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检索条件"任意字段=IET Conference on Data Fusion and Target Tracking 2014: Algorithms and Applications"

共 76 条记录，以下是41-50 订阅

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Gaussian mixture PHD filter for multi-target tracking using passive Doppler-only measurements

Gaussian mixture PHD filter for multi-target tracking using ...

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9th iet data fusion & target tracking conference (DF&TT 2012): algorithms & applications

作者： Mehmet B. Guldogan Umut Orguner Fredrik Gustafsson Division of Automatic Control Department of Electrical Engineering Linköping University Sweden

ISBN: (纸本)9781622761951

In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using a passive sensor network. Non-cooperative transmissions from illuminators of opportunity like GSM base stations, FM radio transmitters or digital broadcasters are exploited by non-directional separately located Doppler measuring sensors. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target scenario. Simulation results show that the GM-PHD filter success fully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario.

关键词： Random sets Multi-target tracking Gaussian mixture probability hypothesis density filter Doppler measurements target tracking illuminators Doppler effect Filters Gauss tracking passive seasor BROADCASTERS FM radio Sensor networks

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Bayesian tracking and multi-core beamforming for estimation of correlated brain sources

Bayesian tracking and multi-core beamforming for estimation ...

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iet conference on data fusion & target tracking 2014: algorithms and applications (DF&TT 2014)

作者： Petia Georgieva Filipe Silva Nidhal Bouaynaya Lyudmila Mihaylova Institute of Electronics Engineering and Telematics of Aveiro (IEETA) Department of Electronics Telecommunications and Informatics(DETI) University of Aveiro 3810-193 Aveiro Portugal College of Engineering Rowan University USA Department of Automatic Control and Systems Engineering University of Sheffield Mappin Street Sheffield S1 3JD UK

ISBN: (纸本)9781632666642

The main contribution of this paper is the general framework, termed multi-core Beamformer Particle Filter (multi-core BPF), for solving the ill-posed EEG inverse problem. The method combines a particle filter (statistical approach) for reconstruction of the brain source spatial locations and a multi-core Beamformer (deterministic approach) for estimation of the corresponding dipole waveforms in a recursive way The intuition behind is to benefit from the advantages of both deterministic and statistical inverse problem solvers in order to improve the estimation accuracy without increasing the complexity and the computational cost. Our simulations show that the proposed algorithm can reconstruct reliably the few most active (the dominant) brain sources that have generated the registered EEG measurements. The main advantage of the method is that in contrast to conventional (single-core) Beamforming spatial filters, the proposed Multi-core Beamformer explicitly takes into consideration the potential temporal correlation between the dipoles.

关键词： Bayesian tracking Particle filtering Multi-core beamforming Brain sources Particle filters Beamforming dipoles Cerebrum Inverse method problem solver

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Noise power estimation under generalized detector employment in automotive detection and tracking systems

Noise power estimation under generalized detector employment...

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9th iet data fusion & target tracking conference (DF&TT 2012): algorithms & applications

作者： Modar Safir Shbat Vyacheslav Tuzlukov College of IT Engineering Kyungpook National University Daegu South Korea

ISBN: (纸本)9781622761951

The noise power estimation process is a vital factor to adaptively define a threshold of target return signal in radar sensor systems and controller area networks (CAN) that are employed to design safety driving applications, collision avoidance systems, and target vehicle tracking systems. This research derives the required detection threshold under implementation of the generalized detector (GD) in frequency modulation continuous wave (FMCW) radar sensor systems for safety driving and tracking applications, for example, under closing vehicle detection. In this paper we propose an appropriate adaptive noise power estimation technique to define the GD threshold based on locally observed noise samples. The improvement in the detection performance reflects an effectiveness of the proposed solution.

关键词： Generalized detector (GD) Adaptive detection threshold Noise power estimation Radar sensor Constant false alarm rate (CFAR) Defensive driving EMPLOYMENT Vehicle detection detector Noise Frequency modulation Sensor systems noise power detection limit Gadolinium

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Dynamic accuracy assessment of data-fusion techniques for wearable, inertial and magnetic based human motion capture 13

Dynamic accuracy assessment of data-fusion techniques for we...

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13th IEEE Sensors conference

作者： Ricci, L. Formica, D. Univ Campus Biomed Roma Lab Biomed Robot & Biomicrosyst Via Alvaro del Fortino 21 I-00128 Rome Italy

ISBN: (纸本)9781479901616

Wearable motion capture systems, based on magneto and inertial sensors are considered a promising technology in the field of human motion capture. Compared to golden standard systems, e.g. based on stereo-photogrammetry, they feature a better portability and usability and are suitable for 'out of the lab' data collection. While different applications of human motion capture systems based on magneto and inertial sensors have been explored in literature, few studies have provided insights about the accuracy that can be achieved using data-fusion techniques and even less did that in a rigorous and controlled way. This work focuses on the dynamic absolute accuracy assessment of data-fusion algorithms for orientation tracking under controlled conditions. A 7 DOF robotic arm is used in order to apply controlled angular displacements to the motion capture system, in a range of frequencies that is of interest to human motion capture applications. Preliminary results on absolute accuracy that can be achieved with popular and open-source available fusion algorithms, running on a commercial hardware, are reported.

关键词： Magnetos

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Bayes optimal knowledge exploitation for target tracking with hard constraints

Bayes optimal knowledge exploitation for target tracking wit...

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9th iet data fusion & target tracking conference (DF&TT 2012): algorithms & applications

作者： F. Papi M. Podt Y. Boers G. Battistello M. Ulmke Thales Nederland BV Sensors-TBU Radar Engineering Hengelo Netherlands Sensor Data and Information Fusion Department Fraunhofer FKIE Wachtberg Germany

ISBN: (纸本)9781622761951

Nonlinear target tracking is a well known problem and its Bayes optimal solution, based on particle filtering techniques, is nowadays applied in high performance surveillance systems. Oftentimes, additional information about the environment and the target is available, and can be formalized in terms of constraints on target dynamics. Hence, a Constrained version of the Bayesian Filtering problem has to be solved to achieve optimal tracking performance. In this paper we consider the Constrained Filtering problem for the case of perfectly known hard constraints. We clarify that in such a case the Particle Filter (PF) is still Bayes optimal if we can correctly model the constraints. We then show that from a Bayesian viewpoint, exploitation of the available knowledge in the prediction or in the update step are equivalent. Finally, we consider simple techniques to exploit constraints in the prediction and update steps of a PF, and use the Kullback-Leibler divergence to illustrate their equivalence through simulations.

关键词： Constrained Bayesian Filtering Sequential Monte Carlo Pseudo-Measurements Rejection-Sampling Kullback-Leibler Divergence targets Constraint knowledge target tracking exploitation Particle filters Bayes equivalence filter technique tracking extra message

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Video Observations for Cloud Activity-Based Intelligence (VOCABI)

Video Observations for Cloud Activity-Based Intelligence (VO...

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IEEE National Aerospace and Electronics conference (NAECON)

作者： Blasch, Erik DiBona, Phillip Czajkowski, Michael Barry, Kevin Rimey, Ray Freeman, Jeff Newman, Kevin Aved, Alex Hinman, Mike Air Force Res Lab Rome NY 13441 USA LM ATL INL Cherry Hill NJ 08002 USA

ISBN: (纸本)9781479946891

The availability of video imagery through reduction in sensor size, cost, and power has enabled an explosion of collection opportunities. With the increased amount of imagery there is a need to understand the usefulness of video for applications such as Activity-Based Intelligence (ABI), situation understanding, and event-based processing. In this paper, we explore some of the emerging developments in video observations with a focus on cloud technology. Cloud technology supports integration of multiple algorithms, storage of large data sets, indexing over multimedia, and workflow opportunities between humans and machines. We highlight multiple tools such as GeoFlix (TM), Application Knowledge Interface To algorithms (AKITA (TM)), and Intelligence Preparation of the Operational Environment (IPOE) using the Ozone Widget Framework (OWF) for permissive surveillance, data to decisions, and information fusion. These tools enable data analytics, algorithm comparison, and user-defined visualizations. An example is presented for target localization and tracking through planned video observations.

关键词： Video Analytics Cloud Computing automation and autonomy information fusion visualization

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PERFORMANCE OF BEARING-ONLY ESM-RADAR TRACK ASSOCIATION

PERFORMANCE OF BEARING-ONLY ESM-RADAR TRACK ASSOCIATION

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iet data fusion & target tracking conference

作者： C. R. Offer Thales UK Manor Royal

ISBN: (纸本)9781622761951

The bearing-only association of tracks from dissimilar sensors is considered, in particular the association of radar and electronic support measures (ESM) tracks. An approach to characterising performance is suggested, namely the curve representing the relationship between the median time to reach a correct association decision and the mean number of incorrect associations, as the association threshold is varied. Results are presented for artificially generated tracks, showing the variation of performance with track density and accuracy. Results are also given for the association of real radar tracks of ships with simulated ESM tracks. In particular, it is concluded that the ability to attach identity information from ESM to long-range radar tracks will be significantly improved if the ESM bearing errors are reduced, for example from a magnitude of order 5° to 1°.

关键词： Multisensor fusion ESM radar association

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A Bayesian look at the optimal track labelling problem

A Bayesian look at the optimal track labelling problem

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9th iet data fusion & target tracking conference (DF&TT 2012): algorithms & applications

作者： Edson H. Aoki Yvo Boers Lennart Svensson Pranab Mandal Arunabha Bagchi University of Twente Netherlands Thales Nederland B.V. Chalmers University of Technology Sweden

ISBN: (纸本)9781622761951

In multi-target tracking (MTT), the problem of assigning labels to tracks (track labelling) is vastly covered in literature, but its exact mathematical formulation, in terms of Bayesian statistics, has not been yet looked at in detail. Doing so, however, may help us to understand how Bayes-optimal track labelling should be performed or numerically approximated. Moreover, it can help us to better understand and tackle some practical difficulties associated with the MTT problem, in particular the so-called “mixed labelling” phenomenon that has been observed in MTT algorithms. In this paper, we rigorously formulate the optimal track labelling problem using Finite Set Statistics (FISST), and look in detail at the mixed labeling phenomenon. As practical contributions of the paper, we derive a new track extraction formulation with some nice properties and a statistic associated with track labelling with clear physical meaning. Additionally, we show how to calculate this statistic for two well-known MTT algorithms.

关键词： target tracking Finite Set Statistics Track labelling Particle filter Track Packaging Bayesian Statistics

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Person tracking via audio and video fusion

Person tracking via audio and video fusion

引用

9th iet data fusion & target tracking conference (DF&TT 2012): algorithms & applications

作者： Eleonora D'Arca Neil M. Robertson James Hopgood The University of Edinburgh Edinburgh Edinburgh GB Edinburgh Research Partnership Heriot-Watt University Edinburgh UK

ISBN: (纸本)9781622761951

In this paper we present a joint audio-video (AV) tracker which can track the active source between two freely moving persons speaking in turn to simulate a meeting scenario, but less constrained. Our tracker differs from existing work in that it requires only a small number of sensors, works when speaker is not close to the sensors and relies on simple, yet efficient, inference techniques in AV processing. The system uses audio and video measures of the target position on the ground plane to strengthen the single modality predictions that would be weak if taken on their own as occlusions, clutter, reverberations and speech pauses happen in the test environment. In particular, the inter-microphone signal delays and the target image locations are input to single modality Bayesian filters, whose proposed likelihoods are multiplied in a Kalman Filter to give the joint AV final estimation. Despite the low complexity of the system, results show that the multi-modal tracker does not fail, tolerating video occlusion and intermittent speech (within 50 cm of accuracy) in the context of a non-meeting scenario. The system evaluation is done both on single modality than multi-modality tracking, and the performance improvement given by the AV fusion is discussed and quantified i.e 24 % improvement on the audio tracker accuracy.

关键词： Multimodal fusion Audio tracking Video tracking Kalman filtering AV occlusion Audio Videotapes system evaluation Inference mechanisms fusion reactions fusion fusion (melting) fusion, biological performance boost Occlusion Sensors Modality tracking TEST ENVIRONMENT Loudspeakers

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Box-particle intensity filter

Box-particle intensity filter

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9th iet data fusion & target tracking conference (DF&TT 2012): algorithms & applications

作者： M. Schikora A. Gning L. Mihaylova D. Cremers W. Koch R. Streit Department of Computer Science University College London UK Department Sensor Data and Information Fusion Fraunhofer FKIE Wachtberg Germany School of Computing and Communications University of Lancaster UK Department of Computer Science Technical University of Munich Germany Metron Systems Inc. Reston VA USA

ISBN: (纸本)9781622761951

This paper develops a novel approach for multi-target tracking, called box-particle intensity filter (box-iFilter). The approach is able to cope with unknown clutter, false alarms and estimates the unknown number of targets. Further more, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. The box-iFilter reduces the number of particles significantly, which improves the runtime considerably. The low particle number enables this approach to be used for distributed computing. A box-particle is a random sample that occupies a small and controllable rectangular region of non-zero volume. Manipulation of boxes utilizes the methods from the field of interval analysis. Our studies suggest that the box-iFilter reaches an accuracy similar to a sequential Monte Carlo (SMC) iFilter but with much less computational costs.

关键词： Multi-target tracking Box Particle Filters Poisson Point Processes Intensity Filter Interval Measurements Filters Boxes data association Unknown False alarms

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