State Estimation for Flag Hidden Markov Models with Imperfect Sensors

被引：0
|
作者：
Doty, Kyle [1 ]
Roy, Sandip [1 ]
Fischer, Thomas R. [1 ]
机构：
[1] Washington State Univ, Pullman, WA 99164 USA
来源：
2016 ANNUAL CONFERENCE ON INFORMATION SCIENCE AND SYSTEMS (CISS) | 2016年
关键词：
Hidden Markov Models; Smart Homes; Maximum Likelihood; PROBABILITY; BOUNDS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
State detection is studied for a special class of flag Hidden Markov Models (HMMs), which comprise 1) an arbitrary finite-state underlying Markov chain and 2) a structured observation process wherein a subset of states emit distinct flags with some probability while other states are unmeasured. The focus of this article is to develop an explicit computation of the probability of error for the maximum-likelihood filter, specifically for the case that the sensors are imperfect. The algebraic result is leveraged to address sensor placement in a couple of examples, including one on activity-monitoring in a home environment that is drawn from field data.
引用
收藏
页数：6
相关论文
共 50 条
  • [41] Efficient recursive distributed state estimation of hidden Markov models over unreliable networks
    Tamjidi, Amirhossein
    Oftadeh, Reza
    Chakravorty, Suman
    Shell, Dylan
    AUTONOMOUS ROBOTS, 2020, 44 (3-4) : 321 - 338
  • [42] Identifiability and Consistent Estimation of Nonparametric Translation Hidden Markov Models with General State Space
    Gassiat, Elisabeth
    Le Corff, Sylvain
    Lehericy, Luc
    JOURNAL OF MACHINE LEARNING RESEARCH, 2020, 21
  • [43] Energy-based event-triggered state estimation for hidden Markov models
    Huang, Jiarao
    Shi, Dawei
    Chen, Tongwen
    AUTOMATICA, 2017, 79 : 256 - 264
  • [44] Identifiability and consistent estimation of nonparametric translation hidden markov models with general state space
    Gassiat, Élisabeth
    Le Corff, Sylvain
    Lehéricy, Luc
    1600, Microtome Publishing (21):
  • [45] Efficient recursive distributed state estimation of hidden Markov models over unreliable networks
    Amirhossein Tamjidi
    Reza Oftadeh
    Suman Chakravorty
    Dylan Shell
    Autonomous Robots, 2020, 44 : 321 - 338
  • [46] An algorithm for maximum likelihood estimation of hidden Markov models with unknown state-tying
    Cappe, O
    Mokbel, CE
    Jouvet, D
    Moulines, E
    IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, 1998, 6 (01): : 61 - 70
  • [47] PENALIZED ESTIMATION IN HIGH-DIMENSIONAL HIDDEN MARKOV MODELS WITH STATE-SPECIFIC GRAPHICAL MODELS
    Stadler, Nicolas
    Mukherjee, Sach
    ANNALS OF APPLIED STATISTICS, 2013, 7 (04): : 2157 - 2179
  • [48] Markov models - hidden Markov models
    Grewal, Jasleen K.
    Krzywinski, Martin
    Altman, Naomi
    NATURE METHODS, 2019, 16 (09) : 795 - 796
  • [49] Markov models — hidden Markov models
    Jasleen K. Grewal
    Martin Krzywinski
    Naomi Altman
    Nature Methods, 2019, 16 : 795 - 796
  • [50] Maximum a Posteriori Estimation of Coupled Hidden Markov Models
    Iead Rezek
    Michael Gibbs
    Stephen J. Roberts
    Journal of VLSI signal processing systems for signal, image and video technology, 2002, 32 : 55 - 66
12345