Pub Date : 2012-10-11DOI: 10.1109/SDF.2012.6327908
R. Kaune
Time Difference of Arrival (TDOA) measurements are gained in a network of sensors where a minimum of two sensors is required. Sensors receive the signal of an emitting target and determine the Time of Arrival (TOA) of the signal. Calculating the difference between pairs of TOAs yields TDOA measurements which describe hyperbolae or hyperboloids as possible target locations. The Cramer Rao Lower bound (CRLB) gives the optimal attainable localization accuracy based on a measurement sequence. It depends on the measurement function which reflects the sensors-emitter geometry, the measurement error and the number of measurements. The CRLB can be used to find future trajectories for moving sensors. In this paper, a sensor pair consisting of a moving and a stationary sensor is investigated which takes TDOA measurements of a stationary emitting target. The future trajectory of the moving sensor is determined based on target localization performance.
{"title":"Finding sensor trajectories for TDOA based localization — Preliminary considerations","authors":"R. Kaune","doi":"10.1109/SDF.2012.6327908","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327908","url":null,"abstract":"Time Difference of Arrival (TDOA) measurements are gained in a network of sensors where a minimum of two sensors is required. Sensors receive the signal of an emitting target and determine the Time of Arrival (TOA) of the signal. Calculating the difference between pairs of TOAs yields TDOA measurements which describe hyperbolae or hyperboloids as possible target locations. The Cramer Rao Lower bound (CRLB) gives the optimal attainable localization accuracy based on a measurement sequence. It depends on the measurement function which reflects the sensors-emitter geometry, the measurement error and the number of measurements. The CRLB can be used to find future trajectories for moving sensors. In this paper, a sensor pair consisting of a moving and a stationary sensor is investigated which takes TDOA measurements of a stationary emitting target. The future trajectory of the moving sensor is determined based on target localization performance.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133586658","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 : 2012-10-11DOI: 10.1109/SDF.2012.6327912
D. Musicki, T. Song, Hyoung-Won Kim
We consider passive surveillance using time and frequency difference of arrival signals received by mobile receiver pairs. Signals received by a pair of receivers are correlated in time and frequency, followed by a detection process. In addition to the target (emitter) measurements, we may also create a number of spurious detections in each scan. This paper considers local (distributed) tracking using these measurements, with the main purpose of eliminating spurious measurements and enhancing the emitter detection.
{"title":"Distributed time and frequency difference of arrival tracking in clutter","authors":"D. Musicki, T. Song, Hyoung-Won Kim","doi":"10.1109/SDF.2012.6327912","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327912","url":null,"abstract":"We consider passive surveillance using time and frequency difference of arrival signals received by mobile receiver pairs. Signals received by a pair of receivers are correlated in time and frequency, followed by a detection process. In addition to the target (emitter) measurements, we may also create a number of spurious detections in each scan. This paper considers local (distributed) tracking using these measurements, with the main purpose of eliminating spurious measurements and enhancing the emitter detection.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123530815","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 : 2012-10-11DOI: 10.1109/SDF.2012.6327919
Sabine Schreiber-Ehle, W. Koch
In the ever growing literature on countering the cyber threat, the so-called JDL model of data fusion, well established in the information fusion community, has been applied to characterize the inner structure of problems within cyber defence and their mutual relationship. The overarching goal is to provide contributions to comprehensive cyber situational awareness by producing timely situation pictures. Cyber situational awareness, however, is prerequisite to taking appropriate actions, i.e. for “defence”. In this review paper, we provide an overview of what has been proposed in this context by various authors and collect basic insights published in the open literature. By doing so, we wish to provide an overview of the current discussion which reflects our own apprehension and prioritization. Moreover, we stress our opinion where relevant research questions are to be expected.
{"title":"The JDL model of data fusion applied to cyber-defence — A review paper","authors":"Sabine Schreiber-Ehle, W. Koch","doi":"10.1109/SDF.2012.6327919","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327919","url":null,"abstract":"In the ever growing literature on countering the cyber threat, the so-called JDL model of data fusion, well established in the information fusion community, has been applied to characterize the inner structure of problems within cyber defence and their mutual relationship. The overarching goal is to provide contributions to comprehensive cyber situational awareness by producing timely situation pictures. Cyber situational awareness, however, is prerequisite to taking appropriate actions, i.e. for “defence”. In this review paper, we provide an overview of what has been proposed in this context by various authors and collect basic insights published in the open literature. By doing so, we wish to provide an overview of the current discussion which reflects our own apprehension and prioritization. Moreover, we stress our opinion where relevant research questions are to be expected.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127573005","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 : 2012-10-11DOI: 10.1109/SDF.2012.6327904
Matthieu Canaud, L. Mihaylova, Nour-Eddin El Faouzi, Romain Billot, J. Sau
Prediction of traffic flow variables such as traffic volume, travel speed or travel time for a short time horizon is of paramount importance in traffic control. Hence, the data assimilation process in traffic modeling for estimation and prediction plays a key role. However, the increasing complexity, non-linearity and presence of various uncertainties (both in the measured data and models) are important factors affecting the traffic state prediction. To overcome this problem, new methodologies have been proposed. With this aim, in this paper we propose the use of the Probability Hypothesis Density (PHD) filter for traffic estimation. This methology is intensively studied, developed and improved for the purposes of multiple object tracking and consists in the recursive state estimation of several targets by using the information coming from an observation process. However, some issues need to be studied, especially the impact of the clutter (false alarm) intensity. The goal of this paper is to expose the potential of the PHD filters for real-time traffic state estimation and the choice of an appropriate clutter intensity. This investigation is based on a Cell Transmission Model (CTM) coupled with the PHD filter. It brings a novel tool to the state estimation problem and allows one to estimate the densities in traffic networks. In this work, we compare this PHD filter with the particle filter (PF) which has been successfully applied in traffic control and conclude that the PHD filter can be seen as a relevant alternative that opens new research avenues.
{"title":"A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter","authors":"Matthieu Canaud, L. Mihaylova, Nour-Eddin El Faouzi, Romain Billot, J. Sau","doi":"10.1109/SDF.2012.6327904","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327904","url":null,"abstract":"Prediction of traffic flow variables such as traffic volume, travel speed or travel time for a short time horizon is of paramount importance in traffic control. Hence, the data assimilation process in traffic modeling for estimation and prediction plays a key role. However, the increasing complexity, non-linearity and presence of various uncertainties (both in the measured data and models) are important factors affecting the traffic state prediction. To overcome this problem, new methodologies have been proposed. With this aim, in this paper we propose the use of the Probability Hypothesis Density (PHD) filter for traffic estimation. This methology is intensively studied, developed and improved for the purposes of multiple object tracking and consists in the recursive state estimation of several targets by using the information coming from an observation process. However, some issues need to be studied, especially the impact of the clutter (false alarm) intensity. The goal of this paper is to expose the potential of the PHD filters for real-time traffic state estimation and the choice of an appropriate clutter intensity. This investigation is based on a Cell Transmission Model (CTM) coupled with the PHD filter. It brings a novel tool to the state estimation problem and allows one to estimate the densities in traffic networks. In this work, we compare this PHD filter with the particle filter (PF) which has been successfully applied in traffic control and conclude that the PHD filter can be seen as a relevant alternative that opens new research avenues.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131510992","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 : 2012-10-11DOI: 10.1109/SDF.2012.6327910
Fotios Katsilieris, A. Charlish, Y. Boers
Multifunction radars are highly configurable and possess some form of beam agility, allowing maintenance of a large number of tasks supporting varied functions. However, the surveillance function is commonly executed using a fixed periodic pattern, not utilising the full hardware potential. In this paper, a new method of surveillance control is proposed which utilises a particle filter to estimate a probability density of the undetected target location. Subsequently, the finite resource available for surveillance is allocated between sectors, based on information extracted from this probability density, using the Continuous Double Auction Parameter Selection algorithm. This method is successfully demonstrated through simulation on a surveillance control problem.
{"title":"Towards an online, adaptive algorithm for radar surveillance control","authors":"Fotios Katsilieris, A. Charlish, Y. Boers","doi":"10.1109/SDF.2012.6327910","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327910","url":null,"abstract":"Multifunction radars are highly configurable and possess some form of beam agility, allowing maintenance of a large number of tasks supporting varied functions. However, the surveillance function is commonly executed using a fixed periodic pattern, not utilising the full hardware potential. In this paper, a new method of surveillance control is proposed which utilises a particle filter to estimate a probability density of the undetected target location. Subsequently, the finite resource available for surveillance is allocated between sectors, based on information extracted from this probability density, using the Continuous Double Auction Parameter Selection algorithm. This method is successfully demonstrated through simulation on a surveillance control problem.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128805123","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 : 2012-10-11DOI: 10.1109/SDF.2012.6327907
D. Nagel, Stephen Smith
An airborne radar sensor operating in Ground Moving Target Indicator (GMTI) mode is able to distinguish between airborne targets and ground moving targets. Further, it is possible to separate stationary from moving ground targets. For military radar applications, it is desirable that the GMTI mode be extended to allow classification of detected ground targets. In addition, such an extension should permit classification of helicopters. A model-based classification algorithm suitable for GMTI processing as well as for Doppler signal evaluation is presented, which outputs a likelihood vector and, because it uses only signals in the exo-clutter region (clutter-free region of the range-Doppler domain), does not require STAP-processing.
{"title":"Creating a likelihood vector for ground moving targets in the exo-clutter region of airborne radar signals","authors":"D. Nagel, Stephen Smith","doi":"10.1109/SDF.2012.6327907","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327907","url":null,"abstract":"An airborne radar sensor operating in Ground Moving Target Indicator (GMTI) mode is able to distinguish between airborne targets and ground moving targets. Further, it is possible to separate stationary from moving ground targets. For military radar applications, it is desirable that the GMTI mode be extended to allow classification of detected ground targets. In addition, such an extension should permit classification of helicopters. A model-based classification algorithm suitable for GMTI processing as well as for Doppler signal evaluation is presented, which outputs a likelihood vector and, because it uses only signals in the exo-clutter region (clutter-free region of the range-Doppler domain), does not require STAP-processing.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122667814","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 : 2012-10-11DOI: 10.1109/SDF.2012.6327900
C. Degen, F. Govaers, W. Koch
The so-called lack of memory is an inherent challenge of the probability hypothesis density (PHD) filter and leads to the fact that only targets which rely on a currently available measurement can securely be reported as present in the respective iteration. Yet there is no method presented that enables the sequential Monte Carlo (SMC) version of the intensity filter (iFilter) to manage failure of measurements. In this paper we develop a procedure and a complete implementation scheme within the SMC-iFilter to detect failure of measurements and to generate so-called pseudo measurements, which are used to estimate the state of targets, belonging to missing measurements. To assess the developed method with respect to accuracy a numerical study is carried out, using a simulation of a linear multi-object scenario.
{"title":"Track maintenance using the SMC-intensity filter","authors":"C. Degen, F. Govaers, W. Koch","doi":"10.1109/SDF.2012.6327900","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327900","url":null,"abstract":"The so-called lack of memory is an inherent challenge of the probability hypothesis density (PHD) filter and leads to the fact that only targets which rely on a currently available measurement can securely be reported as present in the respective iteration. Yet there is no method presented that enables the sequential Monte Carlo (SMC) version of the intensity filter (iFilter) to manage failure of measurements. In this paper we develop a procedure and a complete implementation scheme within the SMC-iFilter to detect failure of measurements and to generate so-called pseudo measurements, which are used to estimate the state of targets, belonging to missing measurements. To assess the developed method with respect to accuracy a numerical study is carried out, using a simulation of a linear multi-object scenario.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122622129","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 : 2012-10-11DOI: 10.1109/SDF.2012.6327901
A. Lepoutre, O. Rabaste, F. Gland
In this paper, we address the problem of detecting the appearance time of a target and tracking its state with a particle filter in the Track-Before-Detect context. We show that it is possible to model the problem as a quickest detection change problem in a Bayesian framework. In this case, the posterior density of the target time appearance is a mixture where each component represents the hypothesis that the target arrived at a given time. As the posterior density is intractable in practice, we propose to approximate each component of the mixture by a particle filter, and we show that the weights of the mixture can be computed recursively thanks to quantities provided by the different particle filters. The overall filter yields good performance.
{"title":"A particle filter for target arrival detection and tracking in Track-Before-Detect","authors":"A. Lepoutre, O. Rabaste, F. Gland","doi":"10.1109/SDF.2012.6327901","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327901","url":null,"abstract":"In this paper, we address the problem of detecting the appearance time of a target and tracking its state with a particle filter in the Track-Before-Detect context. We show that it is possible to model the problem as a quickest detection change problem in a Bayesian framework. In this case, the posterior density of the target time appearance is a mixture where each component represents the hypothesis that the target arrived at a given time. As the posterior density is intractable in practice, we propose to approximate each component of the mixture by a particle filter, and we show that the weights of the mixture can be computed recursively thanks to quantities provided by the different particle filters. The overall filter yields good performance.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122703183","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 : 2012-10-11DOI: 10.1109/SDF.2012.6327902
Nikolay Petrov, M. Ulmke, L. Mihaylova, A. Gning, M. Schikora, Monika Wieneke, W. Koch
This paper considers the challenging task of realtime extended object tracking using cluttered measurements from laser range scanners. The performance of the recently proposed Box Particle Filter (Box PF) algorithm is evaluated utilising real measurements from laser range scanners obtained within a prototype security system replicating an airport corridor. The problem is expressed as the joint estimation of both state and parameters of an extended target. Circularly and elliptically shaped targets are considered. Promising results are presented.
{"title":"On the performance of the Box Particle Filter for extended object tracking using laser data","authors":"Nikolay Petrov, M. Ulmke, L. Mihaylova, A. Gning, M. Schikora, Monika Wieneke, W. Koch","doi":"10.1109/SDF.2012.6327902","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327902","url":null,"abstract":"This paper considers the challenging task of realtime extended object tracking using cluttered measurements from laser range scanners. The performance of the recently proposed Box Particle Filter (Box PF) algorithm is evaluated utilising real measurements from laser range scanners obtained within a prototype security system replicating an airport corridor. The problem is expressed as the joint estimation of both state and parameters of an extended target. Circularly and elliptically shaped targets are considered. Promising results are presented.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124630948","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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