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检索条件"任意字段=9th IET Data Fusion and Target Tracking Conference: Algorithms and Applications, DF and TT 2012"

共 21 条记录，以下是1-10 订阅

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9th iet data fusion and target tracking conference, df and tt 2012: algorithms and applications

9th IET Data Fusion and Target Tracking Conference, DF and T...

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9th iet data fusion and target tracking conference: algorithms and applications, df and tt 2012

ISBN: (纸本)9781849196246

the proceedings contain 16 papers. the topics discussed include: box-particle intensity filter;Gaussian mixture PHD filter for multi-target tracking using passive Doppler-only measurements;video analytics: past, present, and future;decentralised road-map assisted ground target tracking using a team of UAVs;a Bayesian look at the optimal track labelling problem;performance of bearing-only ESM-radar track association;Bayes optimal knowledge exploitation for target tracking with hard constraints;person tracking via audio and video fusion;optimized instrumental density for particle filter in track-before-detect;track degradation as a consequence of distributed sensor fusion;particle learning methods for state and parameter estimation;unsupervised learning of maritime traffic patterns for anomaly detection;and tracking and managing real world electric vehicle power usage and supply.

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Decentralised road-map assisted ground target tracking using a team of UAVS

Decentralised road-map assisted ground target tracking using...

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9th iet data fusion and target tracking conference: algorithms and applications, df and tt 2012

作者： Oh, Hyondong Kim, Seungkeun Tsourdos, Antonios White, Brian Department of Informatics and Systems Engineering Cranfield University Defence Academy of the UK Swindon United Kingdom Department of Aerospace Engineering Chungnam National University Daejeon Korea Republic of

ISBN: (纸本)9781849196246

this paper presents a ground moving target tracking filter and guidance using a team of UAVs. To improve the estimation accuracy, approximated road-map information using constant curvature segments is utilised with a constrained filtering. Furthermore, the decentralised extended information filter and the Kalman consensus algorithm are applied to a standoff orbit tracking problem along with coordinated vector field guidance, and their performances are analysed depending on the communication noises and network structures using a realistic car trajectory data.

关键词： target tracking

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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 and target tracking conference: algorithms and applications, df and tt 2012

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

ISBN: (纸本)9781849196246

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： Safety factor

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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 and target tracking conference: algorithms and applications, df and tt 2012

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

ISBN: (纸本)9781849196246

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 successfully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario.

关键词： Sensor networks

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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 and target tracking conference: algorithms and applications, df and tt 2012

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

ISBN: (纸本)9781849196246

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： target tracking

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Performance of bearing-only ESM-radar track association

Performance of bearing-only ESM-radar track association

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9th iet data fusion and target tracking conference: algorithms and applications, df and tt 2012

作者： Offer, C.R. Thales UK Manor Royal Crawley West Sussex RH10 9HA United Kingdom

ISBN: (纸本)9781849196246

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： Radar tracking

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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 and target tracking conference: algorithms and applications, df and tt 2012

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

ISBN: (纸本)9781849196246

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

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Track degradation as a consequence of distributed sensor fusion

Track degradation as a consequence of distributed sensor fus...

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9th iet data fusion and target tracking conference: algorithms and applications, df and tt 2012

作者： Easthope, P.F. L-3 Communications ASA Ltd. Rusint House Harvest Crescent Fleet Hampshire GU51 2QS United Kingdom

ISBN: (纸本)9781849196246

It is often taken for granted that measurement-level data fusion must necessarily give rise to an improvement in the track picture, particularly in situations where the sensors are spatially distributed. In many cases, this assumption is justified - an obvious example is where each sensor individually produces too sparse a set of plots to support a track, whereas multiple sensors can provide a sufficient density of measurements. Another example is where complementary viewing angles can permit more precise target localisation. there are, however, situations in which the provision of information from multiple sensors can actually be detrimental to the tracking of closely-spaced objects. this behaviour has been observed using very high-fidelity simulations of a set of high-range-resolution radars observing closely-spaced ballistic targets. It is shown that the complementary viewing angles provided by distributed sensors can actually increase the likelihood of miscorrelation, in situations in which the closely-spaced objects (CSOs) are resolvable in range but not in angle. An attempt to exploit the individual sensor resolution capabilities by using track-associated plot processing was found to be fragile. An MHT (Multi-Hypothesis tracking) approach, however, is shown to be a robust solution.

关键词： Sensor data fusion

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Person tracking via audio and video fusion

Person tracking via audio and video fusion

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9th iet data fusion and target tracking conference: algorithms and applications, df and tt 2012

作者： D'Arca, Eleonora Robertson, Neil M. Hopgood, James Edinburgh Research Partnership Heriot-Watt University and University of Edinburgh Edinburgh United Kingdom

ISBN: (纸本)9781849196246

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 intermicrophone 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： Kalman filters

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Box-particle intensity filter

Box-particle intensity filter

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9th iet data fusion and target tracking conference: algorithms and applications, df and tt 2012

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

ISBN: (纸本)9781849196246

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. Furthermore, 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： Distributed computer systems

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