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.
In order to balance the accuracy and real-time performance of the moving targettracking system, an optimized design and implementation method based on high-level synthesis (HLS) of multi-feature fusion with kernel co...
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In order to balance the accuracy and real-time performance of the moving targettracking system, an optimized design and implementation method based on high-level synthesis (HLS) of multi-feature fusion with kernel co...
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In order to balance the accuracy and real-time performance of the moving targettracking system, an optimized design and implementation method based on high-level synthesis (HLS) of multi-feature fusion with kernel correlation filtering algorithms on FPGA is designed. This design improves the KCF algorithm with LBP and HOG features, and proposes a new dimensionality reduction method for LBP, which enhances the real-time performance while maintaining effective extraction of target features. The algorithm is implemented with FPGA, and a well acceleration effect is obtained on the basis of high precision. In test, the frame rate reaches 35 frames per second. Finally, it is verified through simulation that this feature extraction method can be used to process various image data such as infrared detection and SAR radar imaging, and has a wide range of applications.
This paper studies security issue of targettracking in radar network system. We first derive the analytical result for the attack's effect on the performance of targettracking when one radar station is under dec...
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ISBN:
(纸本)9783319218373;9783319218366
This paper studies security issue of targettracking in radar network system. We first derive the analytical result for the attack's effect on the performance of targettracking when one radar station is under deception attack. Based on this, by extending to radar network system, we find the relationship between the performance of targettracking and attack parameter. Simulation is presented to demonstrate the effectiveness of our results.
Presented here is a fusion system based on simple, low-cost computer vision and radiological sensors for tracking of multiple objects and identifying potential radiological materials being transported or shipped. The ...
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ISBN:
(纸本)9781479999187
Presented here is a fusion system based on simple, low-cost computer vision and radiological sensors for tracking of multiple objects and identifying potential radiological materials being transported or shipped. The main focus of this work is the development of calibration algorithms for characterizing the fused sensor system as a single entity. There is an apparent need for correcting for a scene deviation from the basic inverse distance-squared law governing the detection rates even when evaluating system calibration algorithms. In particular, the computer vision system enables a map of distance-dependence of the sources being tracked, to which the time-dependent radiological data can be incorporated by means of datafusion of the two sensors' output data.
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.
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.
Common data preprocessing routines often introduce considerable flaws in laser-based tracking of extended objects. As an alternative, extended targettracking methods, such as the Gamma-Gaussian-Inverse Wishart (GGIW)...
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ISBN:
(纸本)9781479916344
Common data preprocessing routines often introduce considerable flaws in laser-based tracking of extended objects. As an alternative, extended targettracking methods, such as the Gamma-Gaussian-Inverse Wishart (GGIW) probability hypothesis density (PHD) filter, work directly on raw data. In this paper, the GGIW-PHD filter is applied to real world traffic scenarios. To cope with the large amount of data, a mixture clustering approach which reduces the combinatorial complexity and computation time is proposed. The effective segmentation of raw measurements with respect to spatial distribution and motion is demonstrated and evaluated on two different applications: pedestrian tracking from a vehicle and intersection surveillance.
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