Academic Editor
Author
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Author 1
Editor 2
Jun Liu
School of Automation, Hangzhou Dianzi University, Zhejiang 310018, China
Summary
Edited Journals
ICCK Contributions

Open Access | Research Article | 25 June 2025
A Track Splitting Determination Method for Elliptical Extended Targets Based on Spatio Temporal Similarity
Chinese Journal of Information Fusion | Volume 2, Issue 2: 171-181, 2025 | DOI: 10.62762/CJIF.2025.519610
Abstract
Extended target tracking in occlusion scenarios often suffers from split errors due to sensor limitations and complex target interactions, leading to degraded tracking performance for autonomous vehicles and surveillance systems. To address this issue, in this paper, we propose a Gaussian Wasserstein distance-enhanced spatio-temporal similarity method for split error correction. We first analyze the spatio-temporal characteristics of split extended targets and model their geometric uncertainties via elliptical Gaussian distributions. Then, we integrate the Gaussian Wasserstein distance into the clue-aware trajectory similarity calculation framework to simultaneously capture positional and sh... More >

Graphical Abstract
A Track Splitting Determination Method for Elliptical Extended Targets Based on Spatio Temporal Similarity

Open Access | Research Article | 07 December 2024
Basic Belief Assignment Determination Based on Radial Basis Function Network
Chinese Journal of Information Fusion | Volume 1, Issue 3: 175-182, 2024 | DOI: 10.62762/CJIF.2024.841250
Abstract
In Dempster-Shafer evidence theory (DST), the determination of basic belief assignment (BBA) is an important yet challenging issue before the evidence fusion. The rational mass determination of compound focal elements is crucial for fully taking advantage of DST, i.e., the ability to represent the ambiguity. In this paper, for the compound focal elements, we select and construct the compound-class samples with ambiguous class membership. Then, we use these samples to construct an end-to-end model called Evidential Radial Basis Function Network (E-RBFN), with the input as the sample and the output as the corresponding BBA. The E-RBFN can directly determine the mass values for all focal elemen... More >

Graphical Abstract
Basic Belief Assignment Determination Based on Radial Basis Function Network