Pub Date : 2012-10-11DOI: 10.1109/SDF.2012.6327905
M. Mertens, M. Ulmke
In ground target tracking based on kinematic measurements by airborne radar, several challenges in general strongly deteriorate the performance of any standard tracking filter. The major challenges are imprecise measurements and missed detections, a strong false alarm background, closely-spaced targets, technical and terrain obscuration, and complex target motion. In order to counterbalance such a performance degradation, target attribute and context information can be incorporated into the tracking process. One such target attribute information is provided by the signal strength measurement, which is readily available as it is a standard output of a modern radar system. Signal strength information can be used to estimate the radar cross section (RCS) of a ground moving target. For this method to work, the fluctuations of the target RCS are assumed to follow the analytically tractable Swerling-I and Swerling-III cases. In the present work, the RCS estimation scheme is implemented into the Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filter. The performance of the resulting algorithm is then analyzed based on single and multiple-target simulation scenarios.
{"title":"Ground moving target tracking using signal strength measurements with the GM-CPHD filter","authors":"M. Mertens, M. Ulmke","doi":"10.1109/SDF.2012.6327905","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327905","url":null,"abstract":"In ground target tracking based on kinematic measurements by airborne radar, several challenges in general strongly deteriorate the performance of any standard tracking filter. The major challenges are imprecise measurements and missed detections, a strong false alarm background, closely-spaced targets, technical and terrain obscuration, and complex target motion. In order to counterbalance such a performance degradation, target attribute and context information can be incorporated into the tracking process. One such target attribute information is provided by the signal strength measurement, which is readily available as it is a standard output of a modern radar system. Signal strength information can be used to estimate the radar cross section (RCS) of a ground moving target. For this method to work, the fluctuations of the target RCS are assumed to follow the analytically tractable Swerling-I and Swerling-III cases. In the present work, the RCS estimation scheme is implemented into the Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filter. The performance of the resulting algorithm is then analyzed based on single and multiple-target simulation scenarios.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"29 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":"125975429","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.2012.6327913
Christian Steffes, Sven Rau
In this paper, we investigate a Frequency Difference of Arrival (FDOA) based localization scenario with a stationary sensor network and one moving emitter. A method for Frequency of Arrival (FOA) determination of ADS-B transponder messages is introduced. The FDOA of a message received at a sensor pair can be calculated from the corresponding FOAs. This method decreases the communication requirements drastically as the need to transmit the received signals to a reference sensor or fusion center is eliminated. The accuracy of FDOA calculation is determined for simulated as well as for real measurement data.
{"title":"FDOA determination of ADS-B transponder signals","authors":"Christian Steffes, Sven Rau","doi":"10.1109/SDF.2012.6327913","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327913","url":null,"abstract":"In this paper, we investigate a Frequency Difference of Arrival (FDOA) based localization scenario with a stationary sensor network and one moving emitter. A method for Frequency of Arrival (FOA) determination of ADS-B transponder messages is introduced. The FDOA of a message received at a sensor pair can be calculated from the corresponding FOAs. This method decreases the communication requirements drastically as the need to transmit the received signals to a reference sensor or fusion center is eliminated. The accuracy of FDOA calculation is determined for simulated as well as for real measurement data.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"47 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":"126590621","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.2012.6327911
J. Seitz, Thorsten Vaupel, S. Haimerl, J. G. Boronat, J. Thielecke
A new approach for estimating and tracking the azimuth angle regarding north and a two-dimensional position of a mobile unit is presented. Outdoors, the azimuth angle of a device can be easily detected using an electronic compass and the position can be calculated using a global navigation satellite system (GNSS). Indoors, magnetic disturbances lead to unreliable compass outputs. Also, indoors there exists no standard positioning system like GNSS outdoors. The presented approach is based on Wi-Fi signal strength measurements collected by four horizontally arranged directional antennas. To proof the concept the well known Wi-Fi fingerprinting based on the normalized Euclidean distance in signal space has been adopted. A test with measurements collected in a laboratory demonstrates the feasibility of the approach. Especially in indoor environments this facilitates the use of electronic guides that offer additional information by means of augmented reality, e.g. on museum exhibits in visual range.
{"title":"Wi-Fi azimuth and position tracking using directional received signal strength measurements","authors":"J. Seitz, Thorsten Vaupel, S. Haimerl, J. G. Boronat, J. Thielecke","doi":"10.1109/SDF.2012.6327911","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327911","url":null,"abstract":"A new approach for estimating and tracking the azimuth angle regarding north and a two-dimensional position of a mobile unit is presented. Outdoors, the azimuth angle of a device can be easily detected using an electronic compass and the position can be calculated using a global navigation satellite system (GNSS). Indoors, magnetic disturbances lead to unreliable compass outputs. Also, indoors there exists no standard positioning system like GNSS outdoors. The presented approach is based on Wi-Fi signal strength measurements collected by four horizontally arranged directional antennas. To proof the concept the well known Wi-Fi fingerprinting based on the normalized Euclidean distance in signal space has been adopted. A test with measurements collected in a laboratory demonstrates the feasibility of the approach. Especially in indoor environments this facilitates the use of electronic guides that offer additional information by means of augmented reality, e.g. on museum exhibits in visual range.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"79 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":"125543502","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.2012.6327906
R. Streit
A traffic intensity filter is derived using a probability generating functional approach. Traffic filters estimate, or map, the mean rate at which different regions of state space generate target detection opportunities in a field of distributed sensors. They are Bayesian filters that incorporate sensor measurement likelihood functions and target detection capabilities. Traffic maps contribute to situational awareness for heterogeneous sensor fields. They are practical for applications with large numbers of sensors because their computational complexity is linear in the numbers of sensors and measurements.
{"title":"Multisensor traffic mapping filters","authors":"R. Streit","doi":"10.1109/SDF.2012.6327906","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327906","url":null,"abstract":"A traffic intensity filter is derived using a probability generating functional approach. Traffic filters estimate, or map, the mean rate at which different regions of state space generate target detection opportunities in a field of distributed sensors. They are Bayesian filters that incorporate sensor measurement likelihood functions and target detection capabilities. Traffic maps contribute to situational awareness for heterogeneous sensor fields. They are practical for applications with large numbers of sensors because their computational complexity is linear in the numbers of sensors and measurements.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"188 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":"121119016","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.2012.6327916
J. Sander, G. Schneider, B. Essendorfer, A. Kuwertz
Prevention and management of damage scenarios require adequate situation awareness to make timely, coordinated, and proactive decisions possible. The stakeholders must be able to access and to comprehend relevant information quickly and with justifiable effort. The resulting challenges for intelligence, surveillance, and reconnaissance (ISR) lie not only in the new and further development of individual sensor and exploitation systems but also in interoperable system networking as well as in the realization of adequate strategies for the collection, processing, dissemination, and presentation of data and information products [1], [2], [3], [4]. In this publication, we present a high level architecture for ISR analytics that complies with these observations. It provides the functionality to customize the system precisely to specific scenarios of the ISR domain. We give a more detailed insight into concepts and approaches that are essential for specific architecture components.
{"title":"ISR analytics: Architectural and methodic concepts","authors":"J. Sander, G. Schneider, B. Essendorfer, A. Kuwertz","doi":"10.1109/SDF.2012.6327916","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327916","url":null,"abstract":"Prevention and management of damage scenarios require adequate situation awareness to make timely, coordinated, and proactive decisions possible. The stakeholders must be able to access and to comprehend relevant information quickly and with justifiable effort. The resulting challenges for intelligence, surveillance, and reconnaissance (ISR) lie not only in the new and further development of individual sensor and exploitation systems but also in interoperable system networking as well as in the realization of adequate strategies for the collection, processing, dissemination, and presentation of data and information products [1], [2], [3], [4]. In this publication, we present a high level architecture for ISR analytics that complies with these observations. It provides the functionality to customize the system precisely to specific scenarios of the ISR domain. We give a more detailed insight into concepts and approaches that are essential for specific architecture components.","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":"133479178","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.2012.6327903
B. Ristic, Daniel E. Clark, N. Gordon
Tracking algorithms are based on models: target dynamic and sensor measurement model. In most practical situations the two models are not known exactly and are typically parametrised by an unknown random vector θ. The paper proposes a Bayesian algorithm based on importance sampling for the estimation of θ. The input are detections/measurements collected by the tracking system from non-cooperative targets. The algorithm relies on the particle filter implementation of the probability density hypothesis (PHD) filter to evaluate the likelihood of the measurement set history conditioned on θ. As a byproduct, the proposed algorithm can also output a multi-target state estimate over time. An application to sensor bias estimation is presented in detail as an illustration. The resulting sensor-bias estimation method is applicable to asynchronous sensors and does not require prior knowledge of measurement-to-track associations.
{"title":"Calibration of tracking systems using detections from non-cooperative targets","authors":"B. Ristic, Daniel E. Clark, N. Gordon","doi":"10.1109/SDF.2012.6327903","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327903","url":null,"abstract":"Tracking algorithms are based on models: target dynamic and sensor measurement model. In most practical situations the two models are not known exactly and are typically parametrised by an unknown random vector θ. The paper proposes a Bayesian algorithm based on importance sampling for the estimation of θ. The input are detections/measurements collected by the tracking system from non-cooperative targets. The algorithm relies on the particle filter implementation of the probability density hypothesis (PHD) filter to evaluate the likelihood of the measurement set history conditioned on θ. As a byproduct, the proposed algorithm can also output a multi-target state estimate over time. An application to sensor bias estimation is presented in detail as an illustration. The resulting sensor-bias estimation method is applicable to asynchronous sensors and does not require prior knowledge of measurement-to-track associations.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"13 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":"125201349","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.2012.6327899
James K. Murphy, S. Godsill
This paper introduces a new method of simultaneous object tracking (localization) and environment mapping for objects moving in a potential feld environment. Only weak non-parametric assumptions are made about the shape of the potential function using a Gaussian process prior. A second-and-a-half order numerical scheme for object motion in a potential feld is formulated and it is shown how to use this for potential inference. The method improves tracking performance in structured environments, as is illustrated by its application to urban car tracking. Hidden environmental structure such as the location of obstructions can also be revealed. Prior knowledge (e.g. from maps) can easily be incorporated and can then be updated using feedback from tracking. Information from multiple targets can also be handled in a straightforward manner.
{"title":"Simultaneous localization and mapping for non-parametric potential field environments","authors":"James K. Murphy, S. Godsill","doi":"10.1109/SDF.2012.6327899","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327899","url":null,"abstract":"This paper introduces a new method of simultaneous object tracking (localization) and environment mapping for objects moving in a potential feld environment. Only weak non-parametric assumptions are made about the shape of the potential function using a Gaussian process prior. A second-and-a-half order numerical scheme for object motion in a potential feld is formulated and it is shown how to use this for potential inference. The method improves tracking performance in structured environments, as is illustrated by its application to urban car tracking. Hidden environmental structure such as the location of obstructions can also be revealed. Prior knowledge (e.g. from maps) can easily be incorporated and can then be updated using feedback from tracking. Information from multiple targets can also be handled in a straightforward manner.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"85 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":"114172758","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.2012.6327917
E. Salerno, T. Singh, P. Singla, Maria Scalzo-Cornacchia, A. Bubalo, M. Alford, Eric K. Jones
Knowledge of roadmaps can provide an indication of how information, materials, and people move. Historically, maps have equated to a static look at a network that contains only established and sanctioned routes. Even now, Google map images and hand held Global Positioning System (GPS) units represent a somewhat static look at roadmaps, requiring either recapturing images or manually updating units. In order to have a more up to date, information rich representation of transportation networks or roadmaps this effort has explored the use of movement information, specifically Ground Moving Target Indicator (GMTI) data, for accurately estimating the topology of these networks. This data lends itself to being able to provide not only a single snapshot of the topology of the network, but to provide additional information concerning densities and direction of movement through the network. The novel approach employed for synthesizing the data into a complete estimate of the network is through the use of Hough transforms to identify line segments which collectively represent the road network. Then the total least squares is used to characterize the uncertainty associated with this line segment represPentation of the road network.
{"title":"Road network identification by means of the Hough transform","authors":"E. Salerno, T. Singh, P. Singla, Maria Scalzo-Cornacchia, A. Bubalo, M. Alford, Eric K. Jones","doi":"10.1109/SDF.2012.6327917","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327917","url":null,"abstract":"Knowledge of roadmaps can provide an indication of how information, materials, and people move. Historically, maps have equated to a static look at a network that contains only established and sanctioned routes. Even now, Google map images and hand held Global Positioning System (GPS) units represent a somewhat static look at roadmaps, requiring either recapturing images or manually updating units. In order to have a more up to date, information rich representation of transportation networks or roadmaps this effort has explored the use of movement information, specifically Ground Moving Target Indicator (GMTI) data, for accurately estimating the topology of these networks. This data lends itself to being able to provide not only a single snapshot of the topology of the network, but to provide additional information concerning densities and direction of movement through the network. The novel approach employed for synthesizing the data into a complete estimate of the network is through the use of Hough transforms to identify line segments which collectively represent the road network. Then the total least squares is used to characterize the uncertainty associated with this line segment represPentation of the road network.","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":"131174999","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.2012.6327914
Christian Steffes, Sven Rau
In this paper, we investigate the detection of multi-path signal propagation in a Time Difference of Arrival (TDOA) localization scenario. Usually, TDOA measurements are obtained by determining the absolute maximum of the cross correlation function of signals recorded at different sensor nodes in a sensor network. Multipath signal propagation causes multiple peaks in the cross correlation function which lead to erroneous emitter localization. We use hypotheses of possible multipath signal propagation calculated from the autocorrelation functions to identify the line of sight (LOS) peak in the cross correlation function of a sensor pair.
{"title":"Multipath detection in TDOA localization scenarios","authors":"Christian Steffes, Sven Rau","doi":"10.1109/SDF.2012.6327914","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327914","url":null,"abstract":"In this paper, we investigate the detection of multi-path signal propagation in a Time Difference of Arrival (TDOA) localization scenario. Usually, TDOA measurements are obtained by determining the absolute maximum of the cross correlation function of signals recorded at different sensor nodes in a sensor network. Multipath signal propagation causes multiple peaks in the cross correlation function which lead to erroneous emitter localization. We use hypotheses of possible multipath signal propagation calculated from the autocorrelation functions to identify the line of sight (LOS) peak in the cross correlation function of a sensor pair.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"15 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":"134559052","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.2012.6327909
B. Pannetier, J. Dezert
We propose a new method to track maneuvering ground targets and correct the ground tactical situation. The method developed in this work improves the performances of Structured-Branching Multiple Hypothesis Tracker (SB-MHT) and reduces the incorrect track deletions in tracks maintenance with a new Track Segment Association (TSA) algorithm taking into account both kinematic and classification information. The performances of this method are quantified on a realistic simulated scenario involving twenty maneuvering ground targets observed by an airborne with a Ground Moving Target Indicator (GMTI) sensor and Unattended Ground Sensor (UGS).
{"title":"Track segment association with classification information","authors":"B. Pannetier, J. Dezert","doi":"10.1109/SDF.2012.6327909","DOIUrl":"https://doi.org/10.1109/SDF.2012.6327909","url":null,"abstract":"We propose a new method to track maneuvering ground targets and correct the ground tactical situation. The method developed in this work improves the performances of Structured-Branching Multiple Hypothesis Tracker (SB-MHT) and reduces the incorrect track deletions in tracks maintenance with a new Track Segment Association (TSA) algorithm taking into account both kinematic and classification information. The performances of this method are quantified on a realistic simulated scenario involving twenty maneuvering ground targets observed by an airborne with a Ground Moving Target Indicator (GMTI) sensor and Unattended Ground Sensor (UGS).","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"89 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":"130167955","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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