Results 1 to 10 of about 150,627 (302)
Relationship Between Finite Set Statistics and the Multiple Hypothesis Tracker [PDF]
IEEE Transactions on Aerospace and Electronic Systems, 2018 The multiple hypothesis tracker (MHT) and finite set statistics (FISST) are two approaches to multitarget tracking, which both have been heralded as optimal. In this paper, we show that the multitarget Bayes filter with basis in FISST can be expressed in terms the MHT formalism, consisting of association hypotheses with corresponding probabilities and ...
Edmund Brekke, Mandar A. Chitre
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The multiple hypothesis tracker derived from finite set statistics [PDF]
2017 20th International Conference on Information Fusion (Fusion), 2017 The multiple hypothesis tracker (MHT) has historically been considered a gold standard for multi-target tracking. In this paper we show that the key formula for hypothesis probabilities in Reid's MHT can be derived from the modern theory of finite set statistics (FISST) insofar as appropriate assumptions (Poisson models for clutter and undetected ...
Brekke, Edmund Førland, Chitre, Mandar
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Bernoulli Filters for Multiple Correlated Sensors
IEEE Access, 2021 The Bernoulli filter is a general, Bayes-optimal solution for tracking a single disappearing and reappearing target, using a single sensor whose observations are corrupted by missed detections and a general, known clutter process.
Ronald Mahler
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Bayes-Optimal Set-Valued Tracking of Single Point Targets
IEEE Access, 2021 Conventional single-point-target tracking algorithms are recursive point estimators with point-measurement input data. In less well-known approaches, the tracking algorithm is a recursive set estimator with point-measurement or set-measurement input data.
Ronald Paxton-Sheets Mahler
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Bayes‐optimal tracking of two statistically correlated targets in general clutter
IET Signal Processing, 2022 The Bernoulli filter is a very general, computationally feasible Bayes‐optimal approach for tracking a single disappearing and reappearing target, using a single sensor whose observations are corrupted by missed detections and a known, general point ...
Ronald Mahler
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A Multisource Multi-Bernoulli Filter for Multistatic Radar
IEEE Access, 2022 Compared with conventional monostatic or bistatic radar, multistatic radar has wider coverage, better performance of localization and higher tracking accuracy.
Xueqin Zhou, Hong Ma, Jiang Jin, Hang Xu
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Bipolar Neutrosophic Frank Aggregation Operator and its application in Multi Criteria Decision Making Problem [PDF]
Neutrosophic Sets and Systems, 2022 Aggregation operators can be used to combine and synthesise a finite number of numerical values into a single numerical value. Many areas, including decision-making, expert systems, risk analysis, and image processing, rely heavily on aggregating ...
M. Lathamaheswari +3 more
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The Pairwise-Markov Bernoulli Filter
IEEE Access, 2020 The Bernoulli filter is a general, Bayes-optimal solution for tracking a single disappearing and reappearing target, using a sensor whose observations are corrupted by missed detections and a general, known clutter process.
Ronald Mahler
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Constructions for Clumps Statistics. [PDF]
Discrete Mathematics & Theoretical Computer Science, 2008 We consider a component of the word statistics known as clump; starting from a finite set of words, clumps are maximal overlapping sets of these occurrences. This object has first been studied by Schbath with the aim of counting the number of occurrences
Frédérique Bassino +3 more
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Bayesian Prediction of the Median of Future Observations Based on Finite Mixture Models [PDF]
The Egyptian Statistical Journal, 2004 Bayesian predictive density functions of the median of a set of odd and even number of future order statistics are obtained when the observations (informative and future) are assumed to follow a finite mixture of components of general form and type 1 ...
Essam Al-Hussaini
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