Multiple resolvable group target estimation using graph theory and the multi-Bernoulli filter

2016 ◽  
Author(s):  
Weifeng Liu ◽  
Shujun Zhu ◽  
Chenglin Wen
Keyword(s):  
Graph Theory ◽  
Bernoulli Filter ◽  
Group Target ◽  
Resolvable Group

Structure modeling and estimation of multiple resolvable group targets via graph theory and multi-Bernoulli filter

Automatica ◽  
2018 ◽  
Vol 89 ◽  
pp. 274-289 ◽  
Author(s):  
Weifeng Liu ◽  
Shujun Zhu ◽  
Chenglin Wen ◽  
Yongsheng Yu
Keyword(s):  
Graph Theory ◽  
Structure Modeling ◽  
Bernoulli Filter ◽  
Resolvable Group

scholarly journals Resolvable Group State Estimation with Maneuver Based on Labeled RFS and Graph Theory

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1307
Author(s):  
Weifeng Liu ◽  
Yudong Chi
Keyword(s):  
Graph Theory ◽  
State Estimation ◽  
Target Tracking ◽  
Finite Sets ◽  
Target Model ◽  
Group Members ◽  
Group Target ◽  
Random Finite Sets ◽  
Resolvable Group

In this paper, multiple resolvable group target tracking was considered in the frame of random finite sets. In particular, a group target model was introduced by combining graph theory with the labeled random finite sets (RFS). This accounted for dependence between group members. Simulations were presented to verify the proposed algorithm.

scholarly journals A Detection and Tracking Algorithm for Resolvable Group with Structural and Formation Changes Using the Gibbs-GLMB Filter

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3384 ◽  
Author(s):  
Xinfeng Ru ◽  
Yudong Chi ◽  
Weifeng Liu
Keyword(s):  
State Estimation ◽  
Target Tracking ◽  
Tracking Error ◽  
Relevant Information ◽  
The State ◽  
Tracking Algorithm ◽  
Detection And Tracking ◽  
Group Target ◽  
Resolvable Group

In the field of resolvable group target tracking, further study on the structure and formation of group targets is helpful to reduce the tracking error of group bluetargets. In this paper, we propose an algorithm to detect whether the structure or formation state of group targets changes. In this paper, a Gibbs Generalized Labeled Multi-Bernoulli (GLMB) filter is used to obtain the estimation of discernible group target bluestates. After obtaining the state estimation of the group target, we extract relevant information based on the estimation data to judge whether the structure or formation state has changed. Finally, several experiments are carried out to verify the algorithm.

scholarly journals Tracking Split Group with δ-Generalized Labeled Multi-Bernoulli Filter

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Linhai Gan ◽  
Gang Wang
Keyword(s):  
Performance Evaluation ◽  
Initial Development ◽  
New Targets ◽  
Target Splitting ◽  
Bernoulli Filter ◽  
Group Target

As target splitting is not considered in the initial development of δ-generalized labeled multi-Bernoulli (δ-GLMB) filter, the scenarios where the new targets appearing conditioned on the preexisting one are not readily addressed by this filter. In view of this, we model the group target as gamma Gaussian inverse Wishart (GGIW) distribution and derive a δ-GLMB filter based on the group splitting model, in which the target splitting event is investigated. Two simplifications of the approach are presented to improve the computing efficiency, where with splitting detection, we need not to predict the splitting events of all the GGIW components in every iteration. With component combination applied in adaptive birth, a redundant modeling for a newborn target or preexisting target could be avoided. Moreover, a method for labeling performance evaluation of the algorithm is provided. Simulations demonstrate the effectiveness of the proposed approach.

Social attribution, correspondence bias, and the emergence of stereotypes *This research has been completed in partial requirement of a doctoral dissertation of the first author under the supervision of the second author. We wish to thank the members of the social psychology division at the University of Louvain at Louvain-la-Neuve, especially Steve Rocher and Emanuele Castano for their insightful comments on preliminary drafts. This paper has been enriched by feedbacks received from Russell Spears, Roos Vonk, Margit Oswald, and anonymous reviewers. Many thanks to Philippe Kemp and Christophe Remy for their assistance in running the study, and to the students who volunteered to appear on the video. This research has been presented at the Social Cognition workshop of the Kurt Lewin Institute, workshop held at the Free University of Amsterdam in April 1998: we want to thank participants of this workshop and especially Gün Semin for their useful feedback. Completion of this study benefited from a research grant from the Belgian National Fund for Scientific Research to the first author.

1999 ◽  
Vol 58 (4) ◽  
pp. 233-240 ◽  
Author(s):  
Anouk Rogier ◽  
Vincent Yzerbyt
Keyword(s):  
Correspondence Bias ◽  
Social Attribution ◽  
The Social ◽  
Dispositional Attributions ◽  
Kurt Lewin ◽  
Group Members ◽  
Group Target ◽  
The University ◽  
Institute Workshop ◽  
Research Grant

Yzerbyt, Rogier and Fiske (1998) argued that perceivers confronted with a group high in entitativity (i.e., a group perceived as an entity, a tight-knit group) more readily call upon an underlying essence to explain people's behavior than perceivers confronted with an aggregate. Their study showed that group entitativity promoted dispositional attributions for the behavior of group members. Moreover, stereotypes emerged when people faced entitative groups. In this study, we replicate and extend these results by providing further evidence that the process of social attribution is responsible for the emergence of stereotypes. We use the attitude attribution paradigm ( Jones & Harris, 1967 ) and show that the correspondence bias is stronger for an entitative group target than for an aggregate. Besides, several dependent measures indicate that the target's group membership stands as a plausible causal factor to account for members' behavior, a process we call Social Attribution. Implications for current theories of stereotyping are discussed.

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