Pub Date : 2016-10-01DOI: 10.1109/WPNC.2016.7822839
Cristian Pana, S. Severi, G. Abreu
Accurate and flexible probability density estimation is fundamental in machine learning tasks, in classification and routine data analyses applications. In this paper we propose an adaptive version of the Histogram Trend Filtering (HTF), which is a relatively new method used for non-parametric density estimation. This technique enjoys low computational complexity, while being able to automatically detect abrupt changes in the underlying dynamics of the estimated distribution. Therefore, it can deal with estimating both stationary and non-stationary distributions.
{"title":"An adaptive approach to non-parametric estimation of dynamic probability density functions","authors":"Cristian Pana, S. Severi, G. Abreu","doi":"10.1109/WPNC.2016.7822839","DOIUrl":"https://doi.org/10.1109/WPNC.2016.7822839","url":null,"abstract":"Accurate and flexible probability density estimation is fundamental in machine learning tasks, in classification and routine data analyses applications. In this paper we propose an adaptive version of the Histogram Trend Filtering (HTF), which is a relatively new method used for non-parametric density estimation. This technique enjoys low computational complexity, while being able to automatically detect abrupt changes in the underlying dynamics of the estimated distribution. Therefore, it can deal with estimating both stationary and non-stationary distributions.","PeriodicalId":148664,"journal":{"name":"2016 13th Workshop on Positioning, Navigation and Communications (WPNC)","volume":"552 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116397130","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 : 2016-10-01DOI: 10.1109/WPNC.2016.7822843
Ernő Horváth, C. Pozna
In vehicle and robot navigation low-level tasks such as path planning, obstacle avoidance and autonomous operation are extensively studied nowadays. Most of these task require map building. In this paper a map representation is discussed with the focus for the singular domain of our Neobotix MP500 mobile robot. Among others the state of the art map building techniques will be introduced such as topological map, line map, landmark-based map and of course in more detail the occupancy grid based map. The probabilistic representation of the occupancy grid will be examined as a map building problem for the given mobile robot.
{"title":"Probabilistic occupancy grid map building for Neobotix MP500 robot","authors":"Ernő Horváth, C. Pozna","doi":"10.1109/WPNC.2016.7822843","DOIUrl":"https://doi.org/10.1109/WPNC.2016.7822843","url":null,"abstract":"In vehicle and robot navigation low-level tasks such as path planning, obstacle avoidance and autonomous operation are extensively studied nowadays. Most of these task require map building. In this paper a map representation is discussed with the focus for the singular domain of our Neobotix MP500 mobile robot. Among others the state of the art map building techniques will be introduced such as topological map, line map, landmark-based map and of course in more detail the occupancy grid based map. The probabilistic representation of the occupancy grid will be examined as a map building problem for the given mobile robot.","PeriodicalId":148664,"journal":{"name":"2016 13th Workshop on Positioning, Navigation and Communications (WPNC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116971875","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 : 2016-10-01DOI: 10.1109/WPNC.2016.7822845
Christopher J. Lowrance, Adrian P. Lauf
A robot is sometimes unable to obtain its precise location, or the location of its neighbors, due to the limitations of the global positioning system (GPS) or due to positioning information not being exchanged between neighbors. In either scenario, received radio signals can be used by a robot to infer positioning information, but the process is challenging because of the dynamics of radio wave propagation. To mitigate these effects and improve accuracy, many of the existing methods for inferring the direction of arrival (DoA) of radio signals impose unrealistic resource demands on robots. For instance, the schemes either require rotating directional antenna systems to be mounted on robots, or they depend upon other collaborative systems, with known locations, to assist in localization. However, in reality, robots usually do not have sufficient resources, in terms of space and energy, to support the mounting of directional antennas, nor are they guaranteed to have the luxury of collaborative team members. As a more practical alternative, this paper presents a coarse-grained method of finding the relative direction of a transmitter using minimal resources. Specifically, the approach only depends upon a robot's ability to move in an approximate triangular pattern while sampling the received signal strength indicator (RSSI) from its off-the-shelf radio. The RSSI samples are uniquely processed using a combination of regression and vector analysis to estimate the DoA. The proposed technique was evaluated in simulation, as well as in the real world using an actual robot. The evaluation shows an average accuracy improvement of over 30 degrees, as well as a reduction of over 80% in sampling movement, when compared to related work.
{"title":"Direction of arrival estimation for robots using radio signal strength and mobility","authors":"Christopher J. Lowrance, Adrian P. Lauf","doi":"10.1109/WPNC.2016.7822845","DOIUrl":"https://doi.org/10.1109/WPNC.2016.7822845","url":null,"abstract":"A robot is sometimes unable to obtain its precise location, or the location of its neighbors, due to the limitations of the global positioning system (GPS) or due to positioning information not being exchanged between neighbors. In either scenario, received radio signals can be used by a robot to infer positioning information, but the process is challenging because of the dynamics of radio wave propagation. To mitigate these effects and improve accuracy, many of the existing methods for inferring the direction of arrival (DoA) of radio signals impose unrealistic resource demands on robots. For instance, the schemes either require rotating directional antenna systems to be mounted on robots, or they depend upon other collaborative systems, with known locations, to assist in localization. However, in reality, robots usually do not have sufficient resources, in terms of space and energy, to support the mounting of directional antennas, nor are they guaranteed to have the luxury of collaborative team members. As a more practical alternative, this paper presents a coarse-grained method of finding the relative direction of a transmitter using minimal resources. Specifically, the approach only depends upon a robot's ability to move in an approximate triangular pattern while sampling the received signal strength indicator (RSSI) from its off-the-shelf radio. The RSSI samples are uniquely processed using a combination of regression and vector analysis to estimate the DoA. The proposed technique was evaluated in simulation, as well as in the real world using an actual robot. The evaluation shows an average accuracy improvement of over 30 degrees, as well as a reduction of over 80% in sampling movement, when compared to related work.","PeriodicalId":148664,"journal":{"name":"2016 13th Workshop on Positioning, Navigation and Communications (WPNC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128598406","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 : 2016-10-01DOI: 10.1109/WPNC.2016.7822850
M. Ulmschneider, R. Raulefs, C. Gentner, M. Walter
Precise localization and tracking in intelligent transportation systems has aroused great interest since it is required in a large variety of applications. The positioning accuracy of global navigation satellite systems is unreliable and insufficient enough for many use cases. In urban canyons or tunnels, the positioning performance degrades due to a low received signal power, multipath propagation, or signal blocking. Instead we exploit the ubiquitous access to cellular mobile radio networks. Cellular networks are designed to cover the access to the network in an area by a single link to reduce the risk of interference from neighboring base stations. The idea of Channel-SLAM is to exploit the numerous multipath components (MPCs) of a radio signal arriving at the receiver for positioning. Each MPC can be regarded as being sent from a virtual transmitter in a pure line-of-sight condition. Within this paper, we show how to apply multipath assisted positioning in an urban scenario. Therefore, we analyze how a road user equipped with a circular antenna array is tracked in an urban scenario in the presence of only one physical transmitter. We further jointly estimate the positions of the physical and the virtual transmitters to enrich maps.
{"title":"Multipath assisted positioning in vehicular applications","authors":"M. Ulmschneider, R. Raulefs, C. Gentner, M. Walter","doi":"10.1109/WPNC.2016.7822850","DOIUrl":"https://doi.org/10.1109/WPNC.2016.7822850","url":null,"abstract":"Precise localization and tracking in intelligent transportation systems has aroused great interest since it is required in a large variety of applications. The positioning accuracy of global navigation satellite systems is unreliable and insufficient enough for many use cases. In urban canyons or tunnels, the positioning performance degrades due to a low received signal power, multipath propagation, or signal blocking. Instead we exploit the ubiquitous access to cellular mobile radio networks. Cellular networks are designed to cover the access to the network in an area by a single link to reduce the risk of interference from neighboring base stations. The idea of Channel-SLAM is to exploit the numerous multipath components (MPCs) of a radio signal arriving at the receiver for positioning. Each MPC can be regarded as being sent from a virtual transmitter in a pure line-of-sight condition. Within this paper, we show how to apply multipath assisted positioning in an urban scenario. Therefore, we analyze how a road user equipped with a circular antenna array is tracked in an urban scenario in the presence of only one physical transmitter. We further jointly estimate the positions of the physical and the virtual transmitters to enrich maps.","PeriodicalId":148664,"journal":{"name":"2016 13th Workshop on Positioning, Navigation and Communications (WPNC)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123751807","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 : 2016-10-01DOI: 10.1109/WPNC.2016.7822842
Talmon Alexandri, R. Diamant
We present a partial observable, non-linear solution for AUV aided navigation, referred as the Reverse Bearing Only Target Motion Analysis (Reverse BO-TMA). Reverse BO-TMA utilizes information about the course and speed of a passing vessel to provide a passive method for self localization of an Autonomous Underwater Vehicle (AUV). In Reverse BO-TMA, the AUV relies on radiated noise from the vessel to measure the bearing to the target vessel. Compared to traditional range-based localization methods, Reverse BO-TMA is a fully passive method that allows the AUV to remain farther from the anchor and does not require collaboration or message exchange in the form of time-synchronization between the AUV and the vessel. We formalize the Reverse BO-TMA, and solve it through least squares optimization. Numerical results show that the Reverse BO-TMA provides accurate localization performance that can greatly increase the accuracy of AUV navigation during long term deployments.
{"title":"A Reverse Bearings Only Target Motion Analysis (BO-TMA) for improving AUV navigation accuracy","authors":"Talmon Alexandri, R. Diamant","doi":"10.1109/WPNC.2016.7822842","DOIUrl":"https://doi.org/10.1109/WPNC.2016.7822842","url":null,"abstract":"We present a partial observable, non-linear solution for AUV aided navigation, referred as the Reverse Bearing Only Target Motion Analysis (Reverse BO-TMA). Reverse BO-TMA utilizes information about the course and speed of a passing vessel to provide a passive method for self localization of an Autonomous Underwater Vehicle (AUV). In Reverse BO-TMA, the AUV relies on radiated noise from the vessel to measure the bearing to the target vessel. Compared to traditional range-based localization methods, Reverse BO-TMA is a fully passive method that allows the AUV to remain farther from the anchor and does not require collaboration or message exchange in the form of time-synchronization between the AUV and the vessel. We formalize the Reverse BO-TMA, and solve it through least squares optimization. Numerical results show that the Reverse BO-TMA provides accurate localization performance that can greatly increase the accuracy of AUV navigation during long term deployments.","PeriodicalId":148664,"journal":{"name":"2016 13th Workshop on Positioning, Navigation and Communications (WPNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129880265","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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