Pub Date : 2012-03-15DOI: 10.1109/WPNC.2012.6268757
M. O. Ergin, A. Wolisz
Wireless sensor networks (WSN) have gained a significant attention in research and carry the promise to be helpful in numerous aspects of life. For many applications, the location information of the nodes needs to be known. As this information is not necessarily available, there is a huge interest in algorithms estimating the positions of individual nodes. The precision and computational complexity of such “localization” algorithms is still a big issue. However, there are cases where the nodes are placed in one of a few possible predetermined positions. In those cases, computing the relative positions of nodes in relation to each other might be sufficient to determine their real positions. In this study, we introduce a methodology for discovering the sequence of nodes in a unidimensional configuration using the measured Received Signal Strength(RSS) values and allowance of frequency diversity of high frequency radio (CC2420) that is frequently used in wireless sensor networks. In the reported experimental tests, we were able to determine the node sequence correctly for the nodes that are as close as 50cm to each other, using the developed methodology.
{"title":"Node position discovery in wireless sensor networks","authors":"M. O. Ergin, A. Wolisz","doi":"10.1109/WPNC.2012.6268757","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268757","url":null,"abstract":"Wireless sensor networks (WSN) have gained a significant attention in research and carry the promise to be helpful in numerous aspects of life. For many applications, the location information of the nodes needs to be known. As this information is not necessarily available, there is a huge interest in algorithms estimating the positions of individual nodes. The precision and computational complexity of such “localization” algorithms is still a big issue. However, there are cases where the nodes are placed in one of a few possible predetermined positions. In those cases, computing the relative positions of nodes in relation to each other might be sufficient to determine their real positions. In this study, we introduce a methodology for discovering the sequence of nodes in a unidimensional configuration using the measured Received Signal Strength(RSS) values and allowance of frequency diversity of high frequency radio (CC2420) that is frequently used in wireless sensor networks. In the reported experimental tests, we were able to determine the node sequence correctly for the nodes that are as close as 50cm to each other, using the developed methodology.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"95 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":"129212779","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.2012.6268739
Jani Saloranta, D. Macagnano, G. Abreu
In this paper we illustrate the potential of the generic I-SCAL framework for localization. Much like algebraic Multidimensional Scaling, which was originally utilized in other fields of science until it was identified as suitable for localization, I-SCAL is a SMACOF optimization-based generic framework which, to the best of our knowledge is here, for the first time, employed to solve the localization problem. To do so we propose to modify the rectangular objects employed in the standard I-SCAL framework with circular ones, resulting in faster and better performing algorithm in standard localization scenario. In addition it is shown that the computational complexity be further reduced by means of a vector extrapolation stage added in the optimization stage. The application of the proposed algorithm to the two standard localization scenarios here considered shows that the I-SCAL algorithm outperforms the SMACOF algorithm.
{"title":"Interval-scaling for multitarget localization","authors":"Jani Saloranta, D. Macagnano, G. Abreu","doi":"10.1109/WPNC.2012.6268739","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268739","url":null,"abstract":"In this paper we illustrate the potential of the generic I-SCAL framework for localization. Much like algebraic Multidimensional Scaling, which was originally utilized in other fields of science until it was identified as suitable for localization, I-SCAL is a SMACOF optimization-based generic framework which, to the best of our knowledge is here, for the first time, employed to solve the localization problem. To do so we propose to modify the rectangular objects employed in the standard I-SCAL framework with circular ones, resulting in faster and better performing algorithm in standard localization scenario. In addition it is shown that the computational complexity be further reduced by means of a vector extrapolation stage added in the optimization stage. The application of the proposed algorithm to the two standard localization scenarios here considered shows that the I-SCAL algorithm outperforms the SMACOF algorithm.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"9 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":"126882639","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.2012.6268762
Shweta Shrestha, E. Laitinen, J. Talvitie, E. Lohan
Indoors wireless propagation effects in the context of positioning are still not deeply understood or reported in the existing literature. For an indoor localization system based on Received Signal Strength Indicator measurements, deriving suitable path loss models with adequate shadowing or shadow fading modeling is of utmost importance in order to be able to build low complexity positioning estimators. In this paper we analyze in depth the basic path loss and shadowing models and their parameters, based on extensive measurement campaigns with WLAN and cellular signals. We also discuss the similarities and differences between cellular and WLAN wireless propagation models.
室内无线传播在定位环境下的影响在现有文献中还没有被深入理解或报道。对于基于接收信号强度指标(Received Signal Strength Indicator)测量的室内定位系统，为了能够构建低复杂度的定位估计器，推导合适的路径损失模型并进行适当的阴影或阴影衰落建模是至关重要的。在本文中，我们深入分析了基本的路径损失和阴影模型及其参数，基于广泛的测量活动与无线局域网和蜂窝信号。我们还讨论了蜂窝和WLAN无线传播模型之间的异同。
{"title":"RSSI channel effects in cellular and WLAN positioning","authors":"Shweta Shrestha, E. Laitinen, J. Talvitie, E. Lohan","doi":"10.1109/WPNC.2012.6268762","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268762","url":null,"abstract":"Indoors wireless propagation effects in the context of positioning are still not deeply understood or reported in the existing literature. For an indoor localization system based on Received Signal Strength Indicator measurements, deriving suitable path loss models with adequate shadowing or shadow fading modeling is of utmost importance in order to be able to build low complexity positioning estimators. In this paper we analyze in depth the basic path loss and shadowing models and their parameters, based on extensive measurement campaigns with WLAN and cellular signals. We also discuss the similarities and differences between cellular and WLAN wireless propagation models.","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":"128808148","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.2012.6268730
Gabriel E. García, H. Wymeersch, William Riblier, Alexandre Cazalis
Cooperative positioning is a solution for location-aware applications where GPS-aided localization is unfeasible. In this paper, we provide a qualitative comparison between cooperative and non-cooperative localization under node-failure scenarios, in a typical indoor environment using off-the-shelf 802.15.4a radios. From our analysis, we observe the improved robustness and coverage offered by the cooperative approach in node-failure scenarios.
{"title":"Cooperative localization with 802.15.4a CSS radios: Robustness to node failures","authors":"Gabriel E. García, H. Wymeersch, William Riblier, Alexandre Cazalis","doi":"10.1109/WPNC.2012.6268730","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268730","url":null,"abstract":"Cooperative positioning is a solution for location-aware applications where GPS-aided localization is unfeasible. In this paper, we provide a qualitative comparison between cooperative and non-cooperative localization under node-failure scenarios, in a typical indoor environment using off-the-shelf 802.15.4a radios. From our analysis, we observe the improved robustness and coverage offered by the cooperative approach in node-failure scenarios.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"18 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":"115487236","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.2012.6268742
Leila Pishdad, F. Labeau
Particle filters have been widely used in positioning problems, to post-process the noisy location sensor measurements. In this paper, instead of the commonly used Prior Importance Function for particle filtering, we have formulated and applied the Optimal Importance Function. Unlike other importance functions, the Optimal Importance Function minimizes the variance of particle weights and thus resolves the degeneracy problem of particle filters. In this work, we have derived a closed form formula for the Optimal Importance Function using map-independent random walk velocity motion model and a GMM sensor error. Due to the generality of the proposed method, it can be used for a wide range of moving objects in different environments. Simulation results support the validity of modeling assumptions and the advantage of applying an Optimal Importance Function in indoor localization and positioning.
{"title":"Indoor positioning using particle filters with optimal importance function","authors":"Leila Pishdad, F. Labeau","doi":"10.1109/WPNC.2012.6268742","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268742","url":null,"abstract":"Particle filters have been widely used in positioning problems, to post-process the noisy location sensor measurements. In this paper, instead of the commonly used Prior Importance Function for particle filtering, we have formulated and applied the Optimal Importance Function. Unlike other importance functions, the Optimal Importance Function minimizes the variance of particle weights and thus resolves the degeneracy problem of particle filters. In this work, we have derived a closed form formula for the Optimal Importance Function using map-independent random walk velocity motion model and a GMM sensor error. Due to the generality of the proposed method, it can be used for a wide range of moving objects in different environments. Simulation results support the validity of modeling assumptions and the advantage of applying an Optimal Importance Function in indoor localization and positioning.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"11 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":"133894869","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.2012.6268738
Yun-teng Lu, A. Finger
The indoor ranging signal recovery requires not only the high detection probability but also the excellent detection accuracy, which is related to the matched filter output SINR, especially for radio signal based location and positioning systems, where little deviation of time delay will yield radical error of position estimation. Furthermore, the ranging signals in multi-measurment channels demands the so-called sparse condition [1] for uniquely determining the results. Because the corresponding recovery matrix in terms of time-code frame is a wide matrix, which can not be orthogonalized between all columns any more. So far, many related recovery algorithms haven been developed, like optimization based l1-norm minimization and greedy approaches based OMP, ROMP and CoSaMP [2]. However, these algorithms are either not real-time enough or short of uniform performance in different scenarios. In this paper we will first introduce the novel ranging signals for higher time delay estimation, then develop the corresponding detection algorithm namely Regularized Compressive Sampling Mathing Pursuit (RCoSaMP), which outperforms the most conventional detection approaches.
{"title":"Indoor ranging signal recovery via regularized CoSaMP","authors":"Yun-teng Lu, A. Finger","doi":"10.1109/WPNC.2012.6268738","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268738","url":null,"abstract":"The indoor ranging signal recovery requires not only the high detection probability but also the excellent detection accuracy, which is related to the matched filter output SINR, especially for radio signal based location and positioning systems, where little deviation of time delay will yield radical error of position estimation. Furthermore, the ranging signals in multi-measurment channels demands the so-called sparse condition [1] for uniquely determining the results. Because the corresponding recovery matrix in terms of time-code frame is a wide matrix, which can not be orthogonalized between all columns any more. So far, many related recovery algorithms haven been developed, like optimization based l1-norm minimization and greedy approaches based OMP, ROMP and CoSaMP [2]. However, these algorithms are either not real-time enough or short of uniform performance in different scenarios. In this paper we will first introduce the novel ranging signals for higher time delay estimation, then develop the corresponding detection algorithm namely Regularized Compressive Sampling Mathing Pursuit (RCoSaMP), which outperforms the most conventional detection approaches.","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":"133231373","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.2012.6268733
E. Laitinen, E. Lohan, J. Talvitie, Shweta Shrestha
This paper focuses on the WLAN-based indoor location by taking into account the contribution of each hearable Access Point (AP) in the location estimation. Typically, in many indoor scenarios of interest for the future location services, such as malls, shopping centers, airports or other transit hubs, the amount of hearable APs is huge and it is important to find out whether some of these APs are redundant for the purpose of location accuracy and may be dropped. Moreover, many APs nowadays are multi-antenna APs or support multiple MAC addresses coming from exactly the same location, thus it is likely that they may bring little or no benefit if keeping all in the positioning stage. The purpose of our paper is to address various significance measures in WLAN-based location and to compare them from the point of view of the accuracy of the location solution. The access point significance is studied both at the training stage and at the estimation stage. Our models are based on real measurement data.
{"title":"Access point significance measures in WLAN-based location","authors":"E. Laitinen, E. Lohan, J. Talvitie, Shweta Shrestha","doi":"10.1109/WPNC.2012.6268733","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268733","url":null,"abstract":"This paper focuses on the WLAN-based indoor location by taking into account the contribution of each hearable Access Point (AP) in the location estimation. Typically, in many indoor scenarios of interest for the future location services, such as malls, shopping centers, airports or other transit hubs, the amount of hearable APs is huge and it is important to find out whether some of these APs are redundant for the purpose of location accuracy and may be dropped. Moreover, many APs nowadays are multi-antenna APs or support multiple MAC addresses coming from exactly the same location, thus it is likely that they may bring little or no benefit if keeping all in the positioning stage. The purpose of our paper is to address various significance measures in WLAN-based location and to compare them from the point of view of the accuracy of the location solution. The access point significance is studied both at the training stage and at the estimation stage. Our models are based on real measurement data.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"11 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":"131506694","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.2012.6268737
J. Robles, Javier Supervia Pola, R. Lehnert
A localization algorithm that is often used by sensor nodes is Min-Max [1] [2]. This algorithm can be easily executed due to the fact that it principally consists of few additions, subtractions and logical comparisons. However, Min-Max provides a coarse position estimation. In our proposal we improve the accuracy of Min-Max by including simple extra operations. We compare the accuracy of our extended Min-Max (E-Min-Max) with other algorithms by using simulation.
{"title":"Extended Min-Max algorithm for position estimation in sensor networks","authors":"J. Robles, Javier Supervia Pola, R. Lehnert","doi":"10.1109/WPNC.2012.6268737","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268737","url":null,"abstract":"A localization algorithm that is often used by sensor nodes is Min-Max [1] [2]. This algorithm can be easily executed due to the fact that it principally consists of few additions, subtractions and logical comparisons. However, Min-Max provides a coarse position estimation. In our proposal we improve the accuracy of Min-Max by including simple extra operations. We compare the accuracy of our extended Min-Max (E-Min-Max) with other algorithms by using simulation.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"28 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":"128732677","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.2012.6268729
S. V. D. Velde, H. Wymeersch, H. Steendam
For large wireless networks, there is a need for accurate localization in a distributed manner. Several algorithms have been developed in order to achieve this goal. However, comparing different algorithms is hard because of the use of different network topologies and measurement models. In this paper two promising message passing algorithms, called SPAWN and SLEEP, are compared in terms of accuracy, complexity, and network traffic. To enable a fair comparison, several alterations are made to SLEEP resulting in ASLEEP with reduced network traffic and the incorporation of reference nodes. Simulations, using measurement models from real ultra-wideband equipment, show that ASLEEP is able to achieve similar estimation quality as SPAWN at much lower complexity and network traffic.
{"title":"Comparison of message passing algorithms for cooperative localization under NLOS conditions","authors":"S. V. D. Velde, H. Wymeersch, H. Steendam","doi":"10.1109/WPNC.2012.6268729","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268729","url":null,"abstract":"For large wireless networks, there is a need for accurate localization in a distributed manner. Several algorithms have been developed in order to achieve this goal. However, comparing different algorithms is hard because of the use of different network topologies and measurement models. In this paper two promising message passing algorithms, called SPAWN and SLEEP, are compared in terms of accuracy, complexity, and network traffic. To enable a fair comparison, several alterations are made to SLEEP resulting in ASLEEP with reduced network traffic and the incorporation of reference nodes. Simulations, using measurement models from real ultra-wideband equipment, show that ASLEEP is able to achieve similar estimation quality as SPAWN at much lower complexity and network traffic.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"61 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":"129197897","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.2012.6268741
Philipp Müller, S. Ali-Löytty, M. Dashti, Henri Nurminen, R. Piché
This paper proposes a novel Gaussian Mixture Filter (GMF) that allows components with negative weights. In the case of a ring-shaped likelihood function, the new filter keeps the number of components low by approximating the likelihood as a Gaussian mixture (GM) of two components, one with positive and the other with negative weight. In this article, the filter is applied to positioning with received signal strength (RSS) based range measurements. The filter is tested using simulated measurements, and the tests indicate that the new GMF outperforms the Extended Kalman Filter (EKF) in both accuracy and consistency.
{"title":"Gaussian mixture filter allowing negative weights and its application to positioning using signal strength measurements","authors":"Philipp Müller, S. Ali-Löytty, M. Dashti, Henri Nurminen, R. Piché","doi":"10.1109/WPNC.2012.6268741","DOIUrl":"https://doi.org/10.1109/WPNC.2012.6268741","url":null,"abstract":"This paper proposes a novel Gaussian Mixture Filter (GMF) that allows components with negative weights. In the case of a ring-shaped likelihood function, the new filter keeps the number of components low by approximating the likelihood as a Gaussian mixture (GM) of two components, one with positive and the other with negative weight. In this article, the filter is applied to positioning with received signal strength (RSS) based range measurements. The filter is tested using simulated measurements, and the tests indicate that the new GMF outperforms the Extended Kalman Filter (EKF) in both accuracy and consistency.","PeriodicalId":399340,"journal":{"name":"2012 9th Workshop on Positioning, Navigation and Communication","volume":"40 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":"129533072","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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