Particle filtering for multitarget detection and tracking

Kreucher, Chris; Morelande, Mark R.; Kastella, Keith; Hero, Alfred O.

doi:10.1109/aero.2005.1559502

Cited by 33 publications

(32 citation statements)

References 28 publications

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“…A Markov chain, referred to as the maneuvering mode, switches between these motion modes. The PF described in Sections IV and V can be extended to handle multiple motion models by adding the maneouvring mode to the target state and producing samples of this discrete random variable along with the other elements of the target state [26], [27].…”

Section: Notation and Modelingmentioning

confidence: 99%

“…1 for a two-dimensional surveillance region. The likelihood conditional on the target cluster can be expanded as (26) where if and zero otherwise and (27) with the number of distinct cell indices in the distinct cell indices, and the multiplicities of the distinct cells. For the Rayliegh-distributed measurement model used here the cell measurement likelihood is given by (6) for nonthresholded measurements and (7) for thresholded measurements.…”

Section: B Development Of the Tracking Algorithmmentioning

confidence: 99%

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A Bayesian Approach to Multiple Target Detection and Tracking

Morelande

Kreucher

Kastella

2007

IEEE Trans. Signal Process.

138

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Abstract-This paper considers the problem of simultaneously detecting and tracking multiple targets. The problem can be formulated in a Bayesian framework and solved, in principle, by computation of the joint multitarget probability density (JMPD). In practice, exact computation of the JMPD is impossible, and the predominant challenge is to arrive at a computationally tractable approximation. A particle filtering scheme is developed for this purpose in which each particle is a hypothesis on the number of targets present and the states of those targets. The importance density for the particle filter is designed in such a way that the measurements can guide sampling of both the target number and the target states. Simulation results, with measurements generated from real target trajectories, demonstrate the ability of the proposed procedure to simultaneously detect and track ten targets with a reasonable sample size.

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Section: Notation and Modelingmentioning

confidence: 99%

Section: B Development Of the Tracking Algorithmmentioning

confidence: 99%

A Bayesian Approach to Multiple Target Detection and Tracking

Morelande

Kreucher

Kastella

2007

IEEE Trans. Signal Process.

138

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“…We use the associated sensor model [16], which dominates the tracking and data fusion literature. In this model, each sensor measurement consists of a number of observations that can be either valid measurements from established targets, or false alarms from clutter or emerging targets.…”

Section: Observation and Observation Lawmentioning

confidence: 99%

Design tools for complex dynamic security systems.

Byrne¹,

Rigdon²,

Rohrer³

et al. 2007

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The development of tools for complex dynamic security systems is not a straight forward engineering task but, rather, a scientific task where discovery of new scientific principles and math is necessary. For years, scientists have observed complex behavior but have had difficulty understanding it. Prominent examples include: insect colony organization, the stock market, molecular interactions, fractals, and emergent behavior. Engineering such systems will be an even greater challenge. This report explores four tools for engineered complex dynamic security systems: Partially Observable Markov Decision Process, Percolation Theory, Graph Theory, and Exergy/Entropy Theory. Additionally, enabling hardware technology for next generation security systems are described: a 100 node wireless sensor network, unmanned ground vehicle and unmanned aerial vehicle.3 Acknowledgment

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“…Concerning the observation model, an association free approach,popularly known as Track-Before-Detect (TBD), is taken Ristic et al [2004], Kreucher et al [2005]. More specifically, for the synthetic data simulation, we will specify the observation model as a simplified ground moving target indicator (GMTI) radar with position only Rayleigh-distributed measurements Kreucher et al [2005].…”

mentioning

confidence: 99%

“…More specifically, for the synthetic data simulation, we will specify the observation model as a simplified ground moving target indicator (GMTI) radar with position only Rayleigh-distributed measurements Kreucher et al [2005]. We will also use thresholded measurement that returns 1 or 0 for each pixel.…”

mentioning

confidence: 99%