refine the measurements. A comparison with a professional solution which is available on the market is also presented.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124265481","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 : 2012-03-15DOI: 10.1109/WPNC.2012.6268735
W. Chai, Cheng Chen, E. Edwan, Jieying Zhang, O. Loffeld
Due to the complementary nature of inertial navigation system (INS) and Wi-Fi positioning principles, an INS/Wi-Fi integrated system is expected to form a low-cost and continuous indoor navigation solution with better performance than using the standalone systems. In this paper, we explore the integration of Wi-Fi measurements with data from microelectromechanical systems (MEMS) based inertial measurement unit (IMU) for indoor vehicle navigation. Two enhancements, which employ adaptive Kalman filtering (AKF) and vehicle constraints, for supporting the integrated system are presented. One field experiment has been conducted for estimating the trajectory of a mobile robot vehicle. The numerical results show that the enhanced integrated system provides higher navigation accuracy, compared to using standalone Wi-Fi positioning and conventional INS/Wi-Fi integration.
{"title":"INS/Wi-Fi based indoor navigation using adaptive Kalman filtering and vehicle constraints","authors":"W. Chai, Cheng Chen, E. Edwan, Jieying Zhang, O. Loffeld","doi":"10.1109/WPNC.2012.6268735","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268735","url":null,"abstract":"Due to the complementary nature of inertial navigation system (INS) and Wi-Fi positioning principles, an INS/Wi-Fi integrated system is expected to form a low-cost and continuous indoor navigation solution with better performance than using the standalone systems. In this paper, we explore the integration of Wi-Fi measurements with data from microelectromechanical systems (MEMS) based inertial measurement unit (IMU) for indoor vehicle navigation. Two enhancements, which employ adaptive Kalman filtering (AKF) and vehicle constraints, for supporting the integrated system are presented. One field experiment has been conducted for estimating the trajectory of a mobile robot vehicle. The numerical results show that the enhanced integrated system provides higher navigation accuracy, compared to using standalone Wi-Fi positioning and conventional INS/Wi-Fi integration.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127342801","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 : 2012-03-15DOI: 10.1109/WPNC.2012.6268743
M. Dashti, S. Ali-Löytty, L. Wirola, Philipp Müller, Henri Nurminen, R. Piché
A robust Kalman filter method for positioning using a database of wireless base station coverage areas is presented. In tests with simulated and real data, the proposed filter is found to be more accurate than static positioning or conventional Kalman filtering.
{"title":"Robust Kalman filter for positioning with wireless BS coverage areas","authors":"M. Dashti, S. Ali-Löytty, L. Wirola, Philipp Müller, Henri Nurminen, R. Piché","doi":"10.1109/WPNC.2012.6268743","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268743","url":null,"abstract":"A robust Kalman filter method for positioning using a database of wireless base station coverage areas is presented. In tests with simulated and real data, the proposed filter is found to be more accurate than static positioning or conventional Kalman filtering.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125489269","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 : 2012-03-15DOI: 10.1109/WPNC.2012.6268744
Meriem Mhedhbi, M. Laaraiedh, B. Uguen
Body Area Networks is an emerging domain taking a big interest from developers and system designers. On the other hand, the need to localize is becoming necessary in diverse applications. Within this context, the aim of this paper is to estimate the different gestures and motions of the human body. Initially, we use information, about human motion, extracted from C3D files. In fact, these files provide us with the exact 3D coordinates of the sensors on a moving body. In a second step the IEEE 802.15.6 channel model is used to estimate the distances between sensors which are the input of the locomotion technique based on Multidimensional Scaling. Basically, this technique did not present satisfying results, that's why we have improved our results by an SVD reconstruction algorithm and by adding distance constraints.
{"title":"Constrained LMDS technique for human motion and gesture estimation","authors":"Meriem Mhedhbi, M. Laaraiedh, B. Uguen","doi":"10.1109/WPNC.2012.6268744","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268744","url":null,"abstract":"Body Area Networks is an emerging domain taking a big interest from developers and system designers. On the other hand, the need to localize is becoming necessary in diverse applications. Within this context, the aim of this paper is to estimate the different gestures and motions of the human body. Initially, we use information, about human motion, extracted from C3D files. In fact, these files provide us with the exact 3D coordinates of the sensors on a moving body. In a second step the IEEE 802.15.6 channel model is used to estimate the distances between sensors which are the input of the locomotion technique based on Multidimensional Scaling. Basically, this technique did not present satisfying results, that's why we have improved our results by an SVD reconstruction algorithm and by adding distance constraints.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129356922","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 : 2012-03-15DOI: 10.1109/WPNC.2012.6268740
Yubin Zhao, Yuan Yang, M. Kyas
In this paper, we propose a robust and efficient particle filtering framework for indoor localization in wireless sensor network (WSN) which searches effective anchors and constructs particle filter dynamically. Within this framework, three algorithms are integrated into the dynamic particle filter: anchor selection algorithm, location constraint resampling and SIR particle filter. The proposed scheme searches the maximum number of anchors with line of sight (LOS) to the target to guarantee the effective measurement. Then, we construct a dynamic particle filter with the chosen anchors and develop a novel resampling scheme which generates the particles within the indoor location constraints. The proposed scheme is proved to be robust and computational efficient. Simulation results show that our scheme is accurate with low computation cost, which is promising for real-time implementation.
{"title":"Dynamic searching particle filtering scheme for indoor localization in wireless sensor network","authors":"Yubin Zhao, Yuan Yang, M. Kyas","doi":"10.1109/WPNC.2012.6268740","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268740","url":null,"abstract":"In this paper, we propose a robust and efficient particle filtering framework for indoor localization in wireless sensor network (WSN) which searches effective anchors and constructs particle filter dynamically. Within this framework, three algorithms are integrated into the dynamic particle filter: anchor selection algorithm, location constraint resampling and SIR particle filter. The proposed scheme searches the maximum number of anchors with line of sight (LOS) to the target to guarantee the effective measurement. Then, we construct a dynamic particle filter with the chosen anchors and develop a novel resampling scheme which generates the particles within the indoor location constraints. The proposed scheme is proved to be robust and computational efficient. Simulation results show that our scheme is accurate with low computation cost, which is promising for real-time implementation.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121766596","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 : 2012-03-15DOI: 10.1109/WPNC.2012.6268732
A. Savioli, E. Goldoni, P. Gamba
Cooperation in wireless communication networks is nowadays commonly used in order to provide either deeper coverage on those areas where radio signal is usually weak, such as inside complex structures and buildings, or to improve the power efficiency of the whole system. In these networks, client devices act as active parts of the signal transmission: by repeating the signal coming from a device to another they make possible to reach remote locations where the main transmitter signal is too weak or not available. Inspired by this ideas, cooperation in Wireless Sensor Networks (WSNs) is used to achieve better accuracy and to make the localization process possible even if there are not enough anchor nodes available. However, this system has to cope with both synchronization and channel access issues, so some considerations must be taken into account. With this work we want to show a preliminary analysis of these issues, focusing on the channel access problem, when multiple radios must share the medium in order to exchange either ranges and data information about their location, and showing the results we obtained on a real WSN implementation using UWB hardware.
{"title":"Impact of channel access on localization in cooperative UWB sensor network: A case study","authors":"A. Savioli, E. Goldoni, P. Gamba","doi":"10.1109/WPNC.2012.6268732","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268732","url":null,"abstract":"Cooperation in wireless communication networks is nowadays commonly used in order to provide either deeper coverage on those areas where radio signal is usually weak, such as inside complex structures and buildings, or to improve the power efficiency of the whole system. In these networks, client devices act as active parts of the signal transmission: by repeating the signal coming from a device to another they make possible to reach remote locations where the main transmitter signal is too weak or not available. Inspired by this ideas, cooperation in Wireless Sensor Networks (WSNs) is used to achieve better accuracy and to make the localization process possible even if there are not enough anchor nodes available. However, this system has to cope with both synchronization and channel access issues, so some considerations must be taken into account. With this work we want to show a preliminary analysis of these issues, focusing on the channel access problem, when multiple radios must share the medium in order to exchange either ranges and data information about their location, and showing the results we obtained on a real WSN implementation using UWB hardware.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"243 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132780320","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 : 2012-03-15DOI: 10.1109/WPNC.2012.6268755
Simona Poilinca, G. Abreu, D. Macagnano, S. Severi
In this paper we present a computational-efficient two-phases model for localization and tracking based on Kalman filter. A first estimate of target position is obtained via Super MDS algorithm only using noisy distance measurements, then location information is refined via a classic Kalman Filter exploiting the noisy acceleration of the target. The main scientific contribution of this paper is to show that, although the information theory proves that such a sequential approach is sub-optimal, the performance is accurate enough even with high-noisy acceleration measurements. This fact suggests that in the vast majority of use cases is possible, taking advantage of its mathematical simplicity, to employee this two-phase model neglecting its sub-optimality.
{"title":"Improved localization using Kalman filter on estimated positions","authors":"Simona Poilinca, G. Abreu, D. Macagnano, S. Severi","doi":"10.1109/WPNC.2012.6268755","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268755","url":null,"abstract":"In this paper we present a computational-efficient two-phases model for localization and tracking based on Kalman filter. A first estimate of target position is obtained via Super MDS algorithm only using noisy distance measurements, then location information is refined via a classic Kalman Filter exploiting the noisy acceleration of the target. The main scientific contribution of this paper is to show that, although the information theory proves that such a sequential approach is sub-optimal, the performance is accurate enough even with high-noisy acceleration measurements. This fact suggests that in the vast majority of use cases is possible, taking advantage of its mathematical simplicity, to employee this two-phase model neglecting its sub-optimality.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114799606","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 : 2012-03-15DOI: 10.1109/WPNC.2012.6268761
E. Koppe, M. Bartholmai, Achim Liers, J. Schiller
Sensor based person tracking is a challenging topic. The main objective is positioning in areas without GPS connection, i.e. indoors. A research project is carried out at BAM, Federal Institute for Materials Research and Testing, to develop and to validate a multi-sensor system for 3D localization. It combines body motion sensing and a guard system for the tracking and recording of the status of persons. The so named BodyGuard system was designed for sensor-based monitoring and radio-based transmission of the movement of a person. Algorithms were developed to transform the sensor data into a spatial coordinate. This paper describes how the BodyGuard system operates, which main components were used in the system, how the individual sensor data are converted into 3D motion data, with which algorithms the individual sensors are processed, how individual errors are compensated and how the sensor data are merged into a 3D Model. Final objective of the BodyGuard system is to determine the exact location of a person in a building, e.g. during fire-fighting operations.
{"title":"Radio-based multi-sensor system for person tracking and indoor positioning","authors":"E. Koppe, M. Bartholmai, Achim Liers, J. Schiller","doi":"10.1109/WPNC.2012.6268761","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268761","url":null,"abstract":"Sensor based person tracking is a challenging topic. The main objective is positioning in areas without GPS connection, i.e. indoors. A research project is carried out at BAM, Federal Institute for Materials Research and Testing, to develop and to validate a multi-sensor system for 3D localization. It combines body motion sensing and a guard system for the tracking and recording of the status of persons. The so named BodyGuard system was designed for sensor-based monitoring and radio-based transmission of the movement of a person. Algorithms were developed to transform the sensor data into a spatial coordinate. This paper describes how the BodyGuard system operates, which main components were used in the system, how the individual sensor data are converted into 3D motion data, with which algorithms the individual sensors are processed, how individual errors are compensated and how the sensor data are merged into a 3D Model. Final objective of the BodyGuard system is to determine the exact location of a person in a building, e.g. during fire-fighting operations.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121985359","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 : 2012-03-15DOI: 10.1109/WPNC.2012.6268758
Haowei Wang, G. Bauer, F. Kirsch, M. Vossiek
Hybrid pedestrian localization based on multiple data sources is becoming more and more popular. Nevertheless, accurate and reliable pedestrian localization is still a challenge due mainly to their unpredictable movement. For some applications such as interactive museum guidance unpredictable pedestrian movement is a major obstacle to accurate localization. In this paper we introduce a novel fusion algorithm using best-neighbor rating. The algorithm reduces the accumulated error originating from unreliable sensor measurements and increases the efficiency by only evaluating the nearby cells of the last estimated position. Experimental results show that a mean error of less than 1.5 M is achievable in real-world scenarios.
{"title":"Optimization of fusion algorithm for hybrid pedestrian localization and navigation","authors":"Haowei Wang, G. Bauer, F. Kirsch, M. Vossiek","doi":"10.1109/WPNC.2012.6268758","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268758","url":null,"abstract":"Hybrid pedestrian localization based on multiple data sources is becoming more and more popular. Nevertheless, accurate and reliable pedestrian localization is still a challenge due mainly to their unpredictable movement. For some applications such as interactive museum guidance unpredictable pedestrian movement is a major obstacle to accurate localization. In this paper we introduce a novel fusion algorithm using best-neighbor rating. The algorithm reduces the accumulated error originating from unreliable sensor measurements and increases the efficiency by only evaluating the nearby cells of the last estimated position. Experimental results show that a mean error of less than 1.5 M is achievable in real-world scenarios.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"59 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114019085","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 : 2012-03-15DOI: 10.1109/WPNC.2012.6268736
Benton Thompson, R. Buehrer
Indoor positioning has become a hot research topic due to a plethora of interesting applications ranging from emergency responder tracking to location-based services. In this work we focus on the problem of network localization, sometimes called collaborative localization, where a network of nodes is to be localized using both connections to anchors (when they exist) and connections between unlocalized nodes [1]. The fast and efficient IPPM algorithm developed in [2] and [3] performs well in most situations. However, it suffers from the existence of local, non-global minima which cause large localization error. In this work, we propose a technique for identifying and mitigating local minima errors, and we show that collaborative position location estimates can be greatly improved using our method.
{"title":"Characterizing and improving the collaborative position location problem","authors":"Benton Thompson, R. Buehrer","doi":"10.1109/WPNC.2012.6268736","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268736","url":null,"abstract":"Indoor positioning has become a hot research topic due to a plethora of interesting applications ranging from emergency responder tracking to location-based services. In this work we focus on the problem of network localization, sometimes called collaborative localization, where a network of nodes is to be localized using both connections to anchors (when they exist) and connections between unlocalized nodes [1]. The fast and efficient IPPM algorithm developed in [2] and [3] performs well in most situations. However, it suffers from the existence of local, non-global minima which cause large localization error. In this work, we propose a technique for identifying and mitigating local minima errors, and we show that collaborative position location estimates can be greatly improved using our method.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115168693","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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