Multi-Target PHD Tracking and Classification Using Imprecise Likelihoods
| Autor: | Samir Hachour, François Delmotte, B. Fortin |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
020301 aerospace & aeronautics
business.industry Applied Mathematics Bayesian probability 020206 networking & telecommunications Pattern recognition 02 engineering and technology Kinematics Multiple target Machine learning computer.software_genre Theoretical Computer Science Probability hypothesis density filter Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION Multi target 0203 mechanical engineering Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Classifier (UML) computer Software Mathematics |
| Zdroj: | International Journal of Approximate Reasoning. 90:17-36 |
| ISSN: | 0888-613X |
| DOI: | 10.1016/j.ijar.2017.06.013 |
| Popis: | This article proposes a method to track and classify multiple target based on kinematics data. On one hand, tracking is performed using a Probability Hypothesis Density (PHD) filter avoiding the association stage, necessary for many tracking algorithms. On the other hand, Belief Functions and imprecise probabilities are used for the classification task, reducing errors from standard Bayesian classifiers when data are ambiguous. The proposed method is evaluated on several scenarios of multiple aircraft tracking. It is shown in particular that when the number of targets is varying, the proposed approach leads to a reduced number of false created target and improves the classification task over a standard Bayesian classifier where both belief function based classifier and imprecise probabilities classifier give the same result. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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