Solving Sensor Identification Problem Without Knowledge of the Ground Truth Using Replicator Dynamics

被引:2
|
作者
Yazidi, Anis [1 ]
Pinto-Orellana, Marco Antonio [2 ]
Hammer, Hugo [1 ]
Mirtaheri, Peyman [2 ]
Herrera-Viedma, Enrique [3 ,4 ]
机构
[1] Oslo Metropolitan Univ, Dept Comp Sci, N-0166 Oslo, Norway
[2] Oslo Metropolitan Univ, Dept Mech Elect & Chem Engn, Oslo, Norway
[3] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada 18070, Spain
[4] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia
关键词
Sociology; Statistics; Reliability theory; Sensor fusion; Convergence; Mathematical model; Distributed learning; scaled replicator dynamics; sensor fusion; JURY THEOREM; FUSION; GAME; ESTIMATORS; SYSTEMS;
D O I
10.1109/TCYB.2019.2958627
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this article, we consider an emergent problem in the sensor fusion area in which unreliable sensors need to be identified in the absence of the ground truth. We devise a novel solution to the problem using the theory of replicator dynamics that require mild conditions compared to the available state-of-the-art approaches. The solution has a low computational complexity that is linear in terms of the number of involved sensors. We provide some sound theoretical results that catalog the convergence of our approach to a solution where we can clearly unveil the sensor type. Furthermore, we present some experimental results that demonstrate the convergence of our approach in concordance with our theoretical findings.
引用
收藏
页码:16 / 24
页数:9
相关论文
共 50 条
  • [1] On Solving the Problem of Identifying Unreliable Sensors Without a Knowledge of the Ground Truth: The Case of Stochastic Environments
    Yazidi, Anis
    Oommen, B. John
    Goodwin, Morten
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2017, 47 (07) : 1604 - 1617
  • [2] Game-Theoretic Learning for Sensor Reliability Evaluation Without Knowledge of the Ground Truth
    Yazidi, Anis
    Hammer, Hugo L.
    Samouylov, Konstantin
    Herrera-Viedma, Enrique
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2021, 51 (12) : 5706 - 5716
  • [3] Data Fusion without Knowledge of the Ground Truth Using Tseltin-like Automata
    Yazidi, Anis
    Sandnes, Frode Eika
    [J]. 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2016, : 4501 - 4506
  • [4] Without a Priori Knowledge Solving the Interior Problem in CT Using Two Scans
    Li, Liang
    Hu, Haifeng
    Chen, Zhiqiang
    Kang, Kejun
    Zhang, Li
    [J]. 2009 IEEE NUCLEAR SCIENCE SYMPOSIUM CONFERENCE RECORD, VOLS 1-5, 2009, : 3280 - +
  • [5] CELLO-EM: Adaptive Sensor Models without Ground Truth
    Vega-Brown, William
    Roy, Nicholas
    [J]. 2013 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2013, : 1907 - 1914
  • [6] Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments
    Yazidi, Anis
    Oommen, B. John
    Goodwin, Morten
    [J]. ADVANCED DATA MINING AND APPLICATIONS, ADMA 2017, 2017, 10604 : 741 - 753
  • [7] On Distinguishing between Reliable and Unreliable Sensors Without a Knowledge of the Ground Truth
    Yazidi, Anis
    Oommen, B. John
    Goodwin, Morten
    [J]. 2015 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT), VOL 2, 2015, : 104 - 111
  • [8] Quality assessment of classification and cluster maps without ground truth knowledge
    Baraldi, A
    Bruzzone, L
    Blonda, P
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2005, 43 (04): : 857 - 873
  • [9] From Appearance to Essence: Comparing Truth Discovery Methods without Using Ground Truth
    Fang, Xiu Susie
    Sheng, Quan Z.
    Wang, Xianzhi
    Zhang, Wei Emma
    Ngu, Anne H. H.
    Yang, Jian
    [J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2020, 11 (06)
  • [10] Solving the split feasibility problem without prior knowledge of matrix norms
    Lopez, Genaro
    Martin-Marquez, Victoria
    Wang, Fenghui
    Xu, Hong-Kun
    [J]. INVERSE PROBLEMS, 2012, 28 (08)