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 rad...
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.
The proceedings contain 16 papers. The topics discussed include: box-particle intensity filter;Gaussian mixture PHD filter for multi-targettracking using passive Doppler-only measurements;video analytics: past, prese...
ISBN:
(纸本)9781849196246
The proceedings contain 16 papers. The topics discussed include: box-particle intensity filter;Gaussian mixture PHD filter for multi-targettracking using passive Doppler-only measurements;video analytics: past, present, and future;decentralised road-map assisted ground targettracking 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 targettracking 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.
In this paper, we present a new method for data association in multi-targettracking situation in the framework of evidence theory. The representation and the fusion of the information in our method are based on the u...
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ISBN:
(纸本)9781849198639
In this paper, we present a new method for data association in multi-targettracking 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.
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, suc...
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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.
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 t...
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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 trackingdata) 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.
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 kinemati...
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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.
This paper presents a ground moving targettracking 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 ...
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ISBN:
(纸本)9781849196246
This paper presents a ground moving targettracking 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.
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 appli...
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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 trackingapplications, 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.
Nonlinear targettracking 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 informatio...
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ISBN:
(纸本)9781849196246
Nonlinear targettracking 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.
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 transmissio...
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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.
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