Bernoulli Filter for Extended Target Tracking in the Framework of Possibility Theory

Extended objects give rise to a varying number of noisy measurements from its reflection (scattering or feature) points. Due to imperfect detection, only some of the feature points are detected in each scan of input data, while false alarms can also be present. The optimal sequential Bayesian state...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2023-12, Vol.59 (6), p.1-8
Main Authors: Chen, Zhijin, Ristic, Branko, Kim, Du Yong
Format: Article
Language:English
Subjects:
Dynamic models
Extended object tracking
False alarms
Filtering theory
Mathematical models
Measurement uncertainty
model mismatch
Noise measurement
Possibility theory
Set theory
State estimation
Statistical analysis
Target tracking
Tracking
Uncertainty
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Summary:Extended objects give rise to a varying number of noisy measurements from its reflection (scattering or feature) points. Due to imperfect detection, only some of the feature points are detected in each scan of input data, while false alarms can also be present. The optimal sequential Bayesian state estimator in the framework of random set theory is the Bernoulli filter for an extended target (BF-X). In this paper we formulate and derive the analog of the BF-X in the framework of possibility theory, where uncertainty is represented using possibility functions, rather than probability distributions. Possibility functions have the capacity to model partial (imprecise) probabilistic specifications and thus the main advantage of the proposed possibilistic BF-X, is enhanced robustness in the absence of precise measurements or dynamic models.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2023.3263825