69 Papers
194 Citations
Li Suqi is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Covariance intersection & Radar. The author has an hindex of 11, co-authored 69 publications.
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Papers
Robust Distributed Fusion With Labeled Random Finite Sets
TL;DR: This paper considers the problem of the distributed fusion of multiobject posteriors in the labeled random finite set filtering framework using a generalized covariance intersection (GCI) method and proposes a novel and general solution that is robust to label inconsistencies between sensors.
Distributed Fusion With Multi-Bernoulli Filter Based on Generalized Covariance Intersection
TL;DR: This letter considers the distributed multi-target tracking through the use of multi-Bernoulli based on generalized Covariance Intersection (G-CI) and approximate the fused posterior as an unlabeled version of δ-generalized labelled multi- Bernoulli (δ-GLMB) distribution, referred to as δ.
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Computationally Eff i cient Multi-Agent Multi-Object Tracking With Labeled Random Finite Sets
TL;DR: This paper presents a novel approach for the GCI fusion of LMO densities that is both robust to label inconsistencies and computationally efficient and shows how the label matching problem can be formulated as a linear assignment problem of finite length.
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Distributed fusion with multi-Bernoulli filter based on generalized Covariance Intersection
Bailu Wang,Wei Yi,Li Suqi,Lingjiang Kong,Xiaobo Yang +4 more
- 10 May 2015
TL;DR: This letter considers the distributed multi-target tracking through the use of multi-Bernoulli based on generalized Covariance Intersection (G-CI) and approximate the fused posterior as an unlabeled version of δ-generalized labelled multi- Bernoulli (δ-GLMB) distribution, referred to as δ.
99
Distributed Joint Attack Detection and Secure State Estimation
Nicola Forti,Giorgio Battistelli,Luigi Chisci,Li Suqi,Bailu Wang,Bruno Sinopoli +5 more
- 01 Mar 2018
TL;DR: A novel distributed HBRS filter is developed and its effectiveness is tested on a case study concerning wide-area monitoring of a power network.
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