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iet conference on data fusion and target tracking 2014: algorithms and applications

ISBN: (纸本)9781849198639

The proceedings contain 12 papers. The topics discussed include: Bayesian tracking and multi-core beamforming for estimation of correlated brain sources;particle filtering for network-based positioning terrestrial radio networks;tracking simulated UAV swarms using particle filters;rectangular extended object tracking with box particle filter using dynamic constraints;probabilistic step and turn detection in indoor localization;fusing kinect sensor and inertial sensors with multi-rate kalman filter;piecewise constant sequential importance sampling for fast particle filtering;hybrid gauss-hermite filter;regional variance in target number: analysis and application for multi-Bernoulli point processes;combined evidential data association;and PHD filtering in presence of highly structured sea clutter process and tracks with extent.

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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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Combined evidential data association

Combined evidential data association

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iet conference on data fusion and target tracking 2014: algorithms and applications

作者： Dallil, A. Ecole Militaire Polytechnique Algeria

ISBN: (纸本)9781849198639

In this paper, we present a new method for data association in multi-target tracking situation in the framework of evidence theory. The representation and the fusion of the information in our method are based on the use of belief function in the sense of Dempster-Shafer theory of evidence. The proposal generates two belief matrices using two different specialized basic belief mass assignments. While the decision making process is based on the extension of the frame of hypotheses. The method has been tested for a nearly constant velocity target in two ambiguous cases using Monte Carlo simulations.

关键词： target tracking

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Linear minimum-mean-square error estimation of Markovian jump linear systems with Stochastic coefficient matrices

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iet CONTROL THEORY AND applications 2014年第12期8卷 1112-1126页

作者： Yang, Yanbo Liang, Yan Pan, Quan Qin, Yuemei Yang, Feng Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Minist Educ Key Lab Informat Fus Technol Beijing Peoples R China

This study presents the state estimation problem of discrete-time Markovian jump linear systems with stochastic coefficient matrices which is motivated by the idea of establishing the general filter framework of the joint state estimation and data association in clutters for tracking the manoeuvering target. According to the orthogonality principle, the linear minimum-mean-square error estimator for this system (abbreviated as LMSCE estimator) is derived recursively and sufficient conditions are given for the stability of the LMSCE estimator. The simulation about tracking the manoeuvering target in clutters shows that the LMSCE estimator obtains much more accurate estimate than the well-known interacting multiple model probabilistic data association filter.

关键词： Signal processing theory multiple model probabilistic data association filter least mean squares methods clutter manoeuvering target tracking stability discrete time Markovian jump linear systems sensor fusion state estimation orthogonality principle matrix algebra LMSCE estimator linear systems Linear algebra (numerical analysis) target tracking recursive estimation Interpolation and function approximation (numerical analysis) sufficient condition Filtering methods in signal processing recursive process state estimation problem linear minimum mean square error estimation Markov processes stochastic coefficient matrix Simulation, modelling and identification discrete time filters

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tracking simulated UAV swarms using particle filters

Tracking simulated UAV swarms using particle filters

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iet conference on data fusion and target tracking 2014: algorithms and applications

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

ISBN: (纸本)9781849198639

This paper provides an initial examination of the use of particle filters in tracking swarms of small targets such as Unmanned Aerial Vehicles using a radar. From the standpoint of conventional tracking solutions, such swarms present a severe challenge - due not only to the quasi-erratic motion of the UAVs relative to the swarm trajectory as a whole, but also from the effects of the small target size upon radar resolution and detection probability. It is shown here that a particle filter is capable of providing a stable track on the swarm centroid, although not the individual constituent UAVs.

关键词： Unmanned aerial vehicles (UAV)

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Multi-feature based automatic recognition of ship targets in ISAR

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iet RADAR SONAR AND NAVIGATION 2009年第4期3卷 406-423页

作者： Pastina, D. Spina, C. Univ Roma La Sapienza Dept INFOCOM I-00184 Rome Italy

This paper deals with the problem of non-cooperative target recognition. Specifically, the aim is the automatic recognition of ship targets from inverse synthetic aperture radar (ISAR) images. For this purpose a new two-step multi-feature based technique is proposed;this technique uses a number of features extracted from the ship radar image and matches these features with those extracted from the images obtained by properly projecting the target models of the classification library. Both cases of a priori known or unknown ship aspect angles are considered: the knowledge of the ship aspect (as available from tracking data) allows the selection of the candidate models on the basis of the matching between the ship and the model length, thus resulting in a performance improvement. Moreover, both single-and multi-frame-based processing techniques are proposed in order to assess the performance improvement achievable when an increasing number of ISAR images are involved in the decision;the fusion strategy adopted for the exploitation of the information from the multiple images is also described. The performance of the overall proposed technique is deeply investigated against simulated data. Results of its application to a set of live ISAR images of a ship target are also provided showing the effectiveness of the proposed approach.

关键词： ISAR image matching ships multiframe based processing technique multifeature based automatic recognition ship radar image Optical, image and video signal processing inverse synthetic aperture radar images features extraction synthetic aperture radar radar imaging feature extraction Radar equipment, systems and applications radar target recognition fusion strategy ship target

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Rectangular extended object tracking with box particle filter using dynamic constraints

Rectangular extended object tracking with box particle filte...

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iet conference on data fusion and target tracking 2014: algorithms and applications

作者： Petrov, Nikolay Mihaylova, Lyudmila Gning, Amadou Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom Department of Computer Science University College London United Kingdom

ISBN: (纸本)9781849198639

Extended objects generate multiple measurements and are characterised with their size or volume. They require methods able to deal with the data association problem and at the same time to estimate both their kinematic states and shape parameters. This paper presents a solution to the extended object tracking for rectangular extended objects, with the Box Particle filter (Box PF) approach. The Box PF is implemented based on dynamically calculated constraints. Promising results are demonstrated.

关键词： Bandpass filters

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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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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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