Pub Date : 2014-03-12DOI: 10.1109/WPNC.2014.6843309
Octavi Font, Guillem Francès, Anders Jonsson, P. Bartie, W. Mackaness
In this paper we present a novel probabilistic approach to activity recognition. Our approach is to estimate posterior probabilities of different activities using Bayes' rule. The approach can handle any type of activities as long as it is possible to estimate the conditional probabilities of potential observations, and easily scales to large numbers of activities. We test our approach empirically in an environment where observations are GPS signals of users moving around in a city.
{"title":"Probabilistic activity recognition in navigation","authors":"Octavi Font, Guillem Francès, Anders Jonsson, P. Bartie, W. Mackaness","doi":"10.1109/WPNC.2014.6843309","DOIUrl":"https://doi.org/10.1109/WPNC.2014.6843309","url":null,"abstract":"In this paper we present a novel probabilistic approach to activity recognition. Our approach is to estimate posterior probabilities of different activities using Bayes' rule. The approach can handle any type of activities as long as it is possible to estimate the conditional probabilities of potential observations, and easily scales to large numbers of activities. We test our approach empirically in an environment where observations are GPS signals of users moving around in a city.","PeriodicalId":106193,"journal":{"name":"2014 11th Workshop on Positioning, Navigation and Communication (WPNC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124942381","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 : 2014-03-12DOI: 10.1109/WPNC.2014.6843307
J. Lategahn, M. Müller, Christof Röhrig
Pedestrian localization systems require the knowledge of a user's position for manifold applications in indoor and outdoor environments. For this purpose several methods can be used, such as a Global Navigation Satellite System (GNSS) or an Inertial Navigation Systems (INS). Since GNSS are not available in indoor environments or street canyons a Time Difference of Arrival (TDoA) system and a low cost Inertial Measurement Unit (IMU), which consists of an accelerometer and a gyroscope, is used to estimate the position of a pedestrian. The localization device is mountable to different positions of the body, like the hip or the pocket of a shirt. The measurements of the IMU are prefiltered to get steps, the step length and fast changings in the user's orientation. To fuse the different measurement types an Extended Kalman Filter (EKF) is applied. To evaluate the algorithm experimental results are presented.
{"title":"Extended Kalman filter for a low cost TDoA/IMU pedestrian localization system","authors":"J. Lategahn, M. Müller, Christof Röhrig","doi":"10.1109/WPNC.2014.6843307","DOIUrl":"https://doi.org/10.1109/WPNC.2014.6843307","url":null,"abstract":"Pedestrian localization systems require the knowledge of a user's position for manifold applications in indoor and outdoor environments. For this purpose several methods can be used, such as a Global Navigation Satellite System (GNSS) or an Inertial Navigation Systems (INS). Since GNSS are not available in indoor environments or street canyons a Time Difference of Arrival (TDoA) system and a low cost Inertial Measurement Unit (IMU), which consists of an accelerometer and a gyroscope, is used to estimate the position of a pedestrian. The localization device is mountable to different positions of the body, like the hip or the pocket of a shirt. The measurements of the IMU are prefiltered to get steps, the step length and fast changings in the user's orientation. To fuse the different measurement types an Extended Kalman Filter (EKF) is applied. To evaluate the algorithm experimental results are presented.","PeriodicalId":106193,"journal":{"name":"2014 11th Workshop on Positioning, Navigation and Communication (WPNC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125176423","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 : 2014-03-12DOI: 10.1109/WPNC.2014.6843294
J. Robles, Gregory Cardenas-Mansilla, R. Lehnert
In many localization systems, the Mobile Node (MN) takes distance measurements with reference nodes called Anchors (ANs) in order to estimate its position. In general, the MN can obtain a better estimation when it takes measurements with multiple ANs. Unfortunately, this can lead to consume more energy and generate more traffic in the network. In this paper, we present an adaptive mechanism for the localization algorithm Extended Kalman Filter. Here, the MN decides the number of ANs to use according to measurable error indicators, which can be used to have an idea about the MN's position error. In this way, if the error indicator suggests that the position error was high in previous periods, then our Selective Extended Kalman Filter (S-EKF) will take measurements with more ANs in the next periods to improve the position accuracy.
{"title":"Adaptive selection of Anchors in the Extended Kalman Filter tracking algorithm","authors":"J. Robles, Gregory Cardenas-Mansilla, R. Lehnert","doi":"10.1109/WPNC.2014.6843294","DOIUrl":"https://doi.org/10.1109/WPNC.2014.6843294","url":null,"abstract":"In many localization systems, the Mobile Node (MN) takes distance measurements with reference nodes called Anchors (ANs) in order to estimate its position. In general, the MN can obtain a better estimation when it takes measurements with multiple ANs. Unfortunately, this can lead to consume more energy and generate more traffic in the network. In this paper, we present an adaptive mechanism for the localization algorithm Extended Kalman Filter. Here, the MN decides the number of ANs to use according to measurable error indicators, which can be used to have an idea about the MN's position error. In this way, if the error indicator suggests that the position error was high in previous periods, then our Selective Extended Kalman Filter (S-EKF) will take measurements with more ANs in the next periods to improve the position accuracy.","PeriodicalId":106193,"journal":{"name":"2014 11th Workshop on Positioning, Navigation and Communication (WPNC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133407703","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 : 2014-03-12DOI: 10.1109/WPNC.2014.6843296
Yubin Zhao, Yuan Yang, M. Kyas
Bayesian estimation methods are widely used for wireless localization systems. They employ priori information and current measurement error distribution models to derive the state of a mobile target. Cramér-Rao lower bound (CRLB) is a fundamental tool to analyze the performance of Bayesian estimators. Although CRLB is derived based on the measurement error distribution, only a few works have investigated the performance using priori information. In this paper, we derive the CRLB formulation in three cases by using the priori information: (1) fundamental Bayesian process; (2) recursive process; (3) adaptive process. These three processes represent the common Bayesian tracking algorithms for wireless system. Simulations are constructed to compare the localization performance according to the different processes. The results indicate how the priori information influences the location estimation and how to improve the performance according to different scenarios.
{"title":"Cramér-Rao lower bound analysis for wireless localization systems using priori information","authors":"Yubin Zhao, Yuan Yang, M. Kyas","doi":"10.1109/WPNC.2014.6843296","DOIUrl":"https://doi.org/10.1109/WPNC.2014.6843296","url":null,"abstract":"Bayesian estimation methods are widely used for wireless localization systems. They employ priori information and current measurement error distribution models to derive the state of a mobile target. Cramér-Rao lower bound (CRLB) is a fundamental tool to analyze the performance of Bayesian estimators. Although CRLB is derived based on the measurement error distribution, only a few works have investigated the performance using priori information. In this paper, we derive the CRLB formulation in three cases by using the priori information: (1) fundamental Bayesian process; (2) recursive process; (3) adaptive process. These three processes represent the common Bayesian tracking algorithms for wireless system. Simulations are constructed to compare the localization performance according to the different processes. The results indicate how the priori information influences the location estimation and how to improve the performance according to different scenarios.","PeriodicalId":106193,"journal":{"name":"2014 11th Workshop on Positioning, Navigation and Communication (WPNC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115701764","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 : 2014-03-12DOI: 10.1109/WPNC.2014.6843306
P. Henkel, Philipp Berthold, Jane Jean Kiam
Global Navigation Satellite System (GNSS) signals, inertial measurements (angular rates, accelerations) and magnetometer measurements complement each other for position determination: GNSS provides a precise and drift-free position solution but is susceptible to signal outages. Inertial measurements are continuously available and of higher rate but suffer from integration drifts. Magnetic field measurements provide an instantaneous orientation in static conditions but are affected by both static and dynamic disturbances. In this paper, we provide a calibration method for magnetometers, which determines the biases and misalignment errors of the magnetometer as well as the magnetic flux including static dis-turbances. The method uses the iterative Gauss-Newton method and precise attitude information (heading, pitch) obtained from two low-cost GPS receivers. The attitude determination requires a tree search of the carrier phase integer ambiguities using a priori information on the distance between both GPS receivers. We also verified the proposed method with kinematic measurements from the CMPS10 sensor. We observe an accuracy of a few degrees for the unfiltered heading and a heading offset of less than 10° in 99.5% of all measurement epochs.
{"title":"Calibration of magnetic field sensors with two mass-market GNSS receivers","authors":"P. Henkel, Philipp Berthold, Jane Jean Kiam","doi":"10.1109/WPNC.2014.6843306","DOIUrl":"https://doi.org/10.1109/WPNC.2014.6843306","url":null,"abstract":"Global Navigation Satellite System (GNSS) signals, inertial measurements (angular rates, accelerations) and magnetometer measurements complement each other for position determination: GNSS provides a precise and drift-free position solution but is susceptible to signal outages. Inertial measurements are continuously available and of higher rate but suffer from integration drifts. Magnetic field measurements provide an instantaneous orientation in static conditions but are affected by both static and dynamic disturbances. In this paper, we provide a calibration method for magnetometers, which determines the biases and misalignment errors of the magnetometer as well as the magnetic flux including static dis-turbances. The method uses the iterative Gauss-Newton method and precise attitude information (heading, pitch) obtained from two low-cost GPS receivers. The attitude determination requires a tree search of the carrier phase integer ambiguities using a priori information on the distance between both GPS receivers. We also verified the proposed method with kinematic measurements from the CMPS10 sensor. We observe an accuracy of a few degrees for the unfiltered heading and a heading offset of less than 10° in 99.5% of all measurement epochs.","PeriodicalId":106193,"journal":{"name":"2014 11th Workshop on Positioning, Navigation and Communication (WPNC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126372483","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 : 2014-03-03DOI: 10.1109/WPNC.2014.6843290
Siamak Yousefi, X. Chang, B. Champagne
In this paper, a 2-stage robust distributed algorithm is proposed for cooperative sensor network localization using time of arrival (TOA) data without identification of non-line of sight (NLOS) links. In the first stage, to overcome the effect of outliers, a convex relaxation of the Huber loss function is applied so that by using iterative optimization techniques, good estimates of the true sensor locations can be obtained. In the second stage, the original (non-relaxed) Huber cost function is further optimized to obtain refined location estimates based on those obtained in the first stage. In both stages, a simple gradient descent technique is used to carry out the optimization. Through simulations and real data analysis, it is shown that the proposed convex relaxation generally achieves a lower root mean squared error (RMSE) compared to other convex relaxation techniques in the literature. Also by doing the second stage, the position estimates are improved and we can achieve an RMSE close to that of the other distributed algorithms which know a priori which links are in NLOS.
{"title":"Distributed cooperative localization in wireless sensor networks without NLOS identification","authors":"Siamak Yousefi, X. Chang, B. Champagne","doi":"10.1109/WPNC.2014.6843290","DOIUrl":"https://doi.org/10.1109/WPNC.2014.6843290","url":null,"abstract":"In this paper, a 2-stage robust distributed algorithm is proposed for cooperative sensor network localization using time of arrival (TOA) data without identification of non-line of sight (NLOS) links. In the first stage, to overcome the effect of outliers, a convex relaxation of the Huber loss function is applied so that by using iterative optimization techniques, good estimates of the true sensor locations can be obtained. In the second stage, the original (non-relaxed) Huber cost function is further optimized to obtain refined location estimates based on those obtained in the first stage. In both stages, a simple gradient descent technique is used to carry out the optimization. Through simulations and real data analysis, it is shown that the proposed convex relaxation generally achieves a lower root mean squared error (RMSE) compared to other convex relaxation techniques in the literature. Also by doing the second stage, the position estimates are improved and we can achieve an RMSE close to that of the other distributed algorithms which know a priori which links are in NLOS.","PeriodicalId":106193,"journal":{"name":"2014 11th Workshop on Positioning, Navigation and Communication (WPNC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114292023","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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