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Probability hypothesis density filtering with multipath …

The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution.

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Hamidreza Amindavar - Amirkabir University of …

Here, it is used together with a recently proposed Gaussian mixture probability hypothesis density (GM-PHD) filter for extended target tracking, which enables estimation of not only position, orientation, and size of the extended targets, but also estimation of extended target type (i.e.

Probability Hypothesis Density Filter Algorithm for Track Before Detect Applications: ..

This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets and estimates the unknown number of targets. Furthermore, it is capable to deal with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. The box-PHD filter reduces the number of particles significantly, which improves the runtime considerably. The small particle number makes this approach attractive 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 methods from the field of interval analysis. The theoretical derivation of the box-PHD filter is presented followed by a comparative analysis with a standard sequential Monte Carlo (SMC) version of the PHD filter. To measure the performance objectively three measures are used: inclusion, volume and the optimum subpattern assignment metric. Our studies suggest that the box-PHD filter reaches similar accuracy results, like a SMCPHD filter but with much considerably less computational costs. Furthermore, we can show that in the presence of strongly biased measurement the box-PHD filter even outperforms the classical SMC-PHD filter.

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A measurement-driven adaptive probability hypothesis density filter for multitarget tracking 1695 ters need to adapt to the changes in cardinality.

end for A measurement-driven adaptive probability hypothesis density filter for multitarget tracking 1693 the particles are generated by real targets and will correspond-
ingly make significant contributions to the filter.

A novel gat- ing strategy that is based on the measurement likelihood of the target state space is proposed to
improve the overall effectiveness of the probability hypothesis density (PHD) filter.

Existential Risks: Analyzing Human Extinction Scenarios

The main contribution of this paper is the implementation of a Probability Hypothesis Density ( phd) filter for tracking of multiple extended targets.

N2 - We consider the particle probability hypothesis density filter (PPHDF) for tracking multiple targets in urban terrain. This is a filtering technique based on random finite sets, implemented using the particle filter. Unlike data association methods, the PPHDF can be modified to estimate both the number of targets and their corresponding tracking parameters. We propose a modified PPHDF algorithm that employs multipath-to-measurement association (PPHDF-MMA) to automatically and adaptively estimate the available types of measurements. By using the best matched measurement at each time step, the new algorithm results in improved radar coverage and scene visibility. Numerical simulations demonstrate the effectiveness of the PPHDF-MMA in improving the tracking performance of multiple targets and targets in clutter.

AB - We consider the particle probability hypothesis density filter (PPHDF) for tracking multiple targets in urban terrain. This is a filtering technique based on random finite sets, implemented using the particle filter. Unlike data association methods, the PPHDF can be modified to estimate both the number of targets and their corresponding tracking parameters. We propose a modified PPHDF algorithm that employs multipath-to-measurement association (PPHDF-MMA) to automatically and adaptively estimate the available types of measurements. By using the best matched measurement at each time step, the new algorithm results in improved radar coverage and scene visibility. Numerical simulations demonstrate the effectiveness of the PPHDF-MMA in improving the tracking performance of multiple targets and targets in clutter.

A measurement-driven adaptive probability hypothesis density filter for multitarget tracking 1697 References 1.
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  • Conventional Weapons - Space War - Atomic Rockets

    Penalized Gaussian mixture probability hypothesis density filter for multiple target tracking

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    A shrinkage probability hypothesis density filter for multitarget tracking ..

  • Type or paste a DOI name into the text box

    We consider the particle probability hypothesis density filter (PPHDF) for tracking multiple targets in urban terrain

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The Fermi Paradox - Wait But Why

Track-Before-Detect (TBD) algorithms are far more efficient over standard DetectBefore- Track (DBT) target tracking approach for tracking targets in low Signal-toNoise- Ratio (SNR) environment . With low SNR scenario the target amplitude may never be strong enough to exceed threshold value and under classical setting such cases will not lead to detection. This might be the case in spatially diversified multiple sensors network like Multiple-Input-Multiple-Output (MIMO) radars. Through letting the tracking directly on the unthresholded data, TBD techniques exploit all the information in the received measurement signal to yield detection and tracking simultaneously. With TBD framework an efficient multitarget, non-linear filtering algorithm is an issue to extract information from target dynamics. In t his thesis Probability-Hypothesis-Density (PHD) filter implementation of a recursive TBD algorithm is proposed. The PHD filter, propagating only the first-order statistical moment of the full target posterior, is a computationally efficient solution to multitarget tracking problems with varying number of targets. Furthermore a PHD filter based tracking algorithm avoids the preassumption of the maximum number of targets performing the state estimation together with number of targets.

Engineering Courses - Concordia University

Abstract—This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has already been derived by Mahler and a Gaussian mixture implementation has been proposed recently. This work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers to achieve better estimation performance. A Gaussian mixture implementation is described. The early results using real data from a laser sensor confirm that the sensitivity of the number of targets in the extended target PHD filter can be avoided with the added flexibility of the extended target CPHD filter.

Senior Physics - Extended Experimental Investigations

We consider the particle probability hypothesis density filter (PPHDF) for tracking multiple targets in urban terrain. This is a filtering technique based on random finite sets, implemented using the particle filter. Unlike data association methods, the PPHDF can be modified to estimate both the number of targets and their corresponding tracking parameters. We propose a modified PPHDF algorithm that employs multipath-to-measurement association (PPHDF-MMA) to automatically and adaptively estimate the available types of measurements. By using the best matched measurement at each time step, the new algorithm results in improved radar coverage and scene visibility. Numerical simulations demonstrate the effectiveness of the PPHDF-MMA in improving the tracking performance of multiple targets and targets in clutter.

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