Some assignment problems arising from multiple target tracking

Poore, Aubrey B.; Gadaleta, Sabino

doi:10.1016/j.mcm.2005.05.026

Cited by 72 publications

(67 citation statements)

References 11 publications

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“…As mentioned in the introduction, a few meta-heuristic methods have been investigated within the framework of MTT (Poore and Gadaleta, 2006). In this section we define how an 'individual' is represented, and how such an individual is evaluated through a so-called 'fitness function'.…”

Section: Population Based Meta-heuristic (Pbmh) Algorithms In Multiplmentioning

confidence: 99%

“…The number of scans S that are used in the problem correspond to its dimension. For a dimension of S ≥ 3 the number of possible permutations greatly increases and the problem becomes NP-hard (Poore and Gadaleta, 2006). For instance, in the case where S = 2 with two observations per scan, there will be a total of seven possible permutations.…”

Section: Introductionmentioning

confidence: 99%

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Associating optical measurements of MEO and GEO objects using Population-Based Meta-Heuristic methods

Zittersteijn

Vananti

Schildknecht

et al. 2016

Advances in Space Research

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Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). The MTT problem quickly becomes an NP-hard combinatorial optimization problem. This means that the effort required to solve the MTT problem increases exponentially with the number of tracked objects. In an attempt to find an approximate solution of sufficient quality, several Population-Based Meta-Heuristic (PBMH) algorithms are implemented and tested on simulated optical measurements. These first results show that one of the tested algorithms, namely the Elitist Genetic Algorithm (EGA), consistently displays the desired behavior of finding good approximate solutions before reaching the optimum. The results further suggest that the algorithm possesses a polynomial time complexity, as the computation times are consistent with a polynomial model. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the association and orbit determination problems simultaneously, and is able to efficiently process large data sets with minimal manual intervention.

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Section: Population Based Meta-heuristic (Pbmh) Algorithms In Multiplmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Associating optical measurements of MEO and GEO objects using Population-Based Meta-Heuristic methods

Zittersteijn

Vananti

Schildknecht

et al. 2016

Advances in Space Research

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show abstract

“…In the computer science literature, this is often referred to as the "knapsack problem" in which n balls have to be placed into m knapsacks. A review of the problem, together with common algorithms to solve it is given by Poore and Gadaleta [32].…”

Section: Point Matching Algorithmmentioning

confidence: 99%

Stochastic reconstruction of filament paths in fibre bundles based on two-dimensional input data

Gommer

Wedgwood

Brown

2015

Composites Part A: Applied Science and Manufacturing

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Abstract:Conventional optical microscopy is an inexpensive technique to analyse fibre bundle micro-structures. Compared to micro-computed tomography, the resolution is higher and larger samples sizes can be analysed. This provides, for example, detailed information of filament spacing which will affect subsequent properties. This paper proposes a probabilistic method to reconstruct paths of individual filaments from a series of two-dimensional micrographs. This allows three-dimensional filament arrangements to be reconstructed and the effects of local arrangements on properties to be quantified. For example, for the small sample analysed in this work, a 15% difference in strength values was found.

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“…Association of a detection to an object; 3. Estimation of the object's position (and speed), Bar-Shalom and Li (1995); Roth et al (2008); Blackman and Popoli (1999); Songhwai et al (2004); Karlsson and Gustafsson (2001); Songhwai et al (2005); Poore and Gadaleta (2006); Ward et al (2003). which can detect the objects.…”

Section: Introductionmentioning

confidence: 99%

A modular architecture for object tracking and migration in self-organizing, distributed systems

Sijs

Meer

Kruithof

et al. 2009

IFAC Proceedings Volumes

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Some assignment problems arising from multiple target tracking

Cited by 72 publications

References 11 publications

Associating optical measurements of MEO and GEO objects using Population-Based Meta-Heuristic methods

Associating optical measurements of MEO and GEO objects using Population-Based Meta-Heuristic methods

Stochastic reconstruction of filament paths in fibre bundles based on two-dimensional input data

A modular architecture for object tracking and migration in self-organizing, distributed systems

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